chore: lint

* feat: LoRA kernel support for bias, dropout, dora, embeddings

* chore: lint

* chore: lint

* address PR feedback, add regression tests, add fsdp2 tests for lora kernels

* update tests for new sigs

* update tests now that bias and dropout are supported

_quarto.yml

@@ -128,11 +128,9 @@ quartodoc:
         - monkeypatch.mistral_attn_hijack_flash
         - monkeypatch.multipack
         - monkeypatch.relora
-        - monkeypatch.llama_expand_mask
         - monkeypatch.lora_kernels
         - monkeypatch.utils
         - monkeypatch.btlm_attn_hijack_flash
-        - monkeypatch.llama_patch_multipack
         - monkeypatch.stablelm_attn_hijack_flash
         - monkeypatch.trainer_fsdp_optim
         - monkeypatch.transformers_fa_utils

cicd/cicd.sh

@@ -3,7 +3,8 @@ set -e
 
 python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
 
-curl -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst | tar -xpf - -C "${HF_HOME}/hub/"  --use-compress-program unzstd --strip-components=1
+set -o pipefail
+curl --silent --show-error --fail --retry 3 --retry-delay 5 -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst | tar -xpf - -C "${HF_HOME}/hub/" --use-compress-program unzstd --strip-components=1
 # hf download "NousResearch/Meta-Llama-3-8B"
 # hf download "NousResearch/Meta-Llama-3-8B-Instruct"
 # hf download "microsoft/Phi-4-reasoning"

codecov.yml

@@ -37,6 +37,7 @@ coverage:
         only_pulls: false
         flags: null
         paths: null
+        informational: true
 
 parsers:
   gcov:

docs/gradient_checkpointing.qmd

@@ -1,5 +1,5 @@
 ---
-title: Gradient Checkpointing and Activation Offloading
+title: Gradient Checkpointing, Activation Offloading, and Layer Offloading
 ---
 
 Gradient checkpointing and activation offloading are techniques used to optimize the performance of deep learning
@@ -27,3 +27,33 @@ The `activation_offloading: legacy` naively offloads activations to CPU and with
 
 For resource constrained environments with limited CPU memory, `activation_offloading: disk` offloads
 activations to disk instead of CPU RAM so that much larger context lengths can be trained with minimal memory.
+
+### Enabling Layer Offloading
+
+```yaml
+layer_offloading: true
+```
+
+Layer offloading reduces GPU memory usage by moving frozen (non-trainable) decoder layer parameters to CPU
+and streaming them back to GPU one layer at a time during the forward and backward passes. This is
+particularly useful for LoRA/QLoRA training where most of the model's parameters are frozen — only the
+trainable adapter weights stay on GPU permanently.
+
+During training, forward and backward hooks on each decoder layer handle the transfer automatically:
+
+- **Forward pass:** Before a layer executes, its frozen params are loaded to GPU. The next layer is
+  prefetched asynchronously on a separate CUDA stream for overlap.
+- **Backward pass:** Same pattern in reverse — the current layer's frozen params are loaded and the
+  previous layer is prefetched.
+
+After each layer finishes, its frozen params are offloaded back to CPU pinned memory.
+
+This approach trades some CPU-GPU transfer overhead for significant GPU memory savings — the freed memory
+is roughly equal to the size of all frozen parameters across all decoder layers, minus one layer's worth
+that is kept on GPU at any given time.
+
+**Requirements:**
+
+- CUDA GPU (CPU-only training is not supported for this feature)
+- Works with any HuggingFace model architecture that uses decoder layers (Llama, Mistral, Qwen, etc.)
+- Best combined with LoRA/QLoRA where most parameters are frozen

docs/optimizations.qmd

@@ -54,6 +54,13 @@ These techniques save VRAM by changing how activations are handled.
 - Activation Offloading: moves activations to CPU RAM or disk, trading I/O overhead for VRAM.
 - Learn more: [Gradient Checkpointing and Offloading Docs](gradient_checkpointing.qmd)
 
+### Layer Offloading
+
+Offloads frozen (non-trainable) decoder layer parameters to CPU and streams them back to GPU one layer at a time during forward/backward passes using CUDA stream prefetching. Especially effective for LoRA/QLoRA where most parameters are frozen.
+
+- **Config:** `layer_offloading: true`
+- **Learn more:** [Layer Offloading Docs](gradient_checkpointing.qmd#enabling-layer-offloading)
+
 ### Cut Cross Entropy (CCE)
 
 Reduces VRAM usage by using an optimized cross-entropy loss calculation.

examples/mistral4/README.md

@@ -6,9 +6,6 @@ Thanks to the team at MistralAI for giving us early access to prepare for this r
 
 ## Getting started
 
-Note: Training this model requires weights in BF16 which we will link to later.
-Users interested in training can convert / descale the existing FP8 weights.
-
 1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
 
 2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage

examples/mistral4/fft-text.yml

@@ -1,4 +1,4 @@
-base_model: mistralai/Mistral-Small-4-119B-2603
+base_model: axolotl-ai-co/Mistral-Small-4-119B-2603-BF16
 
 plugins:
   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

examples/mistral4/fft-vision.yml

@@ -1,4 +1,4 @@
-base_model: mistralai/Mistral-Small-4-119B-2603
+base_model: axolotl-ai-co/Mistral-Small-4-119B-2603-BF16
 processor_type: AutoProcessor
 
 plugins:

examples/mistral4/qlora-text.yml

@@ -1,4 +1,4 @@
-base_model: mistralai/Mistral-Small-4-119B-2603
+base_model: axolotl-ai-co/Mistral-Small-4-119B-2603-BF16
 
 plugins:
   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

examples/mistral4/qlora-vision.yml

@@ -1,4 +1,4 @@
-base_model: mistralai/Mistral-Small-4-119B-2603
+base_model: axolotl-ai-co/Mistral-Small-4-119B-2603-BF16
 processor_type: AutoProcessor
 
 plugins:

examples/qwen3.5/122b-a10b-moe-qlora-fsdp.yaml

@@ -0,0 +1,84 @@
+base_model: Qwen/Qwen3.5-122B-A10B
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+strict: false
+
+chat_template: qwen3_5
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+val_set_size: 0.0
+output_dir: ./outputs/out
+dataset_prepared_path: last_run_prepared
+
+sequence_len: 2048
+sample_packing: true
+
+load_in_4bit: true
+quantize_moe_experts: true
+adapter: qlora
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0
+lora_target_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+# Regex matching to target shared experts too
+# lora_target_modules: 'model\.(language_model\.)?layers\.[\d]+\.(mlp|self_attn)\.(shared_expert\.)?(up|down|gate|gate_up|q|k|v|o)_proj'
+
+# Target experts
+# lora_target_parameters:
+#   - mlp.experts.gate_up_proj
+#   - mlp.experts.down_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_4bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+lora_mlp_kernel: false
+lora_qkv_kernel: false
+lora_o_kernel: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 4
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:
+
+fsdp_config:
+  fsdp_version: 2
+  offload_params: true
+  cpu_ram_efficient_loading: false
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: Qwen3_5MoeDecoderLayer
+  state_dict_type: FULL_STATE_DICT
+  sharding_strategy: FULL_SHARD
+  reshard_after_forward: true
+  activation_checkpointing: true

examples/qwen3.5/122b-a10b-moe-qlora.yaml

@@ -32,7 +32,11 @@ lora_target_modules:
   - v_proj
   - o_proj
 
-#lora_target_parameters:
+# Regex matching to target shared experts too
+# lora_target_modules: 'model\.(language_model\.)?layers\.[\d]+\.(mlp|self_attn)\.(shared_expert\.)?(up|down|gate|gate_up|q|k|v|o)_proj'
+
+# Target experts
+# lora_target_parameters:
 #   - mlp.experts.gate_up_proj
 #   - mlp.experts.down_proj
 
@@ -52,7 +56,6 @@ learning_rate: 0.0002
 bf16: auto
 tf32: true
 
-
 lora_mlp_kernel: false
 lora_qkv_kernel: false
 lora_o_kernel: false

examples/qwen3.5/27b-qlora-fsdp.yaml

@@ -0,0 +1,81 @@
+base_model: Qwen/Qwen3.5-27B
+
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+strict: false
+
+chat_template: qwen3_5
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+val_set_size: 0.0
+output_dir: ./outputs/out
+dataset_prepared_path: last_run_prepared
+
+sequence_len: 2048
+sample_packing: true
+
+load_in_4bit: true
+adapter: qlora
+lora_r: 16
+lora_alpha: 32
+lora_target_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+  - down_proj
+  - up_proj
+  # Uncomment below to also target the linear attention projections.
+  # These use separate in_proj_qkv / in_proj_z / out_proj (Qwen3.5-specific).
+  # - linear_attn.in_proj_qkv
+  # - linear_attn.in_proj_z
+  # - linear_attn.out_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_4bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 4
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:
+
+fsdp_config:
+  fsdp_version: 2
+  offload_params: false
+  cpu_ram_efficient_loading: false
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: Qwen3_5DecoderLayer
+  state_dict_type: FULL_STATE_DICT
+  sharding_strategy: FULL_SHARD
+  reshard_after_forward: true
+  activation_checkpointing: true

examples/qwen3.5/27b-qlora.yaml

@@ -1,9 +1,7 @@
 base_model: Qwen/Qwen3.5-27B
+
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-# Note: Qwen3.5 is an early-fusion VLM (image+text). This config fine-tunes
-# the text-only path. For multimodal (image+text) fine-tuning, add image
-# columns to your dataset following axolotl's multimodal dataset format.
 
 plugins:
   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

examples/qwen3.5/35b-a3b-moe-qlora-fsdp.yaml

@@ -0,0 +1,85 @@
+base_model: Qwen/Qwen3.5-35B-A3B
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+strict: false
+
+chat_template: qwen3_5
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+val_set_size: 0.0
+output_dir: ./outputs/out
+dataset_prepared_path: last_run_prepared
+
+sequence_len: 2048
+sample_packing: true
+
+load_in_4bit: true
+quantize_moe_experts: true
+adapter: qlora
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0
+lora_target_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+
+# Regex matching to target shared experts too
+# lora_target_modules: 'model\.(language_model\.)?layers\.[\d]+\.(mlp|self_attn)\.(shared_expert\.)?(up|down|gate|gate_up|q|k|v|o)_proj'
+
+# Target experts
+# lora_target_parameters:
+#   - mlp.experts.gate_up_proj
+#   - mlp.experts.down_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_4bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+lora_mlp_kernel: false
+lora_qkv_kernel: false
+lora_o_kernel: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 4
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:
+
+fsdp_config:
+  fsdp_version: 2
+  offload_params: true
+  cpu_ram_efficient_loading: false
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: Qwen3_5MoeDecoderLayer
+  state_dict_type: FULL_STATE_DICT
+  sharding_strategy: FULL_SHARD
+  reshard_after_forward: true
+  activation_checkpointing: true

examples/qwen3.5/35b-a3b-moe-qlora.yaml

@@ -32,7 +32,11 @@ lora_target_modules:
   - v_proj
   - o_proj
 
-#lora_target_parameters:
+# Regex matching to target shared experts too
+# lora_target_modules: 'model\.(language_model\.)?layers\.[\d]+\.(mlp|self_attn)\.(shared_expert\.)?(up|down|gate|gate_up|q|k|v|o)_proj'
+
+# Target experts
+# lora_target_parameters:
 #   - mlp.experts.gate_up_proj
 #   - mlp.experts.down_proj
 

examples/qwen3.5/9b-lora-vision.yaml

@@ -26,8 +26,6 @@ lora_r: 32
 lora_alpha: 16
 lora_dropout: 0.05
 # Targets the language model attention and MLP layers.
-# Qwen3.5 is early-fusion: all layers (including those seeing vision tokens) share
-# the same transformer stack, so standard attention targets work for both modalities.
 lora_target_modules:
   - q_proj
   - k_proj

examples/qwen3.5/README.md

@@ -2,20 +2,6 @@
 
 [Qwen3.5](https://huggingface.co/collections/Qwen/qwen35) is a hybrid architecture model series combining Gated DeltaNet linear attention with standard Transformer attention. All Qwen3.5 models are early-fusion vision-language models: dense variants use `Qwen3_5ForConditionalGeneration` and MoE variants use `Qwen3_5MoeForConditionalGeneration`.
 
-Vision and text tokens are processed through the same transformer stack. The configs below train on text-only data unless noted otherwise. See `9b-lora-vision.yaml` for a multimodal example.
-
-Available configs:
-
-| Config | Model | Type | Peak VRAM |
-|---|---|---|---|
-| `27b-qlora.yaml` | Qwen3.5-27B | Dense VLM, text-only QLoRA | ~47 GiB |
-| `27b-fft.yaml` | Qwen3.5-27B | Dense VLM, text-only FFT (vision frozen) | ~53 GiB |
-| `35b-a3b-moe-qlora.yaml` | Qwen3.5-35B-A3B | MoE, text-only QLoRA | — |
-| `122b-a10b-moe-qlora.yaml` | Qwen3.5-122B-A10B | MoE, text-only QLoRA | — |
-| `9b-lora-vision.yaml` | Qwen3.5-9B | Vision+text LoRA, single GPU | — |
-| `9b-fft-vision.yaml` | Qwen3.5-9B | Vision+text FFT, single GPU | ~61 GiB |
-
-
 ## Getting started
 
 1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
@@ -23,43 +9,69 @@ Available configs:
 2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
 
 3. Install FLA for sample packing support with the Gated DeltaNet linear attention layers:
-```bash
-pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.4.1
+  ```bash
+  pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.4.1
+  ```
+  > FLA is required when `sample_packing: true`. Without it, training raises a `RuntimeError` on packed sequences. Vision configs use `sample_packing: false` so FLA is optional there.
+
+4. Pick any config from the table below and run:
+
+    ```bash
+    axolotl train examples/qwen3.5/<config>.yaml
+    ```
+
+Available configs:
+
+| Config | Model | Type | Peak VRAM |
+|---|---|---|---|
+| `9b-lora-vision.yaml` | Qwen3.5-9B | Vision+text LoRA, single GPU | — |
+| `9b-fft-vision.yaml` | Qwen3.5-9B | Vision+text FFT, single GPU | ~61 GiB |
+| `27b-qlora.yaml` | Qwen3.5-27B | Dense, text-only QLoRA | ~47 GiB |
+| `27b-fft.yaml` | Qwen3.5-27B | Dense, text-only FFT (vision frozen) | ~53 GiB |
+| `27b-qlora-fsdp.yaml` | Qwen3.5-27B | Dense, text-only QLoRA + FSDP2 | — |
+| `35b-a3b-moe-qlora.yaml` | Qwen3.5-35B-A3B | MoE, text-only QLoRA | — |
+| `35b-a3b-moe-qlora-fsdp.yaml` | Qwen3.5-35B-A3B | MoE, text-only QLoRA + FSDP2 | — |
+| `122b-a10b-moe-qlora.yaml` | Qwen3.5-122B-A10B | MoE, text-only QLoRA | — |
+| `122b-a10b-moe-qlora-fsdp.yaml` | Qwen3.5-122B-A10B | MoE, text-only QLoRA + FSDP2 | — |
+
+### Gated DeltaNet Linear Attention
+
+Qwen3.5 interleaves standard attention with Gated DeltaNet linear attention layers. To apply LoRA to them, add to `lora_target_modules`:
+
+```yaml
+lora_target_modules:
+  # ... standard projections ...
+  - linear_attn.in_proj_qkv
+  - linear_attn.in_proj_z
+  - linear_attn.out_proj
 ```
-> FLA is required when `sample_packing: true`. Without it, training raises a `RuntimeError` on packed sequences. Vision configs use `sample_packing: false` so FLA is optional there.
 
-4. Run a finetuning example:
+### Routed Experts (MoE)
 
-```bash
-# Dense 27B text-only (QLoRA, ~47 GiB VRAM with sample packing)
-axolotl train examples/qwen3.5/27b-qlora.yaml
+To apply LoRA to routed expert parameters, add `lora_target_parameters`:
 
-# Dense 27B text-only FFT with vision encoder frozen (~53 GiB, single 80 GiB GPU)
-axolotl train examples/qwen3.5/27b-fft.yaml
+```yaml
+lora_target_parameters:
+  - mlp.experts.gate_up_proj
+  - mlp.experts.down_proj
+#  - mlp.gate.weight  # router
+```
 
-# MoE 35B-A3B text-only (QLoRA)
-axolotl train examples/qwen3.5/35b-a3b-moe-qlora.yaml
+### Shared Experts (MoE)
 
-# MoE 122B-A10B text-only (QLoRA)
-axolotl train examples/qwen3.5/122b-a10b-moe-qlora.yaml
-
-# 9B vision+text (LoRA, multimodal dataset)
-axolotl train examples/qwen3.5/9b-lora-vision.yaml
-
-# 9B vision+text FFT, single 80 GiB GPU (~61 GiB peak)
-axolotl train examples/qwen3.5/9b-fft-vision.yaml
+Routed experts and shared experts both have `gate_up_proj`/`down_proj`, so a plain module name in `lora_target_modules` would match both. Use a regex to target only attention and shared expert projections, while `lora_target_parameters` above handles routed experts separately:
 
+```yaml
+lora_target_modules: 'model\.(language_model\.)?layers\.[\d]+\.(mlp|self_attn)\.(shared_expert\.)?(up|down|gate|gate_up|q|k|v|o)_proj'
 ```
 
 ### TIPS
 
-- For inference, you can experiment with `temperature: 0.7`, `top_p: 0.8`, `top_k: 20`, and `min_p: 0`.
-- For **text-only FFT** on 27B, use `27b-fft.yaml` which sets `unfrozen_parameters` to freeze the vision encoder (`model.visual.*`) — this avoids wasting optimizer state on parameters that receive no gradient from text-only data.
+- For inference hyp, please see the respective model card details.
 - You can run a full finetuning of smaller configs by removing `adapter: qlora` and `load_in_4bit: true`. See [Multi-GPU](#optimization-guides) below.
 - Read more on loading your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
 - The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
 - For **multimodal** finetuning, set `processor_type: AutoProcessor`, `skip_prepare_dataset: true`, and `remove_unused_columns: false` as shown in `9b-lora-vision.yaml`.
-- The Gated DeltaNet linear attention layers (`linear_attn.*`) can optionally be added to `lora_target_modules` — they are commented out by default.
 
 ## Optimization Guides
 

pyproject.toml

@@ -61,5 +61,11 @@ skip-magic-trailing-comma = false
 line-ending = "auto"
 docstring-code-format = false
 
+[tool.pytest.ini_options]
+addopts = "-m 'not slow'"
+markers = [
+    "slow: marks tests as slow",
+]
+
 [tool.uv.extra-build-dependencies]
 axolotl = ["huggingface_hub"]

setup.py

@@ -81,16 +81,23 @@ def parse_requirements(extras_require_map):
                     f"https://download.pytorch.org/whl/{torch_cuda_version}"
                 )
 
-            if (major, minor) >= (2, 9):
+            if (major, minor) >= (2, 10):
+                extras_require_map.pop("fbgemm-gpu")
+                extras_require_map["fbgemm-gpu"] = [
+                    "fbgemm-gpu==1.5.0",
+                    "fbgemm-gpu-genai==1.5.0",
+                ]
+                if not install_xformers:
+                    _install_requires.pop(_install_requires.index(xformers_version))
+                extras_require_map["vllm"] = ["vllm==0.17.1"]
+            elif (major, minor) >= (2, 9):
                 extras_require_map.pop("fbgemm-gpu")
                 extras_require_map["fbgemm-gpu"] = [
                     "fbgemm-gpu==1.4.0",
                     "fbgemm-gpu-genai==1.4.2",
                 ]
-                extras_require_map["vllm"] = ["vllm==0.11.1"]
                 if not install_xformers:
                     _install_requires.pop(_install_requires.index(xformers_version))
-                extras_require_map["vllm"] = ["vllm==0.13.0"]
                 if patch == 0:
                     extras_require_map["vllm"] = ["vllm==0.13.0"]
                 else:

src/axolotl/cli/checks.py

@@ -3,6 +3,7 @@
 import os
 from pathlib import Path
 
+import httpcore
 from accelerate.commands.config import config_args
 from huggingface_hub import HfApi
 from huggingface_hub.utils import LocalTokenNotFoundError
@@ -47,7 +48,7 @@ def check_user_token() -> bool:
             "Error verifying HuggingFace token. Remember to log in using `hf auth login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets."
         )
         return False
-    except HTTPError:
+    except (HTTPError, httpcore.ConnectError):
         LOG.warning(
             "Error accessing HuggingFace. This may be due to a network issue or rate limiting."
         )

src/axolotl/cli/main.py

@@ -91,6 +91,7 @@ def preprocess(config: str, cloud: Optional[str] = None, **kwargs):
     type=click.Path(exists=True, path_type=str),
     help="YAML config for sweeping hyperparameters",
 )
+@click.option("--tui", is_flag=True, default=False, help="Enable TUI dashboard")
 @add_options_from_dataclass(TrainerCliArgs)
 @add_options_from_config(AxolotlInputConfig)
 @filter_none_kwargs
@@ -101,6 +102,7 @@ def train(
     launcher: Literal["accelerate", "torchrun", "python"] = "accelerate",
     cloud: str | None = None,
     sweep: str | None = None,
+    tui: bool = False,
     **kwargs,
 ):
     """
@@ -118,6 +120,10 @@ def train(
     # Extract launcher args from extra args (after --)
     launcher_args = ctx.args if ctx.args else []
 
+    # Handle --tui flag: set env var so subprocess workers pick it up
+    if tui:
+        os.environ["AXOLOTL_TUI"] = "1"
+
     # Handle Ray launcher override
     _launcher = None if kwargs.get("use_ray") else launcher
 

src/axolotl/cli/train.py

@@ -2,6 +2,7 @@
 
 import gc
 import os
+import queue
 from pathlib import Path
 from typing import Union
 
@@ -34,22 +35,101 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
     if int(os.getenv("LOCAL_RANK", "0")) == 0:
         check_user_token()
 
-    plugin_manager = PluginManager.get_instance()
-    dataset_meta = plugin_manager.load_datasets(cfg, preprocess=False)
-    if not dataset_meta:
-        if cfg.rl:
-            dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
-        else:
-            dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+    # Start TUI early (before data loading) so it captures preprocessing events
+    tui_renderer = None
+    tui_queue: queue.Queue | None = None
+    is_rank_0 = int(os.getenv("LOCAL_RANK", "0")) == 0
+    if is_rank_0:
+        from axolotl.train import _is_tui_enabled
 
-    model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
+        if _is_tui_enabled(cfg):
+            import queue as _queue
 
-    del model, tokenizer, trainer
+            from axolotl.train import _get_tui_config
+            from axolotl.tui.config import TUIConfig
+            from axolotl.tui.renderer import TUIRenderer
 
-    gc.collect()
+            tui_config_dict = _get_tui_config(cfg)
+            tui_config = (
+                TUIConfig(**tui_config_dict)
+                if isinstance(tui_config_dict, dict)
+                else tui_config_dict
+            )
+            tui_queue = _queue.Queue(maxsize=4096)
+            tui_renderer = TUIRenderer(config=tui_config, metric_queue=tui_queue)
 
-    plugin_manager = PluginManager.get_instance()
-    plugin_manager.post_train_unload(cfg)
+            # Send initial run info
+            model_name = cfg.base_model or ""
+            training_mode = str(cfg.rl) if cfg.rl else "sft"
+            world_size = int(os.environ.get("WORLD_SIZE", 1))
+            try:
+                tui_queue.put_nowait(
+                    {
+                        "type": "run_info",
+                        "model_name": model_name,
+                        "training_mode": training_mode,
+                        "world_size": world_size,
+                    }
+                )
+            except _queue.Full:
+                pass
+
+            tui_renderer.start()
+
+            # Attach logging handler early
+            import logging
+
+            from axolotl.tui.callback import _TUILogHandler
+
+            _early_log_handler = _TUILogHandler(
+                tui_queue, min_level=tui_config.log_level
+            )
+            _early_log_handler.setFormatter(logging.Formatter("[%(name)s] %(message)s"))
+            # Attach to BOTH root and axolotl loggers because axolotl logger
+            # has propagate=False so root handler never sees axolotl.* messages
+            root_logger = logging.getLogger()
+            root_logger.addHandler(_early_log_handler)
+            axolotl_logger = logging.getLogger("axolotl")
+            axolotl_logger.addHandler(_early_log_handler)
+
+            # Stash refs on cfg so train() can reuse the renderer
+            cfg._tui_renderer = tui_renderer
+            cfg._tui_queue = tui_queue
+            cfg._tui_early_log_handler = _early_log_handler
+
+    try:
+        plugin_manager = PluginManager.get_instance()
+        dataset_meta = plugin_manager.load_datasets(cfg, preprocess=False)
+        if not dataset_meta:
+            if cfg.rl:
+                dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
+            else:
+                dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
+
+        del model, tokenizer, trainer
+
+        gc.collect()
+
+        plugin_manager = PluginManager.get_instance()
+        plugin_manager.post_train_unload(cfg)
+    finally:
+        # If the TUI renderer started early but train() didn't get to stop it
+        # (e.g., error during data loading), clean up here
+        if tui_renderer is not None and not tui_renderer._stop_event.is_set():
+            try:
+                if tui_queue is not None:
+                    tui_queue.put_nowait({"type": "done"})
+            except queue.Full:
+                pass
+            tui_renderer.stop()
+        # Remove early log handler from both root and axolotl loggers
+        if hasattr(cfg, "_tui_early_log_handler"):
+            import logging
+
+            logging.getLogger().removeHandler(cfg._tui_early_log_handler)
+            logging.getLogger("axolotl").removeHandler(cfg._tui_early_log_handler)
 
 
 def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):

src/axolotl/core/builders/base.py

@@ -353,6 +353,30 @@ class TrainerBuilderBase(abc.ABC):
                 adam_kwargs["eps"] = (eps1, eps2)
 
                 optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "flash_adamw":
+                from flashoptim import FlashAdamW
+
+                optimizer_cls = FlashAdamW
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "flash_adam":
+                from flashoptim import FlashAdam
+
+                optimizer_cls = FlashAdam
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "flash_sgd":
+                from flashoptim import FlashSGD
+
+                optimizer_cls = FlashSGD
+            elif self.cfg.optimizer == "flash_sgdw":
+                from flashoptim import FlashSGDW
+
+                optimizer_cls = FlashSGDW
+            elif self.cfg.optimizer == "flash_lion":
+                from flashoptim import FlashLion
+
+                optimizer_cls = FlashLion
+                if "betas" in adam_kwargs:
+                    optimizer_kwargs["betas"] = adam_kwargs["betas"]
             else:
                 raise ValueError(
                     f"Unhandled optimizer: {self.cfg.optimizer}. Please raise an Issue."
@@ -484,6 +508,8 @@ class TrainerBuilderBase(abc.ABC):
             training_args_kwargs["accelerator_config"] = AcceleratorConfig()
 
     def _configure_gradient_checkpointing(self, training_args_kwargs: dict):
+        if self.cfg.layer_offloading:
+            training_args_kwargs["layer_offloading"] = True
         if self.cfg.activation_offloading is True:
             # don't use the HF gradient checkpointing, manually wrap
             training_args_kwargs["gradient_checkpointing"] = False

src/axolotl/core/builders/rl.py

@@ -208,7 +208,11 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
 
         if self.eval_dataset:
             trainer_kwargs["eval_dataset"] = self.eval_dataset
-        if self.cfg.adapter and self.peft_config and self.cfg.rl is not RLType.GRPO:
+        if (
+            self.cfg.adapter
+            and self.peft_config
+            and self.cfg.rl not in (RLType.GRPO, RLType.ORPO)
+        ):
             trainer_kwargs["peft_config"] = self.peft_config
         if self.cfg.precompute_ref_log_probs is not None:
             trainer_kwargs["precompute_ref_log_probs"] = (

src/axolotl/core/trainers/base.py

@@ -29,10 +29,12 @@ from transformers.utils import SAFE_WEIGHTS_NAME, is_peft_available
 from trl.experimental.utils import pad_to_length
 from typing_extensions import override
 
+from axolotl.core.trainers.constants import TOKENS_STATE_FILE
 from axolotl.core.trainers.mixins import (
     ActivationOffloadingMixin,
     CheckpointSaveMixin,
     DistributedParallelMixin,
+    LayerOffloadingMixin,
     OptimizerMixin,
     PackingMixin,
     RngLoaderMixin,
@@ -51,8 +53,6 @@ from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
 LOG = get_logger(__name__)
 
-TOKENS_STATE_FILE = "tokens_state."
-
 REDUCTION_FNS = {
     "mean": torch.mean,
     "min": torch.min,
@@ -67,6 +67,7 @@ class AxolotlTrainer(
     OptimizerMixin,
     RngLoaderMixin,
     CheckpointSaveMixin,
+    LayerOffloadingMixin,
     ActivationOffloadingMixin,
     DistributedParallelMixin,
     Trainer,

src/axolotl/core/trainers/constants.py

@@ -0,0 +1 @@
+TOKENS_STATE_FILE = "tokens_state.json"

src/axolotl/core/trainers/dpo/args.py

@@ -2,7 +2,8 @@
 Axolotl specific DPO args
 """
 
-from dataclasses import dataclass
+from dataclasses import dataclass, field
+from typing import Optional
 
 from trl import DPOConfig
 
@@ -16,3 +17,4 @@ class AxolotlDPOConfig(AxolotlTrainingMixins, DPOConfig):
     """
 
     dpo_norm_loss: bool | None = False
+    rpo_alpha: Optional[float] = field(default=None)

src/axolotl/core/trainers/mixins/__init__.py

@@ -4,6 +4,7 @@
 
 from .activation_checkpointing import ActivationOffloadingMixin
 from .checkpoints import CheckpointSaveMixin
+from .layer_offloading import LayerOffloadingMixin
 from .distributed_parallel import DistributedParallelMixin
 from .optimizer import OptimizerMixin
 from .packing import PackingMixin

src/axolotl/core/trainers/mixins/layer_offloading.py

@@ -0,0 +1,304 @@
+"""
+Trainer mixin for layer-wise parameter offloading to CPU.
+
+Offloads frozen (non-trainable) parameters in decoder layers to CPU, then uses
+forward/backward hooks to stream them on/off GPU one layer at a time with CUDA
+stream prefetching. Trainable parameters (e.g. LoRA weights) stay on GPU always.
+
+Forward:  pre-hook loads layer N's frozen params to GPU (prefetches N+1 on
+          transfer stream), post-hook offloads layer N-1's frozen params.
+Backward: same in reverse order.
+"""
+
+import contextlib
+
+import torch
+import torch.nn as nn
+from transformers import Trainer
+
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
+
+def _find_decoder_layers(model: nn.Module) -> tuple[nn.ModuleList | None, list[str]]:
+    """Recursively search the model for the decoder layer ModuleList.
+
+    Finds any ModuleList whose children have 'DecoderLayer' in their class name.
+    Handles all common HF architectures including VLM wrappers (e.g. Qwen3.5-MoE
+    where layers are at model.language_model.layers).
+    """
+    # BFS to find the first ModuleList containing decoder layers
+    queue = [model]
+    while queue:
+        m = queue.pop(0)
+        for _name, child in m.named_children():
+            if isinstance(child, nn.ModuleList) and len(child) > 0:
+                first_type = type(child[0]).__name__
+                if "DecoderLayer" in first_type or "TransformerBlock" in first_type:
+                    layer_types = list({type(layer).__name__ for layer in child})
+                    return child, layer_types
+            else:
+                queue.append(child)
+
+    return None, []
+
+
+def _get_frozen_params(layer: nn.Module) -> list[tuple[str, nn.Parameter]]:
+    """Get all non-trainable parameters in a layer."""
+    return [(n, p) for n, p in layer.named_parameters() if not p.requires_grad]
+
+
+class LayerOffloadManager:
+    """Manages offloading frozen decoder layer params to CPU and streaming
+    them back during forward/backward with CUDA stream overlap.
+
+    Only frozen (requires_grad=False) parameters are offloaded.
+    Trainable parameters (LoRA weights, etc.) remain on GPU at all times.
+    """
+
+    def __init__(
+        self,
+        model: nn.Module,
+        num_prefetch: int = 1,
+    ):
+        self.model = model
+        self.num_prefetch = num_prefetch
+        self._hooks: list = []
+        self._device = None
+
+        # Find decoder layers
+        self.layers, layer_types = _find_decoder_layers(model)
+        if self.layers is None:
+            LOG.warning(
+                "LayerOffloadManager: no decoder layers found, offloading disabled"
+            )
+            self.enabled = False
+            return
+
+        self.enabled = True
+        self.n_layers = len(self.layers)
+        LOG.info(
+            f"Layer offloading: found {self.n_layers} layers ({', '.join(layer_types)})"
+        )
+
+        # Determine GPU device
+        for p in model.parameters():
+            if p.device.type == "cuda":
+                self._device = p.device
+                break
+        if self._device is None:
+            LOG.warning("LayerOffloadManager: no CUDA parameters found")
+            self.enabled = False
+            return
+
+        # Transfer stream for async prefetch
+        self._transfer_stream = torch.cuda.Stream(device=self._device)
+
+        # Track which layers have their frozen params on GPU
+        self._on_gpu: set[int] = set(range(self.n_layers))
+
+        # Cache: frozen param references per layer (list of (name, param) tuples)
+        self._frozen_params: list[list[tuple[str, nn.Parameter]]] = [
+            _get_frozen_params(self.layers[i]) for i in range(self.n_layers)
+        ]
+
+        # CPU storage: pinned tensors for each layer's frozen params
+        # Populated on first offload
+        self._cpu_data: list[dict[str, torch.Tensor]] = [
+            {} for _ in range(self.n_layers)
+        ]
+
+        # Offload all layers upfront
+        self._offload_all()
+
+        # Release cached memory blocks back to the driver
+        torch.cuda.empty_cache()
+
+    def _offload_all(self):
+        """Move all frozen params in all decoder layers to CPU."""
+        mem_before = torch.cuda.memory_allocated(self._device)
+        for i in range(self.n_layers):
+            self._offload_layer(i)
+        mem_after = torch.cuda.memory_allocated(self._device)
+        freed = (mem_before - mem_after) / 1e6
+        LOG.info(
+            f"Layer offloading: offloaded frozen params from {self.n_layers} layers, "
+            f"freed {freed:.0f} MB GPU memory"
+        )
+
+    def _offload_layer(self, idx: int):
+        """Move frozen params of layer idx to CPU pinned memory."""
+        if idx not in self._on_gpu:
+            return
+        for name, param in self._frozen_params[idx]:
+            if param.device.type != "cuda":
+                continue
+            # Allocate pinned CPU tensor on first offload
+            if name not in self._cpu_data[idx]:
+                self._cpu_data[idx][name] = torch.empty_like(
+                    param.data, device="cpu", pin_memory=True
+                )
+            cpu_buf = self._cpu_data[idx][name]
+            # Async copy GPU -> CPU (on transfer stream for overlap)
+            cpu_buf.copy_(param.data, non_blocking=True)
+            # Point parameter at a dummy CPU tensor to free GPU memory
+            param.data = cpu_buf
+        self._on_gpu.discard(idx)
+
+    def _load_layer(self, idx: int, stream=None):
+        """Move frozen params of layer idx back to GPU."""
+        if idx in self._on_gpu or idx < 0 or idx >= self.n_layers:
+            return
+        ctx = (
+            torch.cuda.stream(stream)
+            if stream is not None
+            else contextlib.nullcontext()
+        )
+        with ctx:
+            for _name, param in self._frozen_params[idx]:
+                if param.device.type == "cuda":
+                    continue
+                gpu_data = param.data.to(self._device, non_blocking=True)
+                param.data = gpu_data
+        self._on_gpu.add(idx)
+
+    def _prefetch_layer(self, idx: int):
+        """Async prefetch layer idx on the transfer stream."""
+        if idx in self._on_gpu or idx < 0 or idx >= self.n_layers:
+            return
+        self._transfer_stream.wait_stream(torch.cuda.default_stream(self._device))
+        self._load_layer(idx, stream=self._transfer_stream)
+
+    def _wait_transfer(self):
+        """Make default stream wait for any in-flight transfers."""
+        torch.cuda.default_stream(self._device).wait_stream(self._transfer_stream)
+
+    def setup_hooks(self):
+        """Register forward and backward hooks on each decoder layer."""
+        if not self.enabled:
+            return
+
+        for idx in range(self.n_layers):
+            layer = self.layers[idx]
+
+            def make_pre_fwd(i):
+                def hook(module, args):
+                    # Ensure this layer is on GPU
+                    if i not in self._on_gpu:
+                        self._load_layer(i)
+                    self._wait_transfer()
+                    # Prefetch next layer(s)
+                    for offset in range(1, self.num_prefetch + 1):
+                        self._prefetch_layer(i + offset)
+
+                return hook
+
+            def make_post_fwd(i):
+                def hook(module, args, output):
+                    # Offload previous layer (no longer needed in forward)
+                    if i > 0:
+                        self._offload_layer(i - 1)
+                    # Offload last layer after forward
+                    if i == self.n_layers - 1:
+                        self._offload_layer(i)
+
+                return hook
+
+            def make_pre_bwd(i):
+                def hook(module, grad_output):
+                    # Load this layer for backward
+                    if i not in self._on_gpu:
+                        self._load_layer(i)
+                    self._wait_transfer()
+                    # Prefetch previous layer(s)
+                    for offset in range(1, self.num_prefetch + 1):
+                        self._prefetch_layer(i - offset)
+
+                return hook
+
+            def make_post_bwd(i):
+                def hook(module, grad_input, grad_output):
+                    # Offload the layer above
+                    if i < self.n_layers - 1:
+                        self._offload_layer(i + 1)
+                    # Offload first layer after backward
+                    if i == 0:
+                        self._offload_layer(i)
+
+                return hook
+
+            h1 = layer.register_forward_pre_hook(make_pre_fwd(idx))
+            h2 = layer.register_forward_hook(make_post_fwd(idx))
+            h3 = layer.register_full_backward_pre_hook(make_pre_bwd(idx))
+            h4 = layer.register_full_backward_hook(make_post_bwd(idx))
+            self._hooks.extend([h1, h2, h3, h4])
+
+    def remove_hooks(self):
+        """Remove all hooks and restore layers to GPU."""
+        for h in self._hooks:
+            h.remove()
+        self._hooks.clear()
+        if self.enabled:
+            for i in range(self.n_layers):
+                if i not in self._on_gpu:
+                    self._load_layer(i)
+
+    def pre_step(self):
+        """Called before each training step — ensure layers start offloaded."""
+        if not self.enabled:
+            return
+        for i in list(self._on_gpu):
+            self._offload_layer(i)
+        # Prefetch layer 0 for forward
+        self._prefetch_layer(0)
+
+    def post_step(self):
+        """Called after each training step — ensure layers are offloaded."""
+        if not self.enabled:
+            return
+        for i in list(self._on_gpu):
+            self._offload_layer(i)
+        # Prefetch layer 0 for next step
+        self._prefetch_layer(0)
+
+
+class _LayerOffloadContext:
+    """Context manager wrapping pre_step / post_step around a training step."""
+
+    def __init__(self, manager: LayerOffloadManager):
+        self.manager = manager
+
+    def __enter__(self):
+        self.manager.pre_step()
+        return self
+
+    def __exit__(self, *args):
+        self.manager.post_step()
+
+
+class LayerOffloadingMixin(Trainer):
+    """
+    Trainer mixin class for layer-wise parameter offloading to CPU.
+
+    Offloads frozen decoder layer params to CPU at init, then streams them
+    on/off GPU one layer at a time during each training step.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if getattr(self.args, "layer_offloading", False):
+            LOG.info("Layer parameter offloading enabled")
+            self._layer_offload_manager = LayerOffloadManager(
+                model=self.model,
+                num_prefetch=1,
+            )
+            self._layer_offload_manager.setup_hooks()
+            self._layer_offload_ctx = _LayerOffloadContext(self._layer_offload_manager)
+        else:
+            self._layer_offload_manager = None
+            self._layer_offload_ctx = contextlib.nullcontext()
+
+    def training_step(self, *args, **kwargs):
+        with self._layer_offload_ctx:
+            return super().training_step(*args, **kwargs)

src/axolotl/core/training_args_base.py

@@ -235,6 +235,13 @@ class AxolotlTrainingMixins:
         metadata={"help": "Use activation offloading with CUDA streams for training."},
     )
 
+    layer_offloading: bool | None = field(
+        default=None,
+        metadata={
+            "help": "Offload model layer parameters to CPU during forward, prefetch back during backward."
+        },
+    )
+
     # multi-modal section
 
     image_size: int | tuple[int, int] | None = field(

src/axolotl/integrations/kernels/constants.py

@@ -15,6 +15,7 @@ SPARSE_MOE_BLOCK = {
     "qwen2_moe": "Qwen2MoeSparseMoeBlock",
     "qwen3_moe": "Qwen3MoeSparseMoeBlock",
     "qwen3_5_moe": "Qwen3_5MoeSparseMoeBlock",
+    "qwen3_5_moe_text": "Qwen3_5MoeSparseMoeBlock",
     "qwen3_next": "Qwen3NextSparseMoeBlock",
     "qwen3_vl_moe": "Qwen3VLMoeTextSparseMoeBlock",
     # qwen3_omni_moe: Thinker (standard) + Talker (shared experts + shared_expert_gate)
@@ -58,7 +59,16 @@ def resolve_moe_block_classes(model_type: str):
 
     cls_names = entry if isinstance(entry, list) else [entry]
     module_path = f"transformers.models.{model_type}.modeling_{model_type}"
-    module = importlib.import_module(module_path)
+    try:
+        module = importlib.import_module(module_path)
+    except ModuleNotFoundError:
+        # Text sub-model types (e.g. qwen3_5_moe_text) share the parent module
+        if model_type.endswith("_text"):
+            parent_type = model_type.removesuffix("_text")
+            module_path = f"transformers.models.{parent_type}.modeling_{parent_type}"
+            module = importlib.import_module(module_path)
+        else:
+            raise
 
     classes = []
     for cls_name in cls_names:

src/axolotl/integrations/kernels/libs/scattermoe_lora/kernels/lora_ops.py

@@ -363,7 +363,7 @@ def _scatter2scatter_lora_configs():
 
     Search space:
       BLOCK_M:    {32, 64, 128}
-      BLOCK_N:    {32, 64, 128, 256}
+      BLOCK_N:    {32, 64}
       BLOCK_K:    {32, 64, 128}
       num_warps:  {4, 8}
       num_stages: {3, 4, 5}
@@ -371,7 +371,7 @@ def _scatter2scatter_lora_configs():
     configs = []
     for block_m, block_n, block_k, warps, stages in product(
         [32, 64, 128],  # BLOCK_M
-        [32, 64, 128, 256],  # BLOCK_N
+        [32, 64],  # BLOCK_N
         [32, 64, 128],  # BLOCK_K
         [4, 8],  # num_warps
         [3, 4, 5],  # num_stages
@@ -943,16 +943,16 @@ def _scatter2scatter_lora_dX_configs():
 
     Search space:
       BLOCK_M:    {32, 64, 128}        (token tile)
-      BLOCK_K:    {32, 64, 128, 256}   (output tile)
-      BLOCK_N:    {32, 64, 128, 256}   (reduction tile)
+      BLOCK_K:    {32, 64, 128}   (output tile)
+      BLOCK_N:    {32, 64}   (reduction tile)
       num_warps:  {4, 8}
       num_stages: {3, 4, 5}
     """
     configs = []
     for block_m, block_k, block_n, warps, stages in product(
         [32, 64, 128],  # BLOCK_M
-        [32, 64, 128, 256],  # BLOCK_K (output dimension)
-        [32, 64, 128, 256],  # BLOCK_N (reduction dimension)
+        [32, 64, 128],  # BLOCK_K (output dimension)
+        [32, 64],  # BLOCK_N (reduction dimension)
         [4, 8],  # num_warps
         [3, 4, 5],  # num_stages
     ):
@@ -1278,9 +1278,9 @@ def _group_bwd_lora_configs():
     support GPUs with limited shared memory (e.g. ~99KB on some GPUs).
 
     Search space:
-      BLOCK_M:    {32, 64, 128, 256}   (token-loop tile)
-      BLOCK_K:    {32, 64, 128, 256}
-      BLOCK_N:    {32, 64, 128, 256}
+      BLOCK_M:    {32, 64, 128}   (token-loop tile)
+      BLOCK_K:    {32, 64, 128}
+      BLOCK_N:    {32, 64}
       num_warps:  {4, 8}
       num_stages: {3, 4, 5}
 
@@ -1289,9 +1289,9 @@ def _group_bwd_lora_configs():
     """
     configs = []
     for block_m, block_k, block_n, warps, stages in product(
-        [32, 64, 128, 256],  # BLOCK_M
-        [32, 64, 128, 256],  # BLOCK_K
-        [32, 64, 128, 256],  # BLOCK_N
+        [32, 64, 128],  # BLOCK_M
+        [32, 64, 128],  # BLOCK_K
+        [32, 64],  # BLOCK_N
         [4, 8],  # num_warps
         [3, 4, 5],  # num_stages
     ):

src/axolotl/integrations/liger/args.py

@@ -30,6 +30,15 @@ class LigerArgs(BaseModel):
 
     liger_rope: bool | None = None
     liger_rms_norm: bool | None = None
+    liger_rms_norm_gated: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": (
+                "Enables fused RMSNorm+SiLU gate Triton kernel for models with "
+                "gated RMSNorm (e.g. Qwen3.5 / Qwen3.5 MoE linear attention layers)."
+            )
+        },
+    )
     liger_layer_norm: bool | None = None
     liger_swiglu: bool | None = None
     liger_glu_activation: bool | None = None

src/axolotl/integrations/liger/models/qwen3_5.py

@@ -0,0 +1,175 @@
+"""
+Liger FLCE for Qwen3.5. Based on transformers v5.3.0.
+"""
+
+import sys
+from copy import deepcopy
+from typing import Optional, Union
+
+import torch
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from transformers.cache_utils import Cache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+def lce_forward(
+    self,
+    input_ids: Optional[torch.LongTensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Cache] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    **kwargs,
+) -> CausalLMOutputWithPast:
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+    Returns:
+    """
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+
+    logits = None
+    loss = None
+    # if in training mode, don't materialize logits
+    if self.training and (labels is not None):
+        loss = LigerForCausalLMLoss(
+            hidden_states=hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+
+    else:  # if in inference mode materialize logits
+        slice_indices = (
+            slice(-logits_to_keep, None)
+            if isinstance(logits_to_keep, int)
+            else logits_to_keep
+        )
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )
+
+
+def apply_liger_kernel_to_qwen3_5(
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = False,
+    rms_norm: bool = False,
+    rms_norm_gated: bool = False,
+    glu_activation: bool = False,
+    layer_norm: bool = False,
+    **kwargs,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Qwen3.5 models.
+
+    Note: Qwen3_5RMSNorm uses zero-init weight with offset 1.0 (like Gemma),
+    so we use LigerRMSNorm with offset=1.0 and init_fn="zeros".
+
+    Args:
+        cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False.
+        fused_linear_cross_entropy (bool):
+            Whether to apply Liger's fused linear cross entropy loss. Default is False.
+            `cross_entropy` and `fused_linear_cross_entropy` cannot both be True.
+            If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient.
+        rms_norm (bool): Whether to apply Liger's RMSNorm. Default is False.
+        rms_norm_gated (bool): Whether to apply fused RMSNorm+SiLU gate kernel for
+            Qwen3_5RMSNormGated (used in linear attention layers). Default is False.
+        glu_activation (bool): Whether to apply Liger's SwiGLU MLP. Default is False.
+        layer_norm (bool): Whether to apply Liger's LayerNorm. Default is False.
+    """
+
+    import transformers.models.qwen3_5.modeling_qwen3_5  # noqa: F401
+    from liger_kernel.transformers.functional import liger_cross_entropy
+    from liger_kernel.transformers.layer_norm import LigerLayerNorm
+    from liger_kernel.transformers.rms_norm import LigerRMSNorm
+    from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
+
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    modeling_qwen3_5 = sys.modules["transformers.models.qwen3_5.modeling_qwen3_5"]
+
+    if rms_norm:
+        # Qwen3_5RMSNorm uses zero-init weight with `output * (1.0 + weight)` pattern
+        class LigerRMSNormForQwen3_5(LigerRMSNorm):
+            def __init__(self, dim, eps=1e-6, **kwargs):
+                super().__init__(
+                    dim,
+                    eps=eps,
+                    offset=1.0,
+                    casting_mode="gemma",
+                    init_fn="zeros",
+                    in_place=False,
+                )
+
+        modeling_qwen3_5.Qwen3_5RMSNorm = LigerRMSNormForQwen3_5
+
+    if rms_norm_gated:
+        from axolotl.kernels.rms_norm_gated import FusedRMSNormGated
+
+        modeling_qwen3_5.Qwen3_5RMSNormGated = FusedRMSNormGated
+
+    if glu_activation:
+
+        def _liger_swiglu_mlp_wrapper(config, intermediate_size=None, **kwargs):
+            """Accepts intermediate_size to pass to LigerSwiGLUMLP"""
+            config = deepcopy(config)
+            if intermediate_size is not None:
+                config.intermediate_size = intermediate_size
+            return LigerSwiGLUMLP(config, **kwargs)
+
+        modeling_qwen3_5.Qwen3_5MLP = _liger_swiglu_mlp_wrapper
+
+    if layer_norm:
+        modeling_qwen3_5.nn.LayerNorm = LigerLayerNorm
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        modeling_qwen3_5.Qwen3_5ForCausalLM.forward = lce_forward

src/axolotl/integrations/liger/models/qwen3_5_moe.py

@@ -0,0 +1,198 @@
+"""
+Liger FLCE for Qwen3.5 MoE. Based on transformers v5.3.0.
+"""
+
+import sys
+from copy import deepcopy
+from typing import Optional, Union
+
+import torch
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from transformers.modeling_outputs import MoeCausalLMOutputWithPast
+
+
+def lce_forward(
+    self,
+    input_ids: Optional[torch.LongTensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values=None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_router_logits: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    **kwargs,
+) -> MoeCausalLMOutputWithPast:
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+    Returns:
+    """
+    from transformers.models.qwen3_5_moe.modeling_qwen3_5_moe import (
+        load_balancing_loss_func,
+    )
+
+    output_router_logits = (
+        output_router_logits
+        if output_router_logits is not None
+        else self.config.output_router_logits
+    )
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_router_logits=output_router_logits,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+
+    logits = None
+    loss = None
+    # if in training mode, don't materialize logits
+    if self.training and (labels is not None):
+        loss = LigerForCausalLMLoss(
+            hidden_states=hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+
+    else:  # if in inference mode materialize logits
+        slice_indices = (
+            slice(-logits_to_keep, None)
+            if isinstance(logits_to_keep, int)
+            else logits_to_keep
+        )
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+        if labels is not None:
+            loss = self.loss_function(
+                logits,
+                labels,
+                self.vocab_size,
+                **kwargs,
+            )
+
+    aux_loss = None
+    if output_router_logits:
+        aux_loss = load_balancing_loss_func(
+            outputs.router_logits,
+            self.num_experts,
+            self.num_experts_per_tok,
+            attention_mask,
+        )
+        if labels is not None:
+            loss += self.router_aux_loss_coef * aux_loss.to(loss.device)
+
+    return MoeCausalLMOutputWithPast(
+        loss=loss,
+        aux_loss=aux_loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        router_logits=outputs.router_logits,
+    )
+
+
+def apply_liger_kernel_to_qwen3_5_moe(
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = False,
+    rms_norm: bool = False,
+    rms_norm_gated: bool = False,
+    glu_activation: bool = False,
+    layer_norm: bool = False,
+    **kwargs,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Qwen3.5 MoE models.
+
+    Note: Qwen3_5MoeRMSNorm uses zero-init weight with offset 1.0 (like Gemma),
+    so we use LigerRMSNorm with offset=1.0 and init_fn="zeros".
+
+    Args:
+        cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False.
+        fused_linear_cross_entropy (bool):
+            Whether to apply Liger's fused linear cross entropy loss. Default is False.
+            `cross_entropy` and `fused_linear_cross_entropy` cannot both be True.
+            If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient.
+        rms_norm (bool): Whether to apply Liger's RMSNorm. Default is False.
+        rms_norm_gated (bool): Whether to apply fused RMSNorm+SiLU gate kernel for
+            Qwen3_5MoeRMSNormGated (used in linear attention layers). Default is False.
+        glu_activation (bool): Whether to apply Liger's SwiGLU MLP. Default is False.
+        layer_norm (bool): Whether to apply Liger's LayerNorm. Default is False.
+    """
+
+    import transformers.models.qwen3_5_moe.modeling_qwen3_5_moe  # noqa: F401
+    from liger_kernel.transformers.functional import liger_cross_entropy
+    from liger_kernel.transformers.layer_norm import LigerLayerNorm
+    from liger_kernel.transformers.rms_norm import LigerRMSNorm
+    from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
+
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    modeling_mod = sys.modules["transformers.models.qwen3_5_moe.modeling_qwen3_5_moe"]
+
+    if rms_norm:
+        # Qwen3_5MoeRMSNorm uses zero-init weight with `output * (1.0 + weight)` pattern
+        class LigerRMSNormForQwen3_5Moe(LigerRMSNorm):
+            def __init__(self, dim, eps=1e-6, **kwargs):
+                super().__init__(
+                    dim,
+                    eps=eps,
+                    offset=1.0,
+                    casting_mode="gemma",
+                    init_fn="zeros",
+                    in_place=False,
+                )
+
+        modeling_mod.Qwen3_5MoeRMSNorm = LigerRMSNormForQwen3_5Moe
+
+    if rms_norm_gated:
+        from axolotl.kernels.rms_norm_gated import FusedRMSNormGated
+
+        modeling_mod.Qwen3_5MoeRMSNormGated = FusedRMSNormGated
+
+    if glu_activation:
+
+        def _liger_swiglu_mlp_wrapper(config, intermediate_size=None, **kwargs):
+            """Accepts intermediate_size to pass to LigerSwiGLUMLP"""
+            config = deepcopy(config)
+            if intermediate_size is not None:
+                config.intermediate_size = intermediate_size
+            return LigerSwiGLUMLP(config, **kwargs)
+
+        modeling_mod.Qwen3_5MoeMLP = _liger_swiglu_mlp_wrapper
+
+    if layer_norm:
+        modeling_mod.nn.LayerNorm = LigerLayerNorm
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        modeling_mod.Qwen3_5MoeForCausalLM.forward = lce_forward

src/axolotl/integrations/liger/plugin.py

@@ -174,6 +174,19 @@ class LigerPlugin(BasePlugin):
                 rms_norm=cfg.liger_rms_norm,
                 layer_norm=cfg.liger_layer_norm,
             )
+        elif cfg.model_config_type == "qwen3_5":
+            from axolotl.integrations.liger.models.qwen3_5 import (
+                apply_liger_kernel_to_qwen3_5,
+            )
+
+            apply_liger_kernel_to_qwen3_5(
+                cross_entropy=cfg.liger_cross_entropy,
+                fused_linear_cross_entropy=cfg.liger_fused_linear_cross_entropy,
+                glu_activation=cfg.liger_glu_activation,
+                rms_norm=cfg.liger_rms_norm,
+                rms_norm_gated=getattr(cfg, "liger_rms_norm_gated", False),
+                layer_norm=cfg.liger_layer_norm,
+            )
         elif cfg.model_config_type == "qwen3_moe":
             from axolotl.integrations.liger.models.qwen3_moe import (
                 apply_liger_kernel_to_qwen3_moe,
@@ -186,6 +199,19 @@ class LigerPlugin(BasePlugin):
                 rms_norm=cfg.liger_rms_norm,
                 layer_norm=cfg.liger_layer_norm,
             )
+        elif cfg.model_config_type == "qwen3_5_moe":
+            from axolotl.integrations.liger.models.qwen3_5_moe import (
+                apply_liger_kernel_to_qwen3_5_moe,
+            )
+
+            apply_liger_kernel_to_qwen3_5_moe(
+                cross_entropy=cfg.liger_cross_entropy,
+                fused_linear_cross_entropy=cfg.liger_fused_linear_cross_entropy,
+                glu_activation=cfg.liger_glu_activation,
+                rms_norm=cfg.liger_rms_norm,
+                rms_norm_gated=getattr(cfg, "liger_rms_norm_gated", False),
+                layer_norm=cfg.liger_layer_norm,
+            )
         elif cfg.model_config_type == "granitemoe":
             from liger_kernel.transformers import apply_liger_kernel_to_granite
 

src/axolotl/kernels/dora.py

@@ -0,0 +1,147 @@
+"""
+Triton kernels for DoRA (Weight-Decomposed Low-Rank Adaptation).
+
+Fuses the weight norm computation and magnitude scaling to avoid
+materializing the full [out_features, in_features] combined weight matrix.
+The B@A product is computed row-by-row inside the kernel.
+"""
+
+import torch
+import triton
+import triton.language as tl
+
+from .quantize import dequantize
+
+
+@triton.jit
+def _dora_fused_norm_kernel(
+    # Pointers
+    W_ptr,  # base weight [out, in] (dequantized, row-major)
+    B_ptr,  # LoRA B [out, rank] (row-major)
+    A_ptr,  # LoRA A [rank, in] (row-major)
+    mag_ptr,  # magnitude vector [out]
+    out_ptr,  # output mag_norm_scale [out]
+    # Shapes
+    out_features,
+    in_features,
+    rank,
+    # Scaling
+    lora_scale,  # float scaling factor
+    # Block sizes
+    BLOCK_IN: tl.constexpr,
+    BLOCK_R: tl.constexpr,  # >= rank, power of 2
+):
+    """Compute mag_norm_scale[i] = magnitude[i] / ||W[i,:] + s * (B[i,:] @ A)[:] ||_2
+
+    Each program handles one output row. B[row,:] is loaded once (small),
+    then we tile over in_features computing the dot product with A[:,tile]
+    and accumulating the squared norm.
+
+    This avoids materializing the full [out, in] B@A matrix.
+    """
+    row = tl.program_id(0)
+    if row >= out_features:
+        return
+
+    # Accumulate squared norm across tiles of in_features
+    norm_sq_acc = tl.zeros([BLOCK_IN], dtype=tl.float32)
+
+    for start in range(0, in_features, BLOCK_IN):
+        cols = start + tl.arange(0, BLOCK_IN)
+        col_mask = cols < in_features
+
+        # Load W[row, cols]
+        w_vals = tl.load(
+            W_ptr + row * in_features + cols,
+            mask=col_mask,
+            other=0.0,
+        ).to(tl.float32)
+
+        # Compute (B[row,:] @ A[:, cols]) for this tile
+        # Load B[row, r] as scalar and A[r, cols] as vector for each r
+        ba_vals = tl.zeros([BLOCK_IN], dtype=tl.float32)
+        for r in tl.static_range(BLOCK_R):
+            # Load scalar B[row, r]
+            b_val = tl.load(
+                B_ptr + row * rank + r,
+                mask=(r < rank),
+                other=0.0,
+            ).to(tl.float32)
+            # Load vector A[r, cols]
+            a_vals = tl.load(
+                A_ptr + r * in_features + cols,
+                mask=(col_mask & (r < rank)),
+                other=0.0,
+            ).to(tl.float32)
+            ba_vals += b_val * a_vals
+
+        # Combined: W + s * (B @ A)
+        combined = w_vals + lora_scale * ba_vals
+
+        # Accumulate squared values
+        norm_sq_acc += tl.where(col_mask, combined * combined, 0.0)
+
+    # Reduce to scalar norm
+    norm_sq = tl.sum(norm_sq_acc, axis=0)
+    norm = tl.sqrt(norm_sq + 1e-12)  # epsilon for numerical stability
+
+    # Load magnitude and compute scale
+    mag = tl.load(mag_ptr + row).to(tl.float32)
+    scale = mag / norm
+
+    tl.store(out_ptr + row, scale)
+
+
+def triton_dora_scale(
+    W: torch.Tensor,
+    W_quant,
+    A: torch.Tensor,
+    B: torch.Tensor,
+    s: float,
+    magnitude: torch.Tensor,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    """Compute DoRA mag_norm_scale using fused Triton kernel.
+
+    Computes B@A row-by-row inside the kernel, avoiding the full
+    [out_features, in_features] materialization.
+
+    Args:
+        W: base weight [out, in] (possibly quantized)
+        W_quant: quantization state
+        A: LoRA A [rank, in]
+        B: LoRA B [out, rank]
+        s: LoRA scaling factor
+        magnitude: learned magnitude [out]
+        dtype: compute dtype
+
+    Returns:
+        mag_norm_scale: [out] tensor = magnitude / ||W + s * B @ A||_2
+    """
+    # Dequantize W to [out, in]
+    W_full = dequantize(W.t(), W_quant).t().contiguous().to(dtype)
+
+    out_features, in_features = W_full.shape
+    rank = A.shape[0]
+
+    out = torch.empty(out_features, dtype=dtype, device=W.device)
+
+    # Block sizes
+    BLOCK_IN = triton.next_power_of_2(min(in_features, 2048))
+    BLOCK_R = triton.next_power_of_2(rank)
+
+    _dora_fused_norm_kernel[(out_features,)](
+        W_full,
+        B.contiguous().to(dtype),
+        A.contiguous().to(dtype),
+        magnitude.contiguous(),
+        out,
+        out_features=out_features,
+        in_features=in_features,
+        rank=rank,
+        lora_scale=s,
+        BLOCK_IN=BLOCK_IN,
+        BLOCK_R=BLOCK_R,
+    )
+
+    return out.detach()

src/axolotl/kernels/quantize.py

@@ -105,6 +105,10 @@ def dequantize(
     # Extract quantization state
     if not isinstance(quant_state, list):
         # New style quant_state class
+        # Non-double-quantized models have offset=None and state2=None
+        if quant_state.offset is None or quant_state.state2 is None:
+            # Fall back to bitsandbytes standard dequantize
+            return bnb.functional.dequantize_4bit(W, quant_state, quant_type="nf4")
         absmax = quant_state.absmax.to(target_device)
         shape = quant_state.shape
         dtype = quant_state.dtype

src/axolotl/kernels/rms_norm_gated.py

@@ -0,0 +1,333 @@
+"""
+Fused RMSNorm + SiLU Gate Triton kernel.
+
+Computes: Y = (W + offset) * RMSNorm(X) * silu(G)
+where RMSNorm(X) = X / sqrt(mean(X^2) + eps)
+and silu(G) = G * sigmoid(G)
+
+Used by Qwen3.5's GatedDeltaNet linear attention layers (Qwen3_5RMSNormGated).
+"""
+
+import math
+import operator
+
+import torch
+import triton
+import triton.language as tl
+from liger_kernel.ops.utils import (
+    calculate_settings,
+    compare_version,
+    ensure_contiguous,
+    torch_to_triton_dtype,
+)
+from liger_kernel.utils import is_npu_available
+
+if compare_version("triton", operator.ge, "3.0.0") and not is_npu_available():
+    try:
+        from triton.language.extra.libdevice import rsqrt
+    except ModuleNotFoundError:
+        from triton.language.extra.cuda.libdevice import rsqrt
+else:
+    from triton.language.math import rsqrt
+
+
+@triton.jit
+def _rms_norm_gated_forward_kernel(
+    Y_ptr,
+    Y_row_stride,
+    X_ptr,
+    X_row_stride,
+    G_ptr,
+    G_row_stride,
+    W_ptr,
+    W_row_stride,
+    RSTD_ptr,
+    RSTD_row_stride,
+    n_cols,
+    eps,
+    offset,
+    BLOCK_SIZE: tl.constexpr,
+):
+    """
+    Y = (W + offset) * (X / RMS(X)) * silu(G)
+
+    All computation done in fp32 (Gemma-style), result cast to input dtype.
+    """
+    row_idx = tl.program_id(0).to(tl.int64)
+    col_offsets = tl.arange(0, BLOCK_SIZE)
+    mask = col_offsets < n_cols
+
+    X_row = tl.load(X_ptr + row_idx * X_row_stride + col_offsets, mask=mask, other=0)
+    G_row = tl.load(G_ptr + row_idx * G_row_stride + col_offsets, mask=mask, other=0)
+    W_row = tl.load(W_ptr + col_offsets, mask=mask, other=0)
+
+    X_row_dtype = X_row.dtype
+
+    # Cast everything to fp32
+    X_fp32 = X_row.to(tl.float32)
+    G_fp32 = G_row.to(tl.float32)
+    W_fp32 = W_row.to(tl.float32)
+
+    # RMS norm
+    mean_sq = tl.sum(X_fp32 * X_fp32, axis=0) / n_cols
+    rstd = rsqrt(mean_sq + eps)
+    tl.store(RSTD_ptr + row_idx * RSTD_row_stride, rstd)
+
+    X_norm = X_fp32 * rstd
+
+    # SiLU gate: silu(G) = G * sigmoid(G)
+    sig_G = tl.sigmoid(G_fp32)
+    silu_G = G_fp32 * sig_G
+
+    # Fused output
+    Y_row = (offset + W_fp32) * X_norm * silu_G
+
+    tl.store(
+        Y_ptr + row_idx * Y_row_stride + col_offsets,
+        Y_row.to(X_row_dtype),
+        mask=mask,
+    )
+
+
+@triton.jit
+def _rms_norm_gated_backward_kernel(
+    dY_ptr,
+    dY_row_stride,
+    dX_ptr,
+    dX_row_stride,
+    dG_ptr,
+    dG_row_stride,
+    X_ptr,
+    X_row_stride,
+    X_dtype: tl.constexpr,
+    G_ptr,
+    G_row_stride,
+    W_ptr,
+    W_row_stride,
+    RSTD_ptr,
+    RSTD_row_stride,
+    dW_ptr,
+    dW_row_stride,
+    n_rows,
+    n_cols,
+    offset,
+    rows_per_program,
+    BLOCK_SIZE: tl.constexpr,
+):
+    """
+    Backward for Y = (W + offset) * (X * RSTD) * silu(G)
+
+    dW = sum_batch(dY * X_norm * silu(G))
+    dG = dY * (W + offset) * X_norm * silu'(G)
+       where silu'(G) = sigmoid(G) * (1 + G * (1 - sigmoid(G)))
+    dX = RSTD * (m - (1/N) * RSTD^2 * dot(m, X) * X)
+       where m = dY * (W + offset) * silu(G)
+    """
+    row_block_id = tl.program_id(0).to(tl.int64)
+    row_start = row_block_id * rows_per_program
+    row_end = min((row_block_id + 1) * rows_per_program, n_rows)
+    col_offsets = tl.arange(0, BLOCK_SIZE)
+    mask = col_offsets < n_cols
+
+    dW_acc = tl.zeros((BLOCK_SIZE,), dtype=tl.float32)
+
+    W_row = tl.load(W_ptr + col_offsets, mask=mask, other=0.0)
+    W_row = W_row.to(tl.float32) + offset
+
+    for row_idx in range(row_start, row_end):
+        dY_row = tl.load(
+            dY_ptr + row_idx * dY_row_stride + col_offsets, mask=mask, other=0.0
+        )
+        X_row = tl.load(
+            X_ptr + row_idx * X_row_stride + col_offsets, mask=mask, other=0.0
+        )
+        G_row = tl.load(
+            G_ptr + row_idx * G_row_stride + col_offsets, mask=mask, other=0.0
+        )
+        rstd_row = tl.load(RSTD_ptr + row_idx * RSTD_row_stride)
+
+        # Cast to fp32
+        dY_fp32 = dY_row.to(tl.float32)
+        X_fp32 = X_row.to(tl.float32)
+        G_fp32 = G_row.to(tl.float32)
+
+        # Recompute intermediates
+        X_norm = X_fp32 * rstd_row
+        sig_G = tl.sigmoid(G_fp32)
+        silu_G = G_fp32 * sig_G
+
+        # dW: accumulate dY * X_norm * silu(G)
+        dW_acc += dY_fp32 * X_norm * silu_G
+
+        # dG: dY * (W + offset) * X_norm * silu'(G)
+        # silu'(G) = sigmoid(G) * (1 + G * (1 - sigmoid(G)))
+        silu_prime_G = sig_G * (1.0 + G_fp32 * (1.0 - sig_G))
+        dG_row = dY_fp32 * W_row * X_norm * silu_prime_G
+        tl.store(
+            dG_ptr + row_idx * dG_row_stride + col_offsets,
+            dG_row.to(X_dtype),
+            mask=mask,
+        )
+
+        # dX: standard RMSNorm backward with effective gradient m = dY * W * silu(G)
+        m = dY_fp32 * W_row * silu_G
+        dX_row = rstd_row * m
+        dX_row += rstd_row * (
+            -(1.0 / n_cols) * rstd_row * rstd_row * tl.sum(m * X_fp32, axis=0) * X_fp32
+        )
+        tl.store(
+            dX_ptr + row_idx * dX_row_stride + col_offsets,
+            dX_row.to(X_dtype),
+            mask=mask,
+        )
+
+    tl.store(
+        dW_ptr + row_block_id * dW_row_stride + col_offsets,
+        dW_acc,
+        mask=mask,
+    )
+
+
+def rms_norm_gated_forward(X, G, W, eps, offset):
+    shape = X.shape
+    dim = shape[-1]
+    X = X.view(-1, dim)
+    G = G.view(-1, dim)
+    n_rows, n_cols = X.shape
+    BLOCK_SIZE, num_warps = calculate_settings(n_cols)
+
+    Y = torch.empty((n_rows, n_cols), dtype=X.dtype, device=X.device)
+    RSTD = torch.empty(n_rows, dtype=torch.float32, device=X.device)
+
+    assert X.shape[1] == W.shape[0], (
+        f"Incompatible hidden size: X.shape[1]={X.shape[1]} vs W.shape[0]={W.shape[0]}"
+    )
+    assert X.shape == G.shape, (
+        f"X and G must have same shape, got {X.shape} and {G.shape}"
+    )
+
+    _rms_norm_gated_forward_kernel[(n_rows,)](
+        Y,
+        Y.stride(0),
+        X,
+        X.stride(0),
+        G,
+        G.stride(0),
+        W,
+        W.stride(0),
+        RSTD,
+        RSTD.stride(0),
+        n_cols,
+        eps,
+        offset,
+        BLOCK_SIZE=BLOCK_SIZE,
+        num_warps=num_warps,
+    )
+    return Y.view(*shape), X, G, RSTD, BLOCK_SIZE, num_warps
+
+
+def rms_norm_gated_backward(dY, X, G, W, RSTD, offset, BLOCK_SIZE, num_warps):
+    shape = dY.shape
+    dim = shape[-1]
+    dY = dY.view(-1, dim)
+    n_rows, n_cols = dY.shape
+
+    sm_count = torch.cuda.get_device_properties(X.device).multi_processor_count
+
+    _dW = torch.empty((sm_count, n_cols), dtype=torch.float32, device=W.device)
+    dX = torch.empty_like(dY)
+    dG = torch.empty_like(dY)
+
+    rows_per_program = math.ceil(n_rows / sm_count)
+    grid = (sm_count,)
+
+    _rms_norm_gated_backward_kernel[grid](
+        dY,
+        dY.stride(0),
+        dX,
+        dX.stride(0),
+        dG,
+        dG.stride(0),
+        X,
+        X.stride(0),
+        torch_to_triton_dtype[X.dtype],
+        G,
+        G.stride(0),
+        W,
+        W.stride(0),
+        RSTD,
+        RSTD.stride(0),
+        _dW,
+        _dW.stride(0),
+        n_rows,
+        n_cols,
+        offset,
+        rows_per_program,
+        BLOCK_SIZE=BLOCK_SIZE,
+        num_warps=num_warps,
+    )
+
+    dX = dX.view(*shape)
+    dG = dG.view(*shape)
+    dW = _dW.sum(dim=0).to(W.dtype)
+    return dX, dG, dW
+
+
+class FusedRMSNormGatedFunction(torch.autograd.Function):
+    @staticmethod
+    @ensure_contiguous
+    def forward(ctx, X, G, W, eps, offset=0.0):
+        """
+        X: (B, T, H) or (BxT, H) — input hidden states
+        G: (B, T, H) or (BxT, H) — gate tensor
+        W: (H,) — weight parameter
+        """
+        Y, X, G, RSTD, BLOCK_SIZE, num_warps = rms_norm_gated_forward(
+            X, G, W, eps, offset
+        )
+        ctx.offset = offset
+        ctx.BLOCK_SIZE = BLOCK_SIZE
+        ctx.num_warps = num_warps
+        ctx.save_for_backward(X, G, W, RSTD)
+        return Y
+
+    @staticmethod
+    @ensure_contiguous
+    def backward(ctx, dY):
+        X, G, W, RSTD = ctx.saved_tensors
+        dX, dG, dW = rms_norm_gated_backward(
+            dY, X, G, W, RSTD, ctx.offset, ctx.BLOCK_SIZE, ctx.num_warps
+        )
+        return dX, dG, dW, None, None
+
+
+class FusedRMSNormGated(torch.nn.Module):
+    """
+    Fused RMSNorm + SiLU Gate.
+
+    Computes: Y = W * RMSNorm(X) * silu(G)
+
+    Drop-in replacement for Qwen3_5RMSNormGated with matching
+    init signature: __init__(hidden_size, eps=1e-6, **kwargs)
+    and forward signature: forward(hidden_states, gate=None)
+    """
+
+    def __init__(self, hidden_size, eps=1e-6, offset=0.0, **kwargs):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+        self.offset = offset
+
+    def forward(self, hidden_states, gate=None):
+        if gate is None:
+            raise ValueError("FusedRMSNormGated requires a gate tensor")
+        if hidden_states.device.type != "cuda":
+            raise ValueError(
+                f"FusedRMSNormGated requires CUDA tensors, got device={hidden_states.device}"
+            )
+        return FusedRMSNormGatedFunction.apply(
+            hidden_states, gate, self.weight, self.variance_epsilon, self.offset
+        )
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"

src/axolotl/loaders/patch_manager.py

@@ -571,15 +571,6 @@ class PatchManager:
         LOG.info("Patching with xformers attention...")
         hijack_llama_attention()
 
-    def _patch_llama_sample_packing(self):
-        """Apply sample packing patches for LLaMA models."""
-        from axolotl.monkeypatch.llama_patch_multipack import (
-            hijack_llama_prepare_4d_mask,
-        )
-
-        LOG.info("Patching llama _prepare_4d_causal_attention_mask*...")
-        hijack_llama_prepare_4d_mask()
-
     def _patch_llama_derived_model(self):
         """Modify all llama derived models in one block."""
         if self.cfg.is_llama_derived_model and not (
@@ -591,8 +582,6 @@ class PatchManager:
                 self._patch_llama_flash_attention()
             elif self.cfg.xformers_attention:
                 self._patch_llama_xformers_attention()
-            elif self.cfg.sample_packing:
-                self._patch_llama_sample_packing()
             elif self.cfg.s2_attention:
                 raise NotImplementedError(
                     "Shifted-sparse attention not currently implemented without flash attention."

src/axolotl/loaders/tokenizer.py

@@ -221,6 +221,14 @@ def load_tokenizer(cfg: DictDefault) -> PreTrainedTokenizer:
             if getattr(tokenizer, attr_name) is None:
                 setattr(tokenizer, attr_name, "<|endoftext|>")
 
+    # Generic fallback: if tokenizer still has no pad_token, use eos_token
+    if tokenizer.pad_token is None and tokenizer.eos_token is not None:
+        tokenizer.pad_token = tokenizer.eos_token
+        LOG.warning(
+            "Tokenizer does not have a pad_token, falling back to eos_token: %s",
+            tokenizer.eos_token,
+        )
+
     additional_special_tokens = None
     if cfg.special_tokens:
         special_tokens = cfg.special_tokens.to_dict()

src/axolotl/monkeypatch/llama_expand_mask.py

@@ -1,24 +0,0 @@
-"""
-expands the binary attention mask per 3.2.2 of https://arxiv.org/pdf/2107.02027.pdf
-"""
-
-from typing import Optional
-
-import torch
-
-from axolotl.monkeypatch.utils import mask_2d_to_4d
-
-
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    masked_zero_one_mask = mask_2d_to_4d(mask, dtype, tgt_len)
-    inverted_mask = 1.0 - masked_zero_one_mask
-
-    return inverted_mask.masked_fill(
-        inverted_mask.to(torch.bool), torch.finfo(dtype).min
-    )
-
-
-def hijack_expand_mask():
-    import transformers
-
-    transformers.models.llama.modeling_llama._expand_mask = _expand_mask

src/axolotl/monkeypatch/llama_patch_multipack.py

@@ -1,26 +0,0 @@
-"""
-Patched LlamaAttention to use torch.nn.functional.scaled_dot_product_attention
-"""
-
-from axolotl.monkeypatch.utils import (
-    patched_prepare_4d_causal_attention_mask,
-    patched_prepare_4d_causal_attention_mask_for_sdpa,
-)
-
-
-def hijack_llama_prepare_4d_mask():
-    from transformers import modeling_attn_mask_utils
-    from transformers.models.llama import modeling_llama
-
-    modeling_llama._prepare_4d_causal_attention_mask_for_sdpa = (
-        patched_prepare_4d_causal_attention_mask_for_sdpa
-    )
-    modeling_attn_mask_utils._prepare_4d_causal_attention_mask_for_sdpa = (
-        patched_prepare_4d_causal_attention_mask_for_sdpa
-    )
-    modeling_llama._prepare_4d_causal_attention_mask = (
-        patched_prepare_4d_causal_attention_mask
-    )
-    modeling_attn_mask_utils._prepare_4d_causal_attention_mask = (
-        patched_prepare_4d_causal_attention_mask
-    )

src/axolotl/monkeypatch/lora_kernels.py

@@ -12,6 +12,7 @@ from torch import nn
 from transformers import AutoConfig
 
 from axolotl.kernels.lora import (
+    apply_lora_embedding,
     apply_lora_mlp_geglu,
     apply_lora_mlp_swiglu,
     apply_lora_o,
@@ -370,13 +371,13 @@ def apply_lora_kernel_patches(
         active_adapter = model.active_adapter
     lora_config = model.model.peft_config[active_adapter]
 
-    # Only patch if conditions are met
-    can_patch = lora_config.lora_dropout == 0 and lora_config.bias == "none"
-
-    if not can_patch:
-        LOG.warning("Cannot patch layers - requires no dropout and no bias")
-        LOG.warning("Please specify `lora_dropout: 0` in your axolotl config file")
-        return model
+    # Log what features are active
+    if lora_config.lora_dropout > 0:
+        LOG.info(f"LoRA kernels: dropout={lora_config.lora_dropout} enabled")
+    if lora_config.bias != "none":
+        LOG.info(f"LoRA kernels: bias={lora_config.bias} enabled")
+    if lora_config.use_dora:
+        LOG.info("LoRA kernels: DoRA enabled")
 
     # This needs to be reset after patching
     original_level = LOG.getEffectiveLevel()
@@ -419,44 +420,33 @@ def apply_lora_kernel_patches(
                     for linear_proj in ["q_proj", "k_proj", "v_proj"]
                 ]
                 can_patch_qkv = all(
-                    hasattr(module, "lora_A")
-                    and len(getattr(module, "lora_magnitude_vector", []) or []) == 0
-                    for module in layer_modules
+                    hasattr(module, "lora_A") for module in layer_modules
                 )
 
                 if can_patch_qkv:
-                    # Add optimized implementation
                     self_attn.apply_qkv = types.MethodType(apply_lora_qkv, self_attn)
                 else:
                     LOG.warning_once(
-                        "Cannot patch some attention QKV projections - requires LoRA "
-                        "adapters and no lora_magnitude_vector (DoRA)"
+                        "Cannot patch some attention QKV projections - requires LoRA adapters"
                     )
             if cfg.lora_o_kernel:
                 # Output patching
                 layer_modules = [
                     getattr(self_attn, linear_proj) for linear_proj in ["o_proj"]
                 ]
-                can_patch_o = all(
-                    hasattr(module, "lora_A")
-                    and len(getattr(module, "lora_magnitude_vector", []) or []) == 0
-                    for module in layer_modules
-                )
+                can_patch_o = all(hasattr(module, "lora_A") for module in layer_modules)
 
                 if can_patch_o:
                     self_attn.apply_o = types.MethodType(apply_lora_o, self_attn)
                 else:
                     LOG.warning_once(
-                        "Cannot patch some attention output projection - requires LoRA "
-                        "adapters and no lora_magnitude_vector (DoRA)"
+                        "Cannot patch some attention output projection - requires LoRA adapters"
                     )
         for gate_proj, up_proj, down_proj, mlp in find_mlp_in_layer(layer):
             if cfg.lora_mlp_kernel:
                 # MLP patching
                 can_patch_mlp = all(
-                    hasattr(proj, "lora_A")
-                    and len(getattr(proj, "lora_magnitude_vector", []) or []) == 0
-                    for proj in (gate_proj, up_proj, down_proj)
+                    hasattr(proj, "lora_A") for proj in (gate_proj, up_proj, down_proj)
                 )
 
                 if can_patch_mlp:
@@ -464,15 +454,50 @@ def apply_lora_kernel_patches(
                     layer.mlp.forward = types.MethodType(apply_fn, mlp)
                 else:
                     LOG.warning_once(
-                        "Cannot patch some MLP layers - requires LoRA adapters and no "
-                        "lora_magnitude_vector (DoRA)"
+                        "Cannot patch some MLP layers - requires LoRA adapters"
                     )
 
+    # Patch embedding layers (model-level, not per-layer)
+    if cfg.lora_embedding_kernel:
+        _patch_embedding_layers(model, cfg)
+
     LOG.setLevel(original_level)
 
     return model
 
 
+def _patch_embedding_layers(model: PeftModelForCausalLM, cfg: DictDefault):
+    """Patch embedding layers with fused LoRA kernel.
+
+    Handles both embed_tokens (nn.Embedding with lora_embedding_A/B) and
+    lm_head (nn.Linear with lora_A/B, used when tied embeddings are untied by PEFT).
+    """
+    pretrained_model = model.model
+    patched = 0
+
+    # Find embedding modules - check common locations
+    for attr_path in [
+        ("model", "embed_tokens"),
+        ("model", "language_model", "embed_tokens"),
+    ]:
+        parent = pretrained_model
+        for attr in attr_path:
+            parent = getattr(parent, attr, None)
+            if parent is None:
+                break
+        if parent is not None and hasattr(parent, "lora_embedding_A"):
+            LOG.info(f"Patching embedding layer: {'.'.join(attr_path)}")
+            parent.forward = types.MethodType(apply_lora_embedding, parent)
+            patched += 1
+
+    # lm_head with LoRA is a Linear layer - already handled by LoRA_O/LoRA_W kernels
+    # when included in target_modules. No special embedding handling needed since
+    # PEFT wraps it as a Linear (not Embedding) even for tied models.
+
+    if not patched:
+        LOG.debug("No embedding layers with LoRA found to patch")
+
+
 class FakeMLP(nn.Module):
     """
     placeholder MLP for triton patching

src/axolotl/monkeypatch/utils.py

@@ -3,15 +3,10 @@ Shared utils for the monkeypatches
 """
 
 import re
-from typing import Optional, Tuple
+from typing import Tuple
 
 import torch
 import torch.nn.functional as F
-from transformers.modeling_attn_mask_utils import (
-    _prepare_4d_causal_attention_mask,
-    _prepare_4d_causal_attention_mask_for_sdpa,
-)
-from transformers.utils import is_torch_bf16_gpu_available
 
 
 @torch.jit.script
@@ -170,65 +165,6 @@ def set_module_name(model, name, value):
     setattr(parent, child_name, value)
 
 
-def mask_2d_to_4d(
-    mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None
-):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    This expansion handles packed sequences so that sequences share the same attention mask integer value
-    when they attend to each other within that sequence.
-    This expansion transforms the mask to lower triangular form to prevent future peeking.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    mask = mask.unsqueeze(1).unsqueeze(2)
-    mask = mask.expand(bsz, 1, tgt_len, src_len)
-
-    # Create a binary mask from the original mask where zeros remain zeros and all other values are set to one
-    binary_mask = torch.where(
-        mask != 0,
-        torch.tensor(1, device=mask.device).to(dtype),
-        torch.tensor(0, device=mask.device).to(dtype),
-    )
-
-    # Create a block-diagonal mask.
-    # we multiply by the binary mask so that 0's in the original mask are correctly excluded
-    zero_one_mask = torch.eq(mask, mask.transpose(-1, -2)).int() * binary_mask
-
-    # Now let's create a lower triangular mask of ones that will zero out the upper triangular part
-    lower_triangular_ones = torch.tril(torch.ones((tgt_len, src_len), dtype=dtype)).to(
-        mask.device
-    )
-
-    # Use the lower triangular mask to zero out the upper triangular part of the zero_one_mask
-    masked_zero_one_mask = zero_one_mask * lower_triangular_ones
-
-    return masked_zero_one_mask
-
-
-def patched_prepare_4d_causal_attention_mask(
-    attention_mask: Optional[torch.Tensor],
-    *args,
-):
-    dtype = torch.bfloat16 if is_torch_bf16_gpu_available() else torch.float32
-    return _prepare_4d_causal_attention_mask(
-        mask_2d_to_4d(attention_mask, dtype=dtype),
-        *args,
-    )
-
-
-def patched_prepare_4d_causal_attention_mask_for_sdpa(
-    attention_mask: Optional[torch.Tensor],
-    *args,
-):
-    dtype = torch.bfloat16 if is_torch_bf16_gpu_available() else torch.float32
-    return _prepare_4d_causal_attention_mask_for_sdpa(
-        mask_2d_to_4d(attention_mask, dtype=dtype),
-        *args,
-    )
-
-
 def detab_code(code: str) -> Tuple[str, str]:
     try:
         spaces = re.match(r"([\s\t]{1,})", code).group(0)

src/axolotl/prompt_strategies/_synthetic.py

@@ -0,0 +1,96 @@
+"""
+Synthetic dataset generator for benchmarking and testing.
+
+Generates datasets with configurable sequence length, dataset size, and token ID ranges.
+Useful for benchmarking memory usage and speed by sequence length, and for validating
+weighted dataset mixes.
+
+YAML configuration example:
+
+    datasets:
+      - path: synthetic
+        type: _synthetic
+        length: 1000
+        sequence_length: 2048
+        min_input_id: 100
+        max_input_id: 32000
+        seed: 42
+"""
+
+from typing import Any, Dict, Optional
+
+import numpy as np
+from datasets import Dataset
+
+from axolotl.prompt_tokenizers import DatasetWrappingStrategy
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
+
+class SyntheticDatasetStrategy(DatasetWrappingStrategy):
+    """Strategy that generates synthetic tokenized data, ignoring the source dataset."""
+
+    def __init__(
+        self,
+        sequence_length: int = 2048,
+        length: int = 1000,
+        min_input_id: int = 100,
+        max_input_id: int = 32000,
+        seed: Optional[int] = None,
+    ):
+        self.sequence_length = sequence_length
+        self.length = length
+        self.min_input_id = min_input_id
+        self.max_input_id = max_input_id
+        self.seed = seed
+
+    def wrap_dataset(
+        self,
+        dataset,
+        process_count: int | None = None,
+        keep_in_memory: bool | None = False,
+        **kwargs,
+    ) -> Dataset:
+        LOG.info(
+            f"Generating synthetic dataset: {self.length} samples, "
+            f"sequence_length={self.sequence_length}, "
+            f"input_id_range=[{self.min_input_id}, {self.max_input_id})"
+        )
+
+        rng = np.random.default_rng(self.seed)
+        input_ids = rng.integers(
+            low=self.min_input_id,
+            high=self.max_input_id,
+            size=(self.length, self.sequence_length),
+        ).tolist()
+
+        attention_mask = [[1] * self.sequence_length] * self.length
+        # labels == input_ids means we train on all tokens
+        labels = [row[:] for row in input_ids]
+
+        return Dataset.from_dict(
+            {
+                "input_ids": input_ids,
+                "attention_mask": attention_mask,
+                "labels": labels,
+            }
+        )
+
+
+def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+    ds_cfg = ds_cfg or {}
+
+    sequence_length = ds_cfg.get("sequence_length", cfg.sequence_len)
+    length = ds_cfg.get("length", 1000)
+    min_input_id = ds_cfg.get("min_input_id", 100)
+    max_input_id = ds_cfg.get("max_input_id", tokenizer.vocab_size)
+    seed = ds_cfg.get("seed", None)
+
+    return SyntheticDatasetStrategy(
+        sequence_length=sequence_length,
+        length=length,
+        min_input_id=min_input_id,
+        max_input_id=max_input_id,
+        seed=seed,
+    )

src/axolotl/train.py

@@ -9,7 +9,6 @@ import os
 import shutil
 import signal
 import sys
-import typing
 import weakref
 from collections import OrderedDict
 from contextlib import ExitStack
@@ -42,9 +41,6 @@ from axolotl.utils.schemas.enums import RLType
 from axolotl.utils.train import determine_last_checkpoint
 from axolotl.utils.trainer import setup_trainer
 
-if typing.TYPE_CHECKING:
-    from axolotl.core.builders import HFCausalTrainerBuilder, HFRLTrainerBuilder
-
 LOG = get_logger(__name__)
 
 TELEMETRY_MANAGER = TelemetryManager.get_instance()
@@ -82,7 +78,7 @@ def setup_model_and_tokenizer(
 
     model_loader = ModelLoader(cfg, tokenizer, processor=processor)
     model, peft_config = model_loader.load()
-    if model.generation_config is not None:
+    if getattr(model, "generation_config", None) is not None:
         model.generation_config.do_sample = True
 
     model_properties = model.config.to_dict()
@@ -487,7 +483,7 @@ def handle_untrained_tokens_fix(
 def setup_model_and_trainer(
     cfg: DictDefault, dataset_meta: TrainDatasetMeta
 ) -> tuple[
-    "HFRLTrainerBuilder" | "HFCausalTrainerBuilder",
+    Trainer,
     PeftModel | PreTrainedModel,
     PreTrainedTokenizer,
     PeftConfig | None,
@@ -554,6 +550,36 @@ def setup_model_and_trainer(
     )
 
 
+def _is_tui_enabled(cfg: DictDefault) -> bool:
+    """Check if TUI is enabled via config or environment variable."""
+    if os.environ.get("AXOLOTL_TUI", "").lower() in ("1", "true", "yes"):
+        return True
+    tui = cfg.get("tui")
+    if tui is None:
+        return False
+    if isinstance(tui, bool):
+        return tui
+    if isinstance(tui, dict):
+        return tui.get("enabled", False)
+    if hasattr(tui, "enabled"):
+        return tui.enabled
+    return False
+
+
+def _get_tui_config(cfg: DictDefault) -> dict:
+    """Extract TUI config dict from cfg."""
+    tui = cfg.get("tui")
+    if tui is None or isinstance(tui, bool):
+        return {"enabled": True}
+    if isinstance(tui, dict):
+        return {**tui, "enabled": True}
+    if hasattr(tui, "model_dump"):
+        d = tui.model_dump()
+        d["enabled"] = True
+        return d
+    return {"enabled": True}
+
+
 @send_errors
 def train(
     cfg: DictDefault, dataset_meta: TrainDatasetMeta
@@ -577,6 +603,37 @@ def train(
         processor,
     ) = setup_model_and_trainer(cfg, dataset_meta)
 
+    # Register TUI callback if enabled and rank 0
+    tui_enabled = _is_tui_enabled(cfg)
+    if tui_enabled and cfg.local_rank == 0:
+        from axolotl.tui import AxolotlTUICallback
+        from axolotl.tui.config import TUIConfig
+
+        tui_config = _get_tui_config(cfg)
+        tui_config_obj = (
+            TUIConfig(**tui_config) if isinstance(tui_config, dict) else tui_config
+        )
+
+        # Reuse the early-started renderer if available (started in do_train)
+        early_renderer = getattr(cfg, "_tui_renderer", None)
+        early_queue = getattr(cfg, "_tui_queue", None)
+
+        tui_callback = AxolotlTUICallback(config=tui_config_obj)
+        if early_renderer is not None and early_queue is not None:
+            # Reuse the already-running renderer and queue
+            tui_callback._renderer = early_renderer
+            tui_callback._queue = early_queue
+            tui_callback._renderer_started_early = True
+        trainer.add_callback(tui_callback)
+
+        # Stash model info so on_train_begin can emit a single unified run_info event
+        tui_callback._pending_run_info = {
+            "model_name": cfg.base_model or "",
+            "training_mode": str(cfg.rl) if cfg.rl else "sft",
+            "world_size": int(os.environ.get("WORLD_SIZE", 1)),
+        }
+        LOG.info("TUI dashboard enabled")
+
     # Handle untrained tokens if configured
     train_dataset = dataset_meta.train_dataset
     handle_untrained_tokens_fix(cfg, model, tokenizer, train_dataset)

src/axolotl/tui/__init__.py

@@ -0,0 +1,17 @@
+"""Axolotl Training TUI — rich-based terminal dashboard for monitoring training runs."""
+
+from axolotl.tui.callback import AxolotlTUICallback
+from axolotl.tui.config import TUIConfig
+from axolotl.tui.io_capture import LineParser, register_parser
+from axolotl.tui.panels import BasePanel, register_panel
+from axolotl.tui.state import TUIState
+
+__all__ = [
+    "AxolotlTUICallback",
+    "BasePanel",
+    "LineParser",
+    "TUIConfig",
+    "TUIState",
+    "register_panel",
+    "register_parser",
+]

src/axolotl/tui/callback.py

@@ -0,0 +1,142 @@
+"""AxolotlTUICallback — HF TrainerCallback that feeds metrics to the TUI."""
+
+from __future__ import annotations
+
+import logging
+import queue
+
+from transformers.trainer_callback import TrainerCallback
+
+from axolotl.tui.config import TUIConfig
+from axolotl.tui.renderer import TUIRenderer
+
+
+class _TUILogHandler(logging.Handler):
+    """Logging handler that pushes log records into the TUI metric queue."""
+
+    _LEVEL_MAP = {
+        logging.DEBUG: "debug",
+        logging.INFO: "info",
+        logging.WARNING: "warning",
+        logging.ERROR: "error",
+        logging.CRITICAL: "error",
+    }
+
+    def __init__(self, metric_queue: queue.Queue, min_level: str = "info"):
+        super().__init__()
+        level_name = min_level.upper()
+        self.setLevel(getattr(logging, level_name, logging.INFO))
+        self._queue = metric_queue
+
+    def emit(self, record: logging.LogRecord) -> None:
+        try:
+            level = self._LEVEL_MAP.get(record.levelno, "info")
+            msg = self.format(record)
+            self._queue.put_nowait(
+                {
+                    "type": "log_line",
+                    "level": level,
+                    "message": msg,
+                }
+            )
+        except queue.Full:
+            pass
+        except Exception:
+            self.handleError(record)
+
+
+class AxolotlTUICallback(TrainerCallback):
+    """Pushes training metrics into a queue for the TUI renderer.
+
+    The callback never blocks on the render thread. The queue is bounded
+    (maxsize=512) with put_nowait; overflow is silently dropped.
+    """
+
+    def __init__(self, config: TUIConfig):
+        self._config = config
+        self._queue: queue.Queue = queue.Queue(maxsize=4096)
+        self._renderer = TUIRenderer(config=config, metric_queue=self._queue)
+        self._log_handler: _TUILogHandler | None = None
+        self._renderer_started_early: bool = False
+        self._pending_run_info: dict | None = None
+
+    def _put(self, event: dict) -> None:
+        try:
+            self._queue.put_nowait(event)
+        except queue.Full:
+            pass
+
+    def on_train_begin(self, args, state, control, model=None, **kwargs):
+        # Send a single unified run_info event with all fields
+        run_info = {
+            "type": "run_info",
+            "run_name": getattr(args, "run_name", "") or "",
+            "total_steps": state.max_steps,
+            "total_epochs": float(args.num_train_epochs)
+            if args.num_train_epochs
+            else 1.0,
+        }
+        # Merge in model_name/training_mode/world_size if stashed by train.py
+        if self._pending_run_info:
+            run_info.update(self._pending_run_info)
+            self._pending_run_info = None
+        self._put(run_info)
+
+        if not self._renderer_started_early:
+            # Attach a logging handler to feed log messages into the events panel
+            self._log_handler = _TUILogHandler(
+                self._queue, min_level=self._config.log_level
+            )
+            self._log_handler.setFormatter(logging.Formatter("[%(name)s] %(message)s"))
+            # Attach to both root and axolotl loggers (axolotl has propagate=False)
+            logging.getLogger().addHandler(self._log_handler)
+            logging.getLogger("axolotl").addHandler(self._log_handler)
+
+            # Start the renderer background thread
+            self._renderer.start()
+
+    def on_log(self, args, state, control, logs=None, **kwargs):
+        if logs is None:
+            return
+
+        # Filter out non-numeric keys and internal keys
+        filtered = {}
+        for key, value in logs.items():
+            if key.startswith("_"):
+                continue
+            if isinstance(value, (int, float)):
+                filtered[key] = value
+            elif isinstance(value, str):
+                # HF Trainer sometimes passes string-encoded numbers
+                try:
+                    filtered[key] = float(value)
+                except (ValueError, TypeError):
+                    pass
+
+        if filtered:
+            self._put({"type": "metrics", "logs": filtered})
+
+    def on_step_end(self, args, state, control, **kwargs):
+        self._put(
+            {
+                "type": "step",
+                "step": state.global_step,
+                "total_steps": state.max_steps,
+                "epoch": state.epoch if state.epoch else 0,
+            }
+        )
+
+    def on_prediction_step(self, args, state, control, **kwargs):
+        pass
+
+    def on_train_end(self, args, state, control, **kwargs):
+        self._put({"type": "done"})
+        # If renderer was started early, do_train's finally block handles stop
+        if not self._renderer_started_early:
+            self._renderer.stop()
+
+        # Remove the logging handler (only if we added it)
+        if self._log_handler:
+            logging.getLogger().removeHandler(self._log_handler)
+            logging.getLogger("axolotl").removeHandler(self._log_handler)
+            self._log_handler = None

src/axolotl/tui/config.py

@@ -0,0 +1,38 @@
+"""TUI configuration — Pydantic model for TUI settings."""
+
+from __future__ import annotations
+
+from pydantic import BaseModel, Field
+
+
+class TUIConfig(BaseModel):
+    """Configuration for the Axolotl Training TUI dashboard."""
+
+    enabled: bool = Field(
+        default=False,
+        json_schema_extra={"description": "Enable the TUI dashboard"},
+    )
+    refresh_rate: int = Field(
+        default=4,
+        json_schema_extra={"description": "Renders per second"},
+    )
+    log_level: str = Field(
+        default="debug",
+        json_schema_extra={"description": "Minimum log level shown in events panel"},
+    )
+    panels: list[str] = Field(
+        default_factory=lambda: ["progress", "training", "hardware", "events", "debug"],
+        json_schema_extra={"description": "Ordered list of panels to display"},
+    )
+    hardware_poll_interval: int = Field(
+        default=2,
+        json_schema_extra={"description": "Seconds between pynvml GPU queries"},
+    )
+    stdout_log_path: str = Field(
+        default="axolotl_stdout.log",
+        json_schema_extra={"description": "File path for captured stdout/stderr log"},
+    )
+    parser_plugins: list[str] = Field(
+        default_factory=list,
+        json_schema_extra={"description": "List of extra parser classes to load"},
+    )

src/axolotl/tui/gpu.py

@@ -0,0 +1,72 @@
+"""GPU polling wrapper around pynvml with graceful fallback."""
+
+from __future__ import annotations
+
+import logging
+
+from axolotl.tui.state import GPUStats
+
+LOG = logging.getLogger(__name__)
+
+_nvml_available = False
+try:
+    import pynvml
+
+    pynvml.nvmlInit()
+    _nvml_available = True
+except Exception:
+    LOG.debug("pynvml unavailable — GPU stats will not be shown")
+
+
+class GPUPoller:
+    """Polls local GPU stats via pynvml. Falls back gracefully if unavailable."""
+
+    def __init__(self):
+        self._device_count = 0
+        if _nvml_available:
+            try:
+                self._device_count = pynvml.nvmlDeviceGetCount()
+            except Exception:
+                self._device_count = 0
+
+    @property
+    def available(self) -> bool:
+        return _nvml_available and self._device_count > 0
+
+    def poll(self) -> list[GPUStats]:
+        if not self.available:
+            return []
+
+        stats = []
+        for i in range(self._device_count):
+            try:
+                handle = pynvml.nvmlDeviceGetHandleByIndex(i)
+                name = pynvml.nvmlDeviceGetName(handle)
+                if isinstance(name, bytes):
+                    name = name.decode("utf-8")
+
+                util = pynvml.nvmlDeviceGetUtilizationRates(handle)
+                mem = pynvml.nvmlDeviceGetMemoryInfo(handle)
+                temp = pynvml.nvmlDeviceGetTemperature(
+                    handle, pynvml.NVML_TEMPERATURE_GPU
+                )
+
+                try:
+                    power = pynvml.nvmlDeviceGetPowerUsage(handle) / 1000.0
+                except Exception:
+                    power = None
+
+                stats.append(
+                    GPUStats(
+                        id=i,
+                        name=name,
+                        util_pct=util.gpu,
+                        vram_used_gb=mem.used / (1024**3),
+                        vram_total_gb=mem.total / (1024**3),
+                        temp_c=temp,
+                        power_w=power,
+                    )
+                )
+            except Exception:
+                LOG.debug("Error polling GPU device %d", i, exc_info=True)
+        return stats

src/axolotl/tui/io_capture.py

@@ -0,0 +1,196 @@
+"""I/O capture: OS-level stdout/stderr redirect, line parser chain, and parser registry."""
+
+from __future__ import annotations
+
+import logging
+import os
+import queue
+import sys
+import threading
+from abc import ABC, abstractmethod
+from datetime import datetime
+from typing import IO
+
+# ---------------------------------------------------------------------------
+# Parser registry
+# ---------------------------------------------------------------------------
+
+_parser_registry: list[type[LineParser]] = []
+
+
+def register_parser(cls: type[LineParser]) -> type[LineParser]:
+    """Decorator to register a LineParser subclass."""
+    if cls not in _parser_registry:
+        _parser_registry.append(cls)
+    return cls
+
+
+def get_registered_parsers() -> list[type[LineParser]]:
+    return list(_parser_registry)
+
+
+# ---------------------------------------------------------------------------
+# Base LineParser
+# ---------------------------------------------------------------------------
+
+
+class LineParser(ABC):
+    """Base class for stdout/stderr line parsers."""
+
+    priority: int = 50
+    name: str = ""
+
+    @abstractmethod
+    def parse(self, line: str, source: str) -> list[dict]:
+        """Parse a single captured line.
+
+        Args:
+            line:   one line of captured output, trailing newline stripped.
+            source: "stdout" or "stderr".
+
+        Returns:
+            List of event dicts to push onto the metric queue.
+            Return [] if this line is not relevant.
+        """
+        ...
+
+
+# ---------------------------------------------------------------------------
+# ParserChain
+# ---------------------------------------------------------------------------
+
+
+class ParserChain:
+    def __init__(self):
+        self._parsers: list[LineParser] = []
+
+    def register(self, parser: LineParser) -> None:
+        self._parsers.append(parser)
+        self._parsers.sort(key=lambda p: p.priority)
+
+    def parse(self, line: str, source: str = "stdout") -> list[dict]:
+        events: list[dict] = []
+        for parser in self._parsers:
+            events.extend(parser.parse(line, source))
+        return events
+
+
+# ---------------------------------------------------------------------------
+# IOCapture — OS-level fd redirect to pipe
+# ---------------------------------------------------------------------------
+
+
+class IOCapture:
+    """Redirects fd 1 and fd 2 into an OS pipe, drains via a reader thread,
+    passes lines through a ParserChain, and tees to a log file."""
+
+    def __init__(
+        self, log_path: str, parser_chain: ParserChain, metric_queue: queue.Queue
+    ):
+        self._parser_chain = parser_chain
+        self._queue = metric_queue
+        self._log_path = log_path
+        self._log_file: IO[str] | None = None
+        self._thread: threading.Thread | None = None
+        self._read_fd: int | None = None
+        self._write_fd: int | None = None
+        self._saved_stdout_fd: int | None = None
+        self._saved_stderr_fd: int | None = None
+
+    def start(self) -> None:
+        # Write run-start separator
+        self._log_file = open(self._log_path, "a", buffering=1)  # noqa: SIM115
+        self._log_file.write(
+            f"\n=== axolotl run started {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ===\n"
+        )
+        self._log_file.flush()
+
+        # OS-level pipe
+        self._read_fd, self._write_fd = os.pipe()
+
+        # Save originals
+        self._saved_stdout_fd = os.dup(1)
+        self._saved_stderr_fd = os.dup(2)
+
+        # Redirect both stdout and stderr into the write end
+        os.dup2(self._write_fd, 1)
+        os.dup2(self._write_fd, 2)
+        os.close(self._write_fd)  # write end now held by fds 1 and 2
+
+        # Also redirect Python-level handles
+        sys.stdout = open(1, "w", buffering=1, closefd=False)  # noqa: SIM115
+        sys.stderr = open(2, "w", buffering=1, closefd=False)  # noqa: SIM115
+
+        # Drain thread
+        self._thread = threading.Thread(target=self._drain, daemon=True)
+        self._thread.start()
+
+    def stop(self) -> None:
+        # Restore fds — closes the write end, causing reader to see EOF
+        if self._saved_stdout_fd is not None and self._saved_stderr_fd is not None:
+            sys.stdout = sys.__stdout__
+            sys.stderr = sys.__stderr__
+            os.dup2(self._saved_stdout_fd, 1)
+            os.dup2(self._saved_stderr_fd, 2)
+            os.close(self._saved_stdout_fd)
+            os.close(self._saved_stderr_fd)
+            self._saved_stdout_fd = None
+            self._saved_stderr_fd = None
+
+        if self._thread is not None:
+            self._thread.join(timeout=2.0)
+            if self._thread.is_alive():
+                logging.getLogger(__name__).warning(
+                    "IO capture thread did not exit after 2s"
+                )
+            self._thread = None
+
+        if self._log_file is not None:
+            self._log_file.close()
+            self._log_file = None
+
+    def _drain(self) -> None:
+        # Read raw bytes and split on both \n and \r to handle tqdm progress bars
+        # which use \r for in-place updates without \n
+        assert self._read_fd is not None, "_drain called before start()"
+        with os.fdopen(self._read_fd, "rb") as pipe:
+            buf = b""
+            while True:
+                chunk = pipe.read(4096)
+                if not chunk:
+                    # EOF — process remaining buffer
+                    if buf:
+                        self._process_line(buf.decode("utf-8", errors="replace"))
+                    break
+                buf += chunk
+                # Split on \n or \r
+                while b"\n" in buf or b"\r" in buf:
+                    # Find the earliest delimiter
+                    idx_n = buf.find(b"\n")
+                    idx_r = buf.find(b"\r")
+                    if idx_n == -1:
+                        idx = idx_r
+                    elif idx_r == -1:
+                        idx = idx_n
+                    else:
+                        idx = min(idx_n, idx_r)
+                    line = buf[:idx].decode("utf-8", errors="replace")
+                    buf = buf[idx + 1 :]
+                    # Handle \r\n as single delimiter
+                    if buf.startswith(b"\n"):
+                        buf = buf[1:]
+                    if line:
+                        self._process_line(line)
+
+    def _process_line(self, line: str) -> None:
+        line = line.rstrip()
+        if not line:
+            return
+        if self._log_file:
+            self._log_file.write(line + "\n")
+            self._log_file.flush()
+        for event in self._parser_chain.parse(line):
+            try:
+                self._queue.put_nowait(event)
+            except queue.Full:
+                pass

src/axolotl/tui/panels/__init__.py

@@ -0,0 +1,63 @@
+"""Panel registry and base class for TUI panels."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+
+from rich.console import RenderableType
+
+from axolotl.tui.state import TUIState
+
+# ---------------------------------------------------------------------------
+# Panel registry
+# ---------------------------------------------------------------------------
+
+_panel_registry: dict[str, type[BasePanel]] = {}
+
+
+def register_panel(position: str = "bottom", weight: int = 50):
+    """Decorator to register a panel class with position and weight."""
+
+    def decorator(cls: type[BasePanel]) -> type[BasePanel]:
+        cls.position = position
+        cls.weight = weight
+        _panel_registry[cls.name] = cls
+        return cls
+
+    return decorator
+
+
+def get_registered_panels() -> dict[str, type[BasePanel]]:
+    return dict(_panel_registry)
+
+
+# ---------------------------------------------------------------------------
+# BasePanel
+# ---------------------------------------------------------------------------
+
+
+class BasePanel(ABC):
+    name: str = ""
+    position: str = "bottom"
+    weight: int = 50
+    min_height: int = 4
+    max_height: int | None = None
+    modes: list[str] = ["*"]
+
+    @abstractmethod
+    def render(self, state: TUIState) -> RenderableType:
+        """Return a rich renderable. Called every tick."""
+        ...
+
+    def on_event(self, event: dict) -> None:  # noqa: B027
+        """Optional: react to raw metric events before state is merged."""
+        pass
+
+
+# Auto-import built-in panels to trigger registration
+from axolotl.tui.panels.completions import CompletionsPanel  # noqa: E402, F401
+from axolotl.tui.panels.debug import DebugPanel  # noqa: E402, F401
+from axolotl.tui.panels.events import EventsPanel  # noqa: E402, F401
+from axolotl.tui.panels.hardware import HardwarePanel  # noqa: E402, F401
+from axolotl.tui.panels.progress import ProgressPanel  # noqa: E402, F401
+from axolotl.tui.panels.training import TrainingPanel  # noqa: E402, F401

src/axolotl/tui/panels/completions.py

@@ -0,0 +1,61 @@
+"""CompletionsPanel — shows recent RL/log_completions samples."""
+
+from __future__ import annotations
+
+from rich.console import RenderableType
+from rich.panel import Panel
+from rich.table import Table
+from rich.text import Text
+
+from axolotl.tui.panels import BasePanel, register_panel
+from axolotl.tui.state import TUIState
+
+
+def _truncate(s: str, maxlen: int = 60) -> str:
+    return s[:maxlen] + "…" if len(s) > maxlen else s
+
+
+@register_panel(position="bottom", weight=20)
+class CompletionsPanel(BasePanel):
+    name = "completions"
+    min_height = 6
+    modes = ["grpo", "dpo"]
+
+    def render(self, state: TUIState) -> RenderableType:
+        if "*" not in self.modes and state.training_mode not in self.modes:
+            return Text("")
+
+        if not state.completions:
+            return Panel(
+                Text("No completions yet...", style="dim"),
+                title="Completions",
+                border_style="magenta",
+            )
+
+        table = Table(
+            show_header=True,
+            header_style="bold",
+            expand=True,
+            box=None,
+            pad_edge=False,
+        )
+        table.add_column("step", justify="right", width=6)
+        table.add_column("prompt", no_wrap=False, max_width=40)
+        table.add_column("completion", no_wrap=False, max_width=40)
+        table.add_column("reward", justify="right", width=8)
+        table.add_column("adv", justify="right", width=8)
+
+        for sample in list(state.completions)[-5:]:
+            reward_str = f"{sample.reward:.2f}" if sample.reward is not None else "--"
+            adv_str = (
+                f"{sample.advantage:+.2f}" if sample.advantage is not None else "--"
+            )
+            table.add_row(
+                str(sample.step),
+                _truncate(sample.prompt),
+                _truncate(sample.completion),
+                reward_str,
+                adv_str,
+            )
+
+        return Panel(table, title="Completions", border_style="magenta")

src/axolotl/tui/panels/debug.py

@@ -0,0 +1,34 @@
+"""DebugPanel — scrolling log of debug-level messages, separate from main events."""
+
+from __future__ import annotations
+
+from rich.console import RenderableType
+from rich.panel import Panel
+from rich.text import Text
+
+from axolotl.tui.panels import BasePanel, register_panel
+from axolotl.tui.state import TUIState
+
+
+@register_panel(position="bottom", weight=30)
+class DebugPanel(BasePanel):
+    name = "debug"
+    min_height = 6
+    max_height = 10
+
+    def render(self, state: TUIState) -> RenderableType:
+        lines = Text()
+        # Show last 8 debug-level log lines
+        debug_lines = [
+            log_entry for log_entry in state.log_lines if log_entry.level == "debug"
+        ][-8:]
+        for log_line in debug_lines:
+            ts = log_line.timestamp.strftime("%H:%M:%S")
+            lines.append(f"[{ts}] ", style="dim")
+            lines.append(log_line.message[:200], style="dim")
+            lines.append("\n")
+
+        if not debug_lines:
+            lines = Text("No debug messages yet...", style="dim")
+
+        return Panel(lines, title="Debug", border_style="dim")

src/axolotl/tui/panels/events.py

@@ -0,0 +1,45 @@
+"""EventsPanel — scrolling log of recent events, color-coded by level."""
+
+from __future__ import annotations
+
+from rich.console import RenderableType
+from rich.panel import Panel
+from rich.text import Text
+
+from axolotl.tui.panels import BasePanel, register_panel
+from axolotl.tui.state import TUIState
+
+_LEVEL_STYLES = {
+    "debug": "dim",
+    "info": "",
+    "warning": "yellow",
+    "error": "red bold",
+    "critical": "red bold",
+}
+
+
+@register_panel(position="bottom", weight=10)
+class EventsPanel(BasePanel):
+    name = "events"
+    min_height = 8
+    max_height = 20
+
+    def render(self, state: TUIState) -> RenderableType:
+        lines = Text()
+        # Show last 15 non-debug log lines (debug goes to DebugPanel)
+        recent = [
+            log_entry for log_entry in state.log_lines if log_entry.level != "debug"
+        ][-15:]
+        for log_line in recent:
+            ts = log_line.timestamp.strftime("%H:%M:%S")
+            level = log_line.level.upper()
+            style = _LEVEL_STYLES.get(log_line.level, "")
+            lines.append(f"[{ts}] ", style="dim")
+            lines.append(f"[{level}] ", style=style or "")
+            lines.append(log_line.message[:200], style=style or "")
+            lines.append("\n")
+
+        if not recent:
+            lines = Text("No events yet...", style="dim")
+
+        return Panel(lines, title="Events", border_style="yellow")

src/axolotl/tui/panels/hardware.py

@@ -0,0 +1,80 @@
+"""HardwarePanel — per-GPU stats via pynvml."""
+
+from __future__ import annotations
+
+from rich.console import RenderableType
+from rich.panel import Panel
+from rich.table import Table
+from rich.text import Text
+
+from axolotl.tui.panels import BasePanel, register_panel
+from axolotl.tui.state import TUIState
+
+_BAR_FULL = "█"
+_BAR_EMPTY = "░"
+
+
+def _util_bar(pct: float, width: int = 6) -> Text:
+    filled = int(pct / 100 * width)
+    bar = _BAR_FULL * filled + _BAR_EMPTY * (width - filled)
+    color = "green" if pct < 70 else ("yellow" if pct < 90 else "red")
+    return Text.assemble((bar, color), f" {pct:3.0f}%")
+
+
+@register_panel(position="right", weight=10)
+class HardwarePanel(BasePanel):
+    name = "hardware"
+    min_height = 6
+
+    def render(self, state: TUIState) -> RenderableType:
+        if not state.gpus:
+            return Panel(
+                Text("GPU stats unavailable", style="dim"),
+                title="Hardware",
+                border_style="green",
+            )
+
+        table = Table(
+            show_header=True,
+            header_style="bold",
+            expand=True,
+            box=None,
+            pad_edge=False,
+        )
+        table.add_column("id", justify="right", width=3)
+        table.add_column("util", no_wrap=True)
+        table.add_column("vram", no_wrap=True)
+        table.add_column("°C", justify="right", width=4)
+        table.add_column("W", justify="right", width=5)
+
+        total_vram_used = 0.0
+        total_vram_total = 0.0
+        total_util = 0.0
+
+        for gpu in state.gpus:
+            total_vram_used += gpu.vram_used_gb
+            total_vram_total += gpu.vram_total_gb
+            total_util += gpu.util_pct
+
+            power_str = f"{gpu.power_w:.0f}" if gpu.power_w is not None else "--"
+            table.add_row(
+                str(gpu.id),
+                _util_bar(gpu.util_pct),
+                f"{gpu.vram_used_gb:.1f}/{gpu.vram_total_gb:.1f} GB",
+                str(gpu.temp_c),
+                power_str,
+            )
+
+        # Footer with aggregates
+        n = len(state.gpus)
+        if n > 1:
+            avg_util = total_util / n
+            table.add_row(
+                "Σ",
+                Text(f"avg {avg_util:.0f}%", style="dim"),
+                Text(f"{total_vram_used:.1f}/{total_vram_total:.1f} GB", style="dim"),
+                "",
+                "",
+            )
+
+        return Panel(table, title="Hardware", border_style="green")

src/axolotl/tui/panels/progress.py

@@ -0,0 +1,73 @@
+"""ProgressPanel — top-bar progress display with step count, elapsed, ETA."""
+
+from __future__ import annotations
+
+from rich.console import RenderableType
+from rich.progress import BarColumn, Progress, TextColumn
+from rich.table import Table
+from rich.text import Text
+
+from axolotl.tui.panels import BasePanel, register_panel
+from axolotl.tui.state import TUIState
+
+
+def _fmt_time(seconds: float | None) -> str:
+    if seconds is None or seconds < 0:
+        return "--:--:--"
+    h = int(seconds) // 3600
+    m = (int(seconds) % 3600) // 60
+    s = int(seconds) % 60
+    return f"{h}:{m:02d}:{s:02d}"
+
+
+def _fmt_eta(seconds: float | None) -> str:
+    if seconds is None or seconds < 0:
+        return "eta --"
+    h = int(seconds) // 3600
+    m = (int(seconds) % 3600) // 60
+    if h > 0:
+        return f"eta {h}h{m:02d}m"
+    return f"eta {m}m{int(seconds) % 60:02d}s"
+
+
+@register_panel(position="top", weight=10)
+class ProgressPanel(BasePanel):
+    name = "progress"
+    min_height = 3
+    max_height = 3
+
+    def render(self, state: TUIState) -> RenderableType:
+        pct = (
+            (state.current_step / state.total_steps * 100)
+            if state.total_steps > 0
+            else 0
+        )
+
+        # Header line
+        mode_upper = state.training_mode.upper() if state.training_mode else "SFT"
+        model_short = state.model_name.split("/")[-1] if state.model_name else "model"
+        header = Text.assemble(
+            ("● ", "bold green"),
+            ("AXOLOTL", "bold cyan"),
+            f"  {mode_upper} · {model_short}   ",
+            (
+                f"{state.current_step} / {state.total_steps}",
+                "bold",
+            ),
+            f"  ·  {_fmt_time(state.elapsed_seconds)} elapsed  ·  {_fmt_eta(state.eta_seconds)}  ·  {pct:.1f}%",
+        )
+
+        # Progress bar
+        progress = Progress(
+            TextColumn(""),
+            BarColumn(bar_width=None),
+            TextColumn("{task.percentage:>3.0f}%"),
+            expand=True,
+        )
+        task = progress.add_task("", total=state.total_steps or 1)
+        progress.update(task, completed=state.current_step)
+
+        table = Table.grid(expand=True)
+        table.add_row(header)
+        table.add_row(progress)
+        return table

src/axolotl/tui/panels/training.py

@@ -0,0 +1,97 @@
+"""TrainingPanel — live scalar metrics table with loss sparkline."""
+
+from __future__ import annotations
+
+from rich.console import RenderableType
+from rich.panel import Panel
+from rich.table import Table
+from rich.text import Text
+
+from axolotl.tui.panels import BasePanel, register_panel
+from axolotl.tui.state import TUIState
+
+# Braille sparkline characters (8 levels)
+_SPARK_CHARS = "▁▂▃▄▅▆▇█"
+
+
+def _sparkline(values: list[float] | None, width: int = 20) -> str:
+    if not values or len(values) < 2:
+        return ""
+    vals = list(values)[-width:]
+    lo, hi = min(vals), max(vals)
+    rng = hi - lo if hi != lo else 1.0
+    return "".join(_SPARK_CHARS[min(int((v - lo) / rng * 7), 7)] for v in vals)
+
+
+# Known key ordering and formatting
+_KNOWN_KEYS: list[tuple[str, str, str]] = [
+    ("loss", "loss", ".4f"),
+    ("grad_norm", "grad norm", ".3f"),
+    ("learning_rate", "lr", ".2e"),
+    ("tokens_per_second", "tok/s", ".1f"),
+    ("samples_per_second", "samples/s", ".1f"),
+    ("mfu", "MFU", ".1f"),
+    # RL-specific
+    ("rewards_mean", "rewards/mean", ".4f"),
+    ("rewards_std", "rewards/std", ".4f"),
+    ("kl_divergence", "KL", ".4f"),
+    ("clip_ratio", "clip ratio", ".3f"),
+    ("queue_size", "queue", "d"),
+]
+
+
+@register_panel(position="left", weight=10)
+class TrainingPanel(BasePanel):
+    name = "training"
+    min_height = 8
+
+    def render(self, state: TUIState) -> RenderableType:
+        table = Table(
+            show_header=True,
+            header_style="bold",
+            expand=True,
+            box=None,
+            pad_edge=False,
+        )
+        table.add_column("metric", style="cyan", no_wrap=True)
+        table.add_column("value", justify="right")
+        table.add_column("trend", justify="left", no_wrap=True)
+
+        for attr, label, fmt in _KNOWN_KEYS:
+            val = getattr(state, attr, None)
+            if val is None:
+                # Also check extra dict
+                val = state.extra.get(attr)
+            if val is None:
+                continue
+
+            try:
+                formatted = f"{val:{fmt}}"
+            except (ValueError, TypeError):
+                formatted = str(val)
+
+            trend = ""
+            if attr == "loss":
+                trend = _sparkline(list(state.loss_history))
+
+            table.add_row(label, formatted, trend)
+
+        # Any extra keys not in _KNOWN_KEYS
+        known_attrs = {k for k, _, _ in _KNOWN_KEYS}
+        for key, val in sorted(state.extra.items()):
+            if key in known_attrs or val is None:
+                continue
+            try:
+                formatted = f"{val:.4f}"
+            except (ValueError, TypeError):
+                formatted = str(val)
+            table.add_row(key, formatted, "")
+
+        if table.row_count == 0:
+            return Panel(
+                Text("Waiting for first log step...", style="dim"),
+                title="Training",
+                border_style="blue",
+            )
+
+        return Panel(table, title="Training", border_style="blue")

src/axolotl/tui/parsers/__init__.py

@@ -0,0 +1,7 @@
+"""Built-in line parsers — auto-imported to trigger @register_parser decorators."""
+
+from axolotl.tui.parsers.deepspeed import DeepSpeedParser  # noqa: F401
+from axolotl.tui.parsers.nccl import NCCLErrorParser  # noqa: F401
+from axolotl.tui.parsers.raw_log import RawLogParser  # noqa: F401
+from axolotl.tui.parsers.torch_compile import TorchCompileParser  # noqa: F401
+from axolotl.tui.parsers.tqdm import TqdmParser  # noqa: F401

src/axolotl/tui/parsers/deepspeed.py

@@ -0,0 +1,29 @@
+"""DeepSpeedParser — extracts DeepSpeed stage info and throughput metrics."""
+
+from __future__ import annotations
+
+import re
+
+from axolotl.tui.io_capture import LineParser, register_parser
+
+
+@register_parser
+class DeepSpeedParser(LineParser):
+    priority = 20
+    name = "deepspeed"
+
+    _SAMPLES_RE = re.compile(r"samples/sec=([0-9.]+)")
+    _STAGE_RE = re.compile(r"ZeRO Stage (\d)")
+
+    def parse(self, line: str, source: str) -> list[dict]:
+        events: list[dict] = []
+        if m := self._SAMPLES_RE.search(line):
+            events.append(
+                {
+                    "type": "metrics",
+                    "logs": {"samples_per_second": float(m.group(1))},
+                }
+            )
+        if m := self._STAGE_RE.search(line):
+            events.append({"type": "run_info", "zero_stage": int(m.group(1))})
+        return events

src/axolotl/tui/parsers/nccl.py

@@ -0,0 +1,27 @@
+"""NCCLErrorParser — surfaces NCCL errors as red alert events."""
+
+from __future__ import annotations
+
+import re
+
+from axolotl.tui.io_capture import LineParser, register_parser
+
+
+@register_parser
+class NCCLErrorParser(LineParser):
+    priority = 10
+    name = "nccl_error"
+
+    _RE = re.compile(r"NCCL error|Unhandled NCCL", re.IGNORECASE)
+
+    def parse(self, line: str, source: str) -> list[dict]:
+        if self._RE.search(line):
+            return [
+                {
+                    "type": "log_line",
+                    "level": "error",
+                    "message": f"⚠ NCCL: {line}",
+                },
+                {"type": "alert", "severity": "error", "message": line},
+            ]
+        return []

src/axolotl/tui/parsers/raw_log.py

@@ -0,0 +1,37 @@
+"""RawLogParser — catches every line as a log_line event."""
+
+from __future__ import annotations
+
+import re
+
+from axolotl.tui.io_capture import LineParser, register_parser
+
+
+@register_parser
+class RawLogParser(LineParser):
+    priority = 99
+    name = "raw_log"
+
+    _LOG_RE = re.compile(
+        r"^(?P<ts>\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}[,\.]\d+)"
+        r"\s*[-]\s*(?P<level>DEBUG|INFO|WARNING|ERROR|CRITICAL)"
+        r"\s*[-]\s*(?P<msg>.+)$",
+        re.IGNORECASE,
+    )
+
+    # Filter out tqdm progress bar lines and other noisy output
+    _TQDM_RE = re.compile(r"^\s*\d+%\|.*\|")
+    _EMPTY_RE = re.compile(r"^\s*$")
+
+    def parse(self, line: str, source: str) -> list[dict]:
+        # Skip empty lines and tqdm progress bar updates
+        if self._EMPTY_RE.match(line) or self._TQDM_RE.match(line):
+            return []
+
+        m = self._LOG_RE.match(line)
+        level = (
+            m.group("level").lower()
+            if m
+            else ("error" if source == "stderr" else "info")
+        )
+        return [{"type": "log_line", "level": level, "message": line}]

src/axolotl/tui/parsers/torch_compile.py

@@ -0,0 +1,26 @@
+"""TorchCompileParser — detects torch.compile graph breaks and recompilations."""
+
+from __future__ import annotations
+
+import re
+
+from axolotl.tui.io_capture import LineParser, register_parser
+
+
+@register_parser
+class TorchCompileParser(LineParser):
+    priority = 20
+    name = "torch_compile"
+
+    _RE = re.compile(r"Graph break|Recompiling|torch\.compile", re.IGNORECASE)
+
+    def parse(self, line: str, source: str) -> list[dict]:
+        if self._RE.search(line):
+            return [
+                {
+                    "type": "log_line",
+                    "level": "warning",
+                    "message": f"⚡ compile: {line}",
+                }
+            ]
+        return []

src/axolotl/tui/parsers/tqdm.py

@@ -0,0 +1,86 @@
+"""TqdmParser — captures tqdm progress bar output and surfaces as structured events."""
+
+from __future__ import annotations
+
+import re
+
+from axolotl.tui.io_capture import LineParser, register_parser
+
+
+@register_parser
+class TqdmParser(LineParser):
+    priority = 15
+    name = "tqdm"
+
+    # Match tqdm-style progress lines, e.g.:
+    #   Tokenizing Prompts (num_proc=24):  35%|███▍      | 19008/54568 [00:02<00:02, 17417.65 examples/s]
+    #   Loading weights:  53%|█████▎    | 77/146 [00:00<00:00, 396.39it/s]
+    #   0%|          | 0/30 [00:00<?, ?it/s]
+    _TQDM_RE = re.compile(
+        r"(?P<desc>.*?)\s*"
+        r"(?P<pct>\d+)%\|[▏▎▍▌▋▊▉█░▓▒# ]*\|\s*"
+        r"(?P<current>[\d,]+)/(?P<total>[\d,]+)"
+        r"\s*\[(?P<elapsed>[^\]]*)\]"
+    )
+
+    # Also match simpler forms like:
+    #   Fetching 0 files: 0it [00:00, ?it/s]
+    _FETCH_RE = re.compile(r"(?P<desc>[\w\s]+):\s*(?P<current>\d+)(?:it)?\s*\[.*?\]")
+
+    def parse(self, line: str, source: str) -> list[dict]:
+        m = self._TQDM_RE.search(line)
+        if m:
+            desc = m.group("desc").strip().rstrip(":")
+            pct = int(m.group("pct"))
+            current = int(m.group("current").replace(",", ""))
+            total = int(m.group("total").replace(",", ""))
+
+            events: list[dict] = []
+
+            # Surface as a log line with progress info
+            if pct == 100 or pct == 0 or pct % 25 == 0:
+                msg = (
+                    f"[{desc}] {pct}% ({current}/{total})"
+                    if desc
+                    else f"{pct}% ({current}/{total})"
+                )
+                events.append(
+                    {
+                        "type": "log_line",
+                        "level": "info",
+                        "message": msg,
+                    }
+                )
+
+            # Also emit as a progress metric
+            cleaned_desc = desc.strip().lower().replace(" ", "_")
+            if not cleaned_desc:
+                cleaned_desc = "progress"
+            events.append(
+                {
+                    "type": "metrics",
+                    "logs": {
+                        f"progress/{cleaned_desc}": pct / 100.0,
+                    },
+                }
+            )
+
+            return events
+
+        # Fallback: try simpler fetch-style progress lines
+        m = self._FETCH_RE.search(line)
+        if m:
+            desc = m.group("desc").strip().rstrip(":")
+            current = int(m.group("current"))
+            cleaned_desc = desc.strip().lower().replace(" ", "_")
+            if not cleaned_desc:
+                cleaned_desc = "fetch"
+            return [
+                {
+                    "type": "log_line",
+                    "level": "info",
+                    "message": f"[{desc}] {current}" if desc else f"{current}",
+                }
+            ]
+
+        return []

src/axolotl/tui/renderer.py

@@ -0,0 +1,449 @@
+"""TUIRenderer — background daemon thread that drives the rich.live.Live display."""
+
+from __future__ import annotations
+
+import logging
+import queue
+import threading
+import time
+from datetime import datetime
+from typing import Any
+
+from rich.console import Console
+from rich.layout import Layout
+from rich.live import Live
+
+from axolotl.tui.config import TUIConfig
+from axolotl.tui.gpu import GPUPoller
+from axolotl.tui.io_capture import (
+    IOCapture,
+    ParserChain,
+    get_registered_parsers,
+)
+from axolotl.tui.panels import BasePanel, get_registered_panels
+from axolotl.tui.state import CompletionSample, LogLine, TUIState
+
+LOG = logging.getLogger(__name__)
+
+
+class TUIRenderer:
+    """Background thread that renders the TUI dashboard using rich.live.Live."""
+
+    def __init__(self, config: TUIConfig, metric_queue: queue.Queue):
+        self._config = config
+        self._queue = metric_queue
+        self._state = TUIState()
+        self._gpu_poller = GPUPoller()
+        self._panels: list[BasePanel] = []
+        self._thread: threading.Thread | None = None
+        self._stop_event = threading.Event()
+        self._io_capture: IOCapture | None = None
+        self._parser_chain: ParserChain | None = None
+
+    def _init_panels(self) -> None:
+        registry = get_registered_panels()
+        for panel_name in self._config.panels:
+            if panel_name in registry:
+                self._panels.append(registry[panel_name]())
+
+    def _init_parser_chain(self) -> None:
+        # Ensure built-in parsers are imported so @register_parser decorators fire
+        import axolotl.tui.parsers  # noqa: F401
+
+        self._parser_chain = ParserChain()
+        # Register all built-in parsers
+        for parser_cls in get_registered_parsers():
+            self._parser_chain.register(parser_cls())
+
+        # Load plugin parsers
+        for plugin_spec in self._config.parser_plugins:
+            try:
+                if "::" in plugin_spec:
+                    # file path :: class name
+                    file_path, class_name = plugin_spec.split("::", 1)
+                    import importlib.util
+
+                    spec = importlib.util.spec_from_file_location(
+                        "custom_parser", file_path
+                    )
+                    if spec is None or spec.loader is None:
+                        raise ImportError(f"Cannot load spec for {file_path}")
+                    mod = importlib.util.module_from_spec(spec)
+                    spec.loader.exec_module(mod)
+                    parser_cls = getattr(mod, class_name)
+                else:
+                    # dotted module path
+                    module_path, class_name = plugin_spec.rsplit(".", 1)
+                    mod = importlib.import_module(module_path)
+                    parser_cls = getattr(mod, class_name)
+                self._parser_chain.register(parser_cls())
+            except Exception as exc:
+                LOG.warning(f"Failed to load parser plugin {plugin_spec}: {exc}")
+
+    def _build_layout(self) -> Layout:
+        layout = Layout()
+
+        top_panels = [p for p in self._panels if p.position == "top"]
+        left_panels = [p for p in self._panels if p.position == "left"]
+        right_panels = [p for p in self._panels if p.position == "right"]
+        bottom_panels = [p for p in self._panels if p.position == "bottom"]
+
+        sections = []
+
+        if top_panels:
+            layout_top = Layout(name="top", size=3)
+            sections.append(layout_top)
+
+        if left_panels or right_panels:
+            layout_middle = Layout(name="middle", ratio=3)
+            middle_parts = []
+            if left_panels:
+                middle_parts.append(Layout(name="left", ratio=1))
+            if right_panels:
+                middle_parts.append(Layout(name="right", ratio=1))
+            if middle_parts:
+                layout_middle.split_row(*middle_parts)
+            sections.append(layout_middle)
+
+        if bottom_panels:
+            layout_bottom = Layout(name="bottom", ratio=2)
+            if len(bottom_panels) > 1:
+                layout_bottom.split_row(
+                    *[
+                        Layout(name=f"bottom_{i}", ratio=1)
+                        for i in range(len(bottom_panels))
+                    ]
+                )
+            sections.append(layout_bottom)
+
+        if sections:
+            layout.split_column(*sections)
+
+        return layout
+
+    def _update_layout(self, layout: Layout) -> None:
+        top_panels = [p for p in self._panels if p.position == "top"]
+        left_panels = [p for p in self._panels if p.position == "left"]
+        right_panels = [p for p in self._panels if p.position == "right"]
+        bottom_panels = [p for p in self._panels if p.position == "bottom"]
+
+        if top_panels:
+            layout["top"].update(top_panels[0].render(self._state))
+
+        if left_panels:
+            layout["left"].update(left_panels[0].render(self._state))
+
+        if right_panels:
+            layout["right"].update(right_panels[0].render(self._state))
+
+        if bottom_panels:
+            if len(bottom_panels) == 1:
+                layout["bottom"].update(bottom_panels[0].render(self._state))
+            else:
+                for i, panel in enumerate(bottom_panels):
+                    layout[f"bottom_{i}"].update(panel.render(self._state))
+
+    def _drain_queue(self) -> None:
+        while True:
+            try:
+                event = self._queue.get_nowait()
+            except queue.Empty:
+                break
+
+            # Dispatch event to panels first
+            for panel in self._panels:
+                panel.on_event(event)
+
+            event_type = event.get("type")
+
+            if event_type == "metrics":
+                logs = event.get("logs", {})
+                self._apply_metrics(logs)
+
+            elif event_type == "step":
+                self._state.current_step = event.get("step", self._state.current_step)
+                self._state.total_steps = event.get(
+                    "total_steps", self._state.total_steps
+                )
+                self._state.current_epoch = event.get(
+                    "epoch", self._state.current_epoch
+                )
+                now = time.time()
+                self._state.elapsed_seconds = now - self._state.start_time.timestamp()
+                if self._state.current_step > 0 and self._state.total_steps > 0:
+                    rate = self._state.elapsed_seconds / self._state.current_step
+                    remaining = self._state.total_steps - self._state.current_step
+                    self._state.eta_seconds = rate * remaining
+
+            elif event_type == "log_line":
+                level = event.get("level", "info")
+                message = event.get("message", "")
+                self._state.log_lines.append(
+                    LogLine(
+                        timestamp=datetime.now(),
+                        level=level,
+                        message=message,
+                    )
+                )
+
+            elif event_type == "completion":
+                self._state.completions.append(
+                    CompletionSample(
+                        step=event.get("step", 0),
+                        prompt=event.get("prompt", ""),
+                        completion=event.get("completion", ""),
+                        reward=event.get("reward"),
+                        advantage=event.get("advantage"),
+                    )
+                )
+
+            elif event_type == "run_info":
+                if "run_name" in event:
+                    self._state.run_name = event["run_name"]
+                if "model_name" in event:
+                    self._state.model_name = event["model_name"]
+                if "training_mode" in event:
+                    self._state.training_mode = event["training_mode"]
+                if "world_size" in event:
+                    self._state.world_size = event["world_size"]
+                if "total_steps" in event:
+                    self._state.total_steps = event["total_steps"]
+                if "total_epochs" in event:
+                    self._state.total_epochs = event["total_epochs"]
+                if "zero_stage" in event:
+                    self._state.zero_stage = event["zero_stage"]
+
+            elif event_type == "done":
+                self._stop_event.set()
+
+    def _apply_metrics(self, logs: dict[str, Any]) -> None:
+        metric_map = {
+            "loss": "loss",
+            "grad_norm": "grad_norm",
+            "learning_rate": "learning_rate",
+            "tokens_per_second": "tokens_per_second",
+            "samples_per_second": "samples_per_second",
+            "mfu": "mfu",
+            "rewards/mean": "rewards_mean",
+            "rewards_mean": "rewards_mean",
+            "rewards/std": "rewards_std",
+            "rewards_std": "rewards_std",
+            "kl": "kl_divergence",
+            "kl_divergence": "kl_divergence",
+            "clip_ratio": "clip_ratio",
+            "queue_size": "queue_size",
+        }
+
+        for key, value in logs.items():
+            if key in metric_map:
+                setattr(self._state, metric_map[key], value)
+            else:
+                self._state.extra[key] = value
+
+        if "loss" in logs and logs["loss"] is not None:
+            self._state.loss_history.append(logs["loss"])
+
+    def start(self) -> None:
+        self._init_panels()
+        self._init_parser_chain()
+
+        # Set up I/O capture
+        assert self._parser_chain is not None, "_init_parser_chain must be called first"
+        self._io_capture = IOCapture(
+            log_path=self._config.stdout_log_path,
+            parser_chain=self._parser_chain,
+            metric_queue=self._queue,
+        )
+
+        # Monkeypatch tqdm to suppress terminal output and route through our queue.
+        # This prevents tqdm progress bars from flickering through the TUI and
+        # ensures all progress events appear in the Events panel.
+        self._install_tqdm_hook()
+
+        self._io_capture_ready = threading.Event()
+        self._thread = threading.Thread(target=self._run, daemon=True)
+        self._thread.start()
+        self._io_capture_ready.wait(timeout=5.0)
+
+    def _install_tqdm_hook(self) -> None:
+        """Replace tqdm's display method to route updates through TUI queue."""
+        try:
+            import io
+
+            import tqdm
+            import tqdm.auto
+
+            q = self._queue
+            self._tqdm_parser = None
+            # Find our tqdm parser in the chain
+            for p in self._parser_chain._parsers if self._parser_chain else []:
+                if p.name == "tqdm":
+                    self._tqdm_parser = p
+                    break
+
+            # Save originals for restore
+            self._orig_tqdm_class_auto = tqdm.auto.tqdm
+            self._orig_tqdm_class_tqdm = tqdm.tqdm
+            self._orig_tqdm_class_std = tqdm.std.tqdm
+
+            class TUITqdm(tqdm.tqdm):
+                """tqdm subclass that sends progress to TUI instead of terminal."""
+
+                def __init__(self, *args, **kwargs):
+                    # Force output to devnull so nothing reaches the terminal
+                    kwargs["file"] = io.StringIO()
+                    kwargs["dynamic_ncols"] = False
+                    kwargs["ncols"] = 80
+                    super().__init__(*args, **kwargs)
+
+                def display(self, msg=None, pos=None):
+                    # Build a progress string and push to queue
+                    if self.total and self.total > 0:
+                        pct = self.n / self.total * 100
+                        desc = self.desc.rstrip(": ") if self.desc else ""
+                        # Emit events at milestones or at low frequency
+                        is_milestone = (
+                            self.n == 0 or self.n >= self.total or int(pct) % 25 == 0
+                        )
+                        if is_milestone:
+                            try:
+                                q.put_nowait(
+                                    {
+                                        "type": "log_line",
+                                        "level": "info",
+                                        "message": f"[{desc}] {pct:.0f}% ({self.n}/{self.total})"
+                                        if desc
+                                        else f"{pct:.0f}% ({self.n}/{self.total})",
+                                    }
+                                )
+                            except Exception:
+                                pass
+
+                        try:
+                            metric_key = (
+                                f"progress/{desc.lower().replace(' ', '_')}"
+                                if desc
+                                else "progress/unknown"
+                            )
+                            q.put_nowait(
+                                {
+                                    "type": "metrics",
+                                    "logs": {metric_key: pct / 100.0},
+                                }
+                            )
+                        except Exception:
+                            pass
+
+                def close(self):
+                    # Emit final completion event
+                    if self.total and self.total > 0 and self.n > 0:
+                        desc = self.desc.rstrip(": ") if self.desc else ""
+                        try:
+                            q.put_nowait(
+                                {
+                                    "type": "log_line",
+                                    "level": "info",
+                                    "message": f"[{desc}] 100% ({self.total}/{self.total}) done"
+                                    if desc
+                                    else f"100% ({self.total}/{self.total}) done",
+                                }
+                            )
+                        except Exception:
+                            pass
+                    super().close()
+
+            # Replace tqdm globally
+            tqdm.auto.tqdm = TUITqdm
+            tqdm.tqdm = TUITqdm
+            # Also patch tqdm.std which some libraries use directly
+            tqdm.std.tqdm = TUITqdm
+            self._tui_tqdm_cls = TUITqdm
+
+        except Exception as exc:
+            LOG.debug(f"Failed to install tqdm hook: {exc}")
+
+    def _uninstall_tqdm_hook(self) -> None:
+        """Restore original tqdm."""
+        try:
+            import tqdm
+            import tqdm.auto
+
+            if hasattr(self, "_orig_tqdm_class_auto"):
+                tqdm.auto.tqdm = self._orig_tqdm_class_auto
+            if hasattr(self, "_orig_tqdm_class_tqdm"):
+                tqdm.tqdm = self._orig_tqdm_class_tqdm
+            if hasattr(self, "_orig_tqdm_class_std"):
+                tqdm.std.tqdm = self._orig_tqdm_class_std
+        except Exception:
+            pass
+
+    def stop(self) -> None:
+        self._stop_event.set()
+        self._uninstall_tqdm_hook()
+        if self._thread is not None:
+            self._thread.join(timeout=5.0)
+
+    def _run(self) -> None:
+        import os
+
+        # Save a handle to the REAL terminal BEFORE IO capture redirects fds.
+        # This ensures rich.live.Live writes to the terminal, not the pipe.
+        saved_tty_fd = os.dup(1)
+        tty_file = os.fdopen(saved_tty_fd, "w", buffering=1, closefd=True)
+        console = Console(file=tty_file)
+
+        layout = self._build_layout()
+        tick_interval = 1.0 / max(self._config.refresh_rate, 1)
+        gpu_poll_counter = 0
+        gpu_poll_ticks = max(
+            1, int(self._config.hardware_poll_interval / tick_interval)
+        )
+
+        # Start I/O capture — redirects fd 1/2 to pipe AFTER we saved the tty fd
+        if self._io_capture:
+            self._io_capture.start()
+
+        # Signal that IO capture is live so start() can return
+        if hasattr(self, "_io_capture_ready"):
+            self._io_capture_ready.set()
+
+        try:
+            with Live(
+                layout,
+                console=console,
+                refresh_per_second=self._config.refresh_rate,
+                screen=True,
+                redirect_stdout=False,
+                redirect_stderr=False,
+            ) as live:
+                while not self._stop_event.is_set():
+                    self._drain_queue()
+
+                    # Poll GPU stats periodically
+                    gpu_poll_counter += 1
+                    if gpu_poll_counter >= gpu_poll_ticks:
+                        gpu_poll_counter = 0
+                        if self._gpu_poller.available:
+                            self._state.gpus = self._gpu_poller.poll()
+
+                    # Update elapsed time
+                    self._state.elapsed_seconds = (
+                        time.time() - self._state.start_time.timestamp()
+                    )
+
+                    self._update_layout(layout)
+                    live.update(layout)
+
+                    time.sleep(tick_interval)
+
+                # Final drain
+                self._drain_queue()
+                self._update_layout(layout)
+                live.update(layout)
+        finally:
+            if self._io_capture:
+                self._io_capture.stop()
+            try:
+                tty_file.close()
+            except Exception:
+                pass

src/axolotl/tui/state.py

@@ -0,0 +1,88 @@
+"""TUI shared data model — dataclasses for the dashboard state."""
+
+from __future__ import annotations
+
+from collections import deque
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Any
+
+
+@dataclass
+class GPUStats:
+    id: int
+    name: str
+    util_pct: float
+    vram_used_gb: float
+    vram_total_gb: float
+    temp_c: int
+    power_w: float | None
+
+
+@dataclass
+class LogLine:
+    timestamp: datetime
+    level: str  # "info" | "debug" | "warning" | "error"
+    message: str
+
+
+@dataclass
+class CompletionSample:
+    step: int
+    prompt: str
+    completion: str
+    reward: float | None
+    advantage: float | None
+
+
+@dataclass
+class TUIState:
+    # Run metadata
+    run_name: str = ""
+    model_name: str = ""
+    training_mode: str = "sft"
+    world_size: int = 1
+    start_time: datetime = field(default_factory=datetime.now)
+
+    # Progress
+    current_step: int = 0
+    total_steps: int = 0
+    current_epoch: float = 0.0
+    total_epochs: float = 1.0
+    elapsed_seconds: float = 0.0
+    eta_seconds: float | None = None
+
+    # Training metrics (rolling window + current)
+    loss: float | None = None
+    grad_norm: float | None = None
+    learning_rate: float | None = None
+    tokens_per_second: float | None = None
+    samples_per_second: float | None = None
+    mfu: float | None = None
+
+    # RL-specific (None for non-RL modes)
+    rewards_mean: float | None = None
+    rewards_std: float | None = None
+    kl_divergence: float | None = None
+    clip_ratio: float | None = None
+    queue_size: int | None = None
+
+    # Per-GPU hardware (list indexed by local rank)
+    gpus: list[GPUStats] = field(default_factory=list)
+
+    # Recent log lines
+    log_lines: deque[LogLine] = field(default_factory=lambda: deque(maxlen=200))
+
+    # Recent completions (GRPO/SFT with log_completions)
+    completions: deque[CompletionSample] = field(
+        default_factory=lambda: deque(maxlen=20)
+    )
+
+    # Loss history for sparkline
+    loss_history: deque[float] = field(default_factory=lambda: deque(maxlen=50))
+
+    # DeepSpeed zero stage (None if not using DeepSpeed)
+    zero_stage: int | None = None
+
+    # Arbitrary plugin state
+    extra: dict[str, Any] = field(default_factory=dict)

src/axolotl/utils/callbacks/qat.py

@@ -25,9 +25,11 @@ def toggle_fake_quant(mod: nn.Module, enable: bool):
         if (
             isinstance(mod, FakeQuantizedLinear)
             and mod.activation_fake_quantizer is not None
+            and hasattr(mod.activation_fake_quantizer, "enabled")
         ):
             mod.activation_fake_quantizer.enabled = enable
-        mod.weight_fake_quantizer.enabled = enable
+        if hasattr(mod.weight_fake_quantizer, "enabled"):
+            mod.weight_fake_quantizer.enabled = enable
 
 
 class QATCallback(TrainerCallback):

src/axolotl/utils/callbacks/tokens_per_second.py

@@ -12,12 +12,11 @@ from transformers import (
     TrainingArguments,
 )
 
+from axolotl.core.trainers.constants import TOKENS_STATE_FILE
 from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)
 
-TOKENS_STATE_FILE = "tokens_state.json"
-
 
 class TokensPerSecondCallback(TrainerCallback):
     """

src/axolotl/utils/config/__init__.py

@@ -22,7 +22,12 @@ from axolotl.utils.schemas.config import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
     AxolotlInputConfig as AxolotlInputConfigBase,
 )
-from axolotl.utils.schemas.datasets import DPODataset, KTODataset, SFTDataset
+from axolotl.utils.schemas.datasets import (
+    DPODataset,
+    KTODataset,
+    SFTDataset,
+    SyntheticDataset,
+)
 
 LOG = get_logger(__name__)
 
@@ -308,6 +313,14 @@ def validate_config(
                 cfg["datasets"][idx] = DPODataset(**ds_cfg)
             elif cfg.get("rl") == "kto" and not isinstance(ds_cfg, KTODataset):
                 cfg["datasets"][idx] = KTODataset(**dict(ds_cfg))
+            elif (
+                ds_cfg.get("type")
+                if isinstance(ds_cfg, dict)
+                else getattr(ds_cfg, "type", None)
+            ) == "_synthetic" and not isinstance(ds_cfg, SyntheticDataset):
+                cfg["datasets"][idx] = SyntheticDataset(
+                    **(ds_cfg if isinstance(ds_cfg, dict) else dict(ds_cfg))
+                )
             elif not isinstance(ds_cfg, SFTDataset):
                 cfg["datasets"][idx] = SFTDataset(**dict(ds_cfg))
 

src/axolotl/utils/data/sft.py

@@ -376,10 +376,14 @@ def _load_and_process_single_dataset(
     streaming: bool = False,
 ) -> tuple[Dataset | IterableDataset, Prompter | None]:
     """Load and process a single dataset based on the passed config."""
-    # Load the dataset
-    dataset = load_dataset_with_config(
-        dataset_config, cfg.hf_use_auth_token, streaming=streaming
-    )
+    # For synthetic datasets, create a minimal placeholder instead of loading from path
+    if dataset_config.type == "_synthetic":
+        dataset = Dataset.from_dict({"text": [""]})
+    else:
+        # Load the dataset
+        dataset = load_dataset_with_config(
+            dataset_config, cfg.hf_use_auth_token, streaming=streaming
+        )
 
     # Parse dataset type
     d_base_type, d_prompt_style = _parse_dataset_type(dataset_config.type)

src/axolotl/utils/quantization.py

@@ -10,9 +10,11 @@ from torchao.quantization import quantize_
 from torchao.quantization.qat import (
     QATConfig,
 )
+from torchao.quantization.qat.fake_quantize_config import Int4WeightFakeQuantizeConfig
 from torchao.quantization.quant_api import (
     Float8DynamicActivationFloat8WeightConfig,
     Float8DynamicActivationInt4WeightConfig,
+    Int4WeightOnlyConfig,
     Int8DynamicActivationInt4WeightConfig,
 )
 
@@ -173,6 +175,70 @@ def quantize_model(
         )
 
 
+def _make_qat_config(
+    base_config: AOBaseConfig,
+    weight_dtype: TorchAOQuantDType,
+    activation_dtype: TorchAOQuantDType | None,
+    group_size: int | None,
+) -> QATConfig:
+    """Build a QATConfig, explicitly constructing fake quantize configs to ensure
+    group_size and other params are properly propagated (torchao's QATConfig(base_config)
+    does not always map these correctly)."""
+    from torchao.quantization.qat.fake_quantize_config import (
+        Float8FakeQuantizeConfig,
+        IntxFakeQuantizeConfig,
+    )
+
+    if isinstance(base_config, MXFakeQuantizeConfig):
+        return QATConfig(
+            activation_config=base_config,
+            weight_config=base_config,
+        )
+
+    # Build explicit weight config
+    weight_fq_config: (
+        Int4WeightFakeQuantizeConfig
+        | IntxFakeQuantizeConfig
+        | Float8FakeQuantizeConfig
+        | None
+    ) = None
+    if weight_dtype == TorchAOQuantDType.int4:
+        gs = (
+            group_size
+            if group_size is not None
+            else getattr(base_config, "group_size", 128)
+        )
+        activation_dt = None
+        if activation_dtype == TorchAOQuantDType.int8:
+            activation_dt = torch.bfloat16
+        elif activation_dtype == TorchAOQuantDType.float8_e4m3fn:
+            activation_dt = torch.float8_e4m3fn
+        kwargs = {"group_size": gs}
+        if activation_dt is not None:
+            kwargs["activation_dtype"] = activation_dt
+        weight_fq_config = Int4WeightFakeQuantizeConfig(**kwargs)
+    elif weight_dtype == TorchAOQuantDType.float8_e4m3fn:
+        weight_fq_config = Float8FakeQuantizeConfig(dtype=torch.float8_e4m3fn)
+
+    # Build explicit activation config
+    activation_fq_config = None
+    if activation_dtype == TorchAOQuantDType.int8:
+        activation_fq_config = IntxFakeQuantizeConfig(
+            dtype=torch.int8, granularity="per_token", is_symmetric=False
+        )
+    elif activation_dtype == TorchAOQuantDType.float8_e4m3fn:
+        activation_fq_config = Float8FakeQuantizeConfig(dtype=torch.float8_e4m3fn)
+
+    if weight_fq_config is not None:
+        return QATConfig(
+            weight_config=weight_fq_config,
+            activation_config=activation_fq_config,
+        )
+
+    # Fallback to base_config for unhandled combos
+    return QATConfig(base_config)
+
+
 def prepare_model_for_qat(
     model,
     weight_dtype: TorchAOQuantDType,
@@ -200,13 +266,9 @@ def prepare_model_for_qat(
         activation_dtype=activation_dtype,
         group_size=group_size,
     )
-    if isinstance(base_config, MXFakeQuantizeConfig):
-        qat_config = QATConfig(
-            activation_config=base_config,
-            weight_config=base_config,
-        )
-    else:
-        qat_config = QATConfig(base_config)
+    qat_config = _make_qat_config(
+        base_config, weight_dtype, activation_dtype, group_size
+    )
     quantize_(model, qat_config)
     if quantize_embedding:
         # activation fake quantization is not supported for embedding layers
@@ -215,12 +277,9 @@ def prepare_model_for_qat(
             activation_dtype=None,
             group_size=group_size,
         )
-        if isinstance(embedding_base_config, MXFakeQuantizeConfig):
-            embedding_qat_config = QATConfig(
-                weight_config=embedding_base_config,
-            )
-        else:
-            embedding_qat_config = QATConfig(embedding_base_config)
+        embedding_qat_config = _make_qat_config(
+            embedding_base_config, weight_dtype, None, group_size
+        )
         quantize_(
             model,
             embedding_qat_config,

src/axolotl/utils/schedulers.py

@@ -2,7 +2,7 @@
 
 import math
 from functools import partial
-from typing import Sequence
+from typing import Any, Sequence
 
 from torch import Tensor
 from torch.optim import Optimizer
@@ -340,3 +340,19 @@ class JaggedLRRestartScheduler(LRScheduler):
             return [lr * scale for lr in original]
 
         return original * scale
+
+    def state_dict(self) -> dict[str, Any]:
+        """Return serializable state, saving inner_schedule as its own state_dict."""
+        state = {
+            key: value
+            for key, value in self.__dict__.items()
+            if key not in ("optimizer", "inner_schedule")
+        }
+        state["inner_schedule_state"] = self.inner_schedule.state_dict()
+        return state
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        """Restore state, including inner_schedule."""
+        inner_state = state_dict.pop("inner_schedule_state")
+        self.__dict__.update(state_dict)
+        self.inner_schedule.load_state_dict(inner_state)

src/axolotl/utils/schemas/config.py

@@ -13,6 +13,7 @@ from pydantic import (
     model_validator,
 )
 
+from axolotl.tui.config import TUIConfig
 from axolotl.utils.datasets import get_default_process_count
 from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.datasets import (
@@ -22,6 +23,7 @@ from axolotl.utils.schemas.datasets import (
     PretrainingDataset,
     SFTDataset,
     StepwiseSupervisedDataset,
+    SyntheticDataset,
 )
 from axolotl.utils.schemas.deprecated import DeprecatedParameters, RemappedParameters
 from axolotl.utils.schemas.dynamic_checkpoint import DynamicCheckpointConfig
@@ -139,6 +141,12 @@ class AxolotlInputConfig(
     vllm: VllmConfig | None = Field(
         default_factory=lambda: VllmConfig(),
     )
+    tui: TUIConfig | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "TUI dashboard configuration. Set enabled: true to activate."
+        },
+    )
     qat: QATConfig | None = None
     quantization: PTQConfig | None = None
     reward_model: bool | None = Field(
@@ -185,7 +193,13 @@ class AxolotlInputConfig(
 
     datasets: (
         Annotated[
-            list[SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset],
+            list[
+                SFTDataset
+                | DPODataset
+                | KTODataset
+                | StepwiseSupervisedDataset
+                | SyntheticDataset
+            ],
             MinLen(1),
         ]
         | None
@@ -198,7 +212,13 @@ class AxolotlInputConfig(
 
     test_datasets: (
         Annotated[
-            list[SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset],
+            list[
+                SFTDataset
+                | DPODataset
+                | KTODataset
+                | StepwiseSupervisedDataset
+                | SyntheticDataset
+            ],
             MinLen(1),
         ]
         | None
@@ -433,6 +453,12 @@ class AxolotlInputConfig(
             "description": "Whether to offload activations. Available options are: true, false, 'legacy', 'disk'."
         },
     )
+    layer_offloading: bool | None = Field(
+        default=False,
+        json_schema_extra={
+            "description": "Offload model layer parameters to CPU during forward, prefetch back during backward."
+        },
+    )
 
     unfrozen_parameters: list[str] | None = Field(
         default=None,
@@ -684,6 +710,12 @@ class AxolotlInputConfig(
             "description": "Apply custom LoRA autograd functions and activation function Triton kernels for speed and memory savings. See: https://docs.axolotl.ai/docs/lora_optims.html"
         },
     )
+    lora_embedding_kernel: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Apply custom LoRA autograd function for embedding layers. See: https://docs.axolotl.ai/docs/lora_optims.html"
+        },
+    )
 
     chunked_cross_entropy: bool | None = Field(
         default=None,
@@ -1294,6 +1326,7 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
             data.get("lora_mlp_kernel")
             or data.get("lora_qkv_kernel")
             or data.get("lora_o_kernel")
+            or data.get("lora_embedding_kernel")
         ):
             capabilities = data.get("capabilities")
             is_fsdp = data.get("fsdp_config") is not None
@@ -1341,7 +1374,12 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
         if data.get("adapter") in ["lora", "qlora"]:
             # Skip if already set, using unsloth optimizations, or using 8-bit
             unsloth_fields = ["unsloth_lora_mlp", "unsloth_lora_qkv", "unsloth_lora_o"]
-            kernel_fields = ["lora_mlp_kernel", "lora_qkv_kernel", "lora_o_kernel"]
+            kernel_fields = [
+                "lora_mlp_kernel",
+                "lora_qkv_kernel",
+                "lora_o_kernel",
+                "lora_embedding_kernel",
+            ]
             if (
                 any(data.get(k) is not None for k in kernel_fields)
                 or any(data.get(k) for k in unsloth_fields)
@@ -1354,10 +1392,6 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
             if data.get("trust_remote_code"):
                 return data
 
-            # Skip if dropout is not 0, as auto enabling it would just disable it during runtime patch checks
-            if data.get("lora_dropout") != 0:
-                return data
-
             # Check multi-GPU compatibility
             capabilities = data.get("capabilities")
             is_multi_gpu = capabilities and capabilities.get("n_gpu", 0) > 1
@@ -1379,6 +1413,9 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
                 if data.get("lora_o_kernel") is None:
                     data["lora_o_kernel"] = True
 
+                if data.get("lora_embedding_kernel") is None:
+                    data["lora_embedding_kernel"] = True
+
                 LOG.warning(
                     "Auto-enabling LoRA kernel optimizations for faster training. "
                     + "Please explicitly set `lora_*_kernel` config values to `false` to disable. "

src/axolotl/utils/schemas/datasets.py

@@ -296,4 +296,42 @@ class KTODataset(BaseModel):
     revision: str | None = None
 
 
-DatasetConfig = SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset
+class SyntheticDataset(BaseModel):
+    """Synthetic dataset configuration for benchmarking and testing.
+
+    Generates datasets with configurable sequence length, dataset size, and token ID
+    ranges. Useful for benchmarking memory usage and speed by sequence length, and for
+    validating weighted dataset mixes.
+    """
+
+    path: Literal["synthetic"] = "synthetic"
+    type: Literal["_synthetic"] = "_synthetic"
+    length: int = Field(
+        default=1000,
+        json_schema_extra={"description": "Number of rows to generate"},
+    )
+    sequence_length: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Sequence length per row (defaults to sequence_len from config)"
+        },
+    )
+    min_input_id: int = Field(
+        default=100,
+        json_schema_extra={"description": "Minimum token ID for generation"},
+    )
+    max_input_id: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Maximum token ID for generation (defaults to tokenizer vocab_size)"
+        },
+    )
+    seed: int | None = Field(
+        default=None,
+        json_schema_extra={"description": "Random seed for reproducibility"},
+    )
+
+
+DatasetConfig = (
+    SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset | SyntheticDataset
+)

src/axolotl/utils/schemas/enums.py

@@ -87,6 +87,11 @@ class CustomSupportedOptimizers(str, Enum):
     came_pytorch = "came_pytorch"
     muon = "muon"
     dion = "dion"
+    flash_adamw = "flash_adamw"
+    flash_adam = "flash_adam"
+    flash_sgd = "flash_sgd"
+    flash_sgdw = "flash_sgdw"
+    flash_lion = "flash_lion"
 
 
 class RingAttnFunc(str, Enum):

src/axolotl/utils/schemas/validation.py

@@ -681,15 +681,7 @@ class LoRAValidationMixin:
     @model_validator(mode="before")
     @classmethod
     def check_lora_kernels_dora(cls, data):
-        if (
-            data.get("lora_mlp_kernel")
-            or data.get("lora_qkv_kernel")
-            or data.get("lora_o_kernel")
-        ) and data.get("peft_use_dora"):
-            raise ValueError(
-                "lora_mlp_kernel, lora_qkv_kernel, and lora_o_kernel are not "
-                "compatible with DoRA at the moment."
-            )
+        # DoRA is now supported by lora kernels
         return data
 
     @model_validator(mode="before")
@@ -790,6 +782,14 @@ class OptimizationValidationMixin:
             LOG.warning("adamw hyperparameters found, but no adamw optimizer set")
         return self
 
+    @staticmethod
+    def _resolve_fsdp_version(data):
+        """Resolve FSDP version from top-level fsdp_version or fsdp_config.fsdp_version."""
+        fsdp_version = data.get("fsdp_version")
+        if fsdp_version is None:
+            fsdp_version = data.get("fsdp_config", {}).get("fsdp_version", 1)
+        return fsdp_version
+
     @model_validator(mode="before")
     @classmethod
     def check_muon_deepspeed_fsdp(cls, data):
@@ -799,15 +799,32 @@ class OptimizationValidationMixin:
                     "Muon optimizer is currently incompatible with DeepSpeed"
                 )
             if data.get("fsdp") or data.get("fsdp_config"):
-                fsdp_version = data.get("fsdp_version")
-                if fsdp_version is None:
-                    fsdp_version = data.get("fsdp_config", {}).get("fsdp_version", 1)
+                fsdp_version = cls._resolve_fsdp_version(data)
                 if str(fsdp_version) != "2":
                     raise ValueError(
                         "Muon optimizer is only compatible with FSDP2. Set fsdp_version: 2 to use Muon with FSDP."
                     )
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_flashoptim_deepspeed_fsdp(cls, data):
+        optimizer = data.get("optimizer") or ""
+        if str(optimizer).startswith("flash_"):
+            if data.get("deepspeed"):
+                raise ValueError(
+                    f"{optimizer} optimizer is incompatible with DeepSpeed. "
+                    "Flash optimizers only support DDP and FSDP2."
+                )
+            if data.get("fsdp") or data.get("fsdp_config"):
+                fsdp_version = cls._resolve_fsdp_version(data)
+                if str(fsdp_version) != "2":
+                    raise ValueError(
+                        f"{optimizer} optimizer is only compatible with FSDP2. "
+                        "Set fsdp_version: 2 to use flash optimizers with FSDP."
+                    )
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_batch_flattening_fa(cls, data):

tests/conftest.py

@@ -15,6 +15,8 @@ import datasets
 import pytest
 import requests
 import torch
+import transformers.utils as _transformers_utils
+import transformers.utils.import_utils as _import_utils
 from huggingface_hub import snapshot_download
 from huggingface_hub.errors import LocalEntryNotFoundError
 from tokenizers import AddedToken
@@ -29,6 +31,26 @@ from tests.hf_offline_utils import (
 
 logging.getLogger("filelock").setLevel(logging.CRITICAL)
 
+# Shim for deepseek v3
+if not hasattr(_import_utils, "is_torch_fx_available"):
+
+    def _is_torch_fx_available():
+        try:
+            import torch.fx  # noqa: F401  # pylint: disable=unused-import
+
+            return True
+        except ImportError:
+            return False
+
+    _import_utils.is_torch_fx_available = _is_torch_fx_available
+
+if not hasattr(_transformers_utils, "is_flash_attn_greater_or_equal_2_10"):
+    from transformers.utils import is_flash_attn_greater_or_equal as _is_flash_attn_gte
+
+    _transformers_utils.is_flash_attn_greater_or_equal_2_10 = lambda: (
+        _is_flash_attn_gte("2.10")
+    )
+
 
 def retry_on_request_exceptions(max_retries=3, delay=1):
     def decorator(func):

tests/core/test_async_grpo.py

@@ -153,7 +153,7 @@ class TestLoraFP8Guard(unittest.TestCase):
 
         proj.base_layer = base_layer
 
-        W, b, quant_state, A, B, s = get_lora_parameters(proj)
+        W, b, quant_state, A, B, s, *_ = get_lora_parameters(proj)
         # quant_state should be None since weight is bf16, not FP8
         self.assertIsNone(quant_state)
 
@@ -174,7 +174,7 @@ class TestLoraFP8Guard(unittest.TestCase):
         scale_inv = torch.ones(1)
         base_layer.weight_scale_inv = scale_inv
 
-        W, b, quant_state, A, B, s = get_lora_parameters(proj)
+        W, b, quant_state, A, B, s, *_ = get_lora_parameters(proj)
         self.assertIs(quant_state, scale_inv)
 
 

tests/e2e/integrations/test_scattermoe_lora_kernels.py

@@ -20,6 +20,7 @@ Test strategy:
 - Tolerances account for tf32 accumulation in Triton kernels
 """
 
+from functools import wraps
 from types import SimpleNamespace
 
 import pytest
@@ -34,6 +35,21 @@ pytestmark = pytest.mark.skipif(
 _SMOE = "axolotl.integrations.kernels.libs.scattermoe_lora"
 
 
+def skip_on_out_of_resources(func):
+    """Skip test if Triton kernel exceeds GPU shared memory limits."""
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        try:
+            return func(*args, **kwargs)
+        except Exception as exc:  # pylint: disable=broad-except
+            if "OutOfResources" in type(exc).__name__:
+                pytest.skip(f"GPU shared memory too small: {exc}")
+            raise
+
+    return wrapper
+
+
 # =============================================================================
 # Helpers
 # =============================================================================
@@ -209,6 +225,7 @@ def make_test_data(
 # =============================================================================
 
 
+@pytest.mark.slow
 class TestForwardPass:
     """Test forward pass of fused scatter2scatter_lora kernel."""
 
@@ -288,6 +305,7 @@ class TestForwardPass:
         )
 
 
+@pytest.mark.slow
 class TestForwardGrouped:
     """Test forward pass with grouped_in/grouped_out configurations."""
 
@@ -377,6 +395,7 @@ class TestForwardGrouped:
 # =============================================================================
 
 
+@pytest.mark.slow
 class TestLoRAGradients:
     """Test backward LoRA gradient computation (dA, dB)."""
 
@@ -452,6 +471,7 @@ class TestLoRAGradients:
 # =============================================================================
 
 
+@pytest.mark.slow
 class TestAutograd:
     """Test full autograd integration through ScatterMoELoRA."""
 
@@ -620,6 +640,7 @@ class TestAutograd:
 # =============================================================================
 
 
+@pytest.mark.slow
 class TestBaseEquivalence:
     """When scaling=0, fused kernel should match base scatter2scatter."""
 
@@ -692,6 +713,7 @@ class TestBaseEquivalence:
 # =============================================================================
 
 
+@pytest.mark.slow
 class TestLoRAAdditivity:
     """Test that the LoRA component is correctly additive."""
 
@@ -749,6 +771,7 @@ class TestLoRAAdditivity:
 # =============================================================================
 
 
+@pytest.mark.slow
 class TestParallelExpertsModule:
     """Test the ParallelExperts module with LoRA."""
 
@@ -816,6 +839,7 @@ class TestParallelExpertsModule:
 # =============================================================================
 
 
+@pytest.mark.slow
 class TestEdgeCases:
     """Edge cases and boundary conditions."""
 
@@ -913,6 +937,7 @@ class TestEdgeCases:
 # =============================================================================
 
 
+@pytest.mark.slow
 class TestFusedDX:
     """Test fused backward dX kernel: dX = dY @ W^T + scaling * (dY @ B) @ A."""
 
@@ -980,6 +1005,7 @@ class TestFusedDX:
     def test_basic(self):
         self._run_fused_dX_test(M=32, K=64, N=128, E=4, R=8, k=2)
 
+    @skip_on_out_of_resources
     def test_large(self):
         self._run_fused_dX_test(M=256, K=256, N=512, E=8, R=16, k=2)
 
@@ -1122,6 +1148,7 @@ class TestFusedDX:
 # =============================================================================
 
 
+@pytest.mark.slow
 class TestFusedGatherBackward:
     """Test fused gather + backward dA/dB kernel."""
 
@@ -1174,6 +1201,7 @@ class TestFusedGatherBackward:
     def test_basic(self):
         self._run_fused_gather_test(M=32, K=64, N=128, E=4, R=8, k=2)
 
+    @skip_on_out_of_resources
     def test_large(self):
         self._run_fused_gather_test(M=256, K=256, N=512, E=8, R=16, k=2)
 
@@ -1183,6 +1211,7 @@ class TestFusedGatherBackward:
     def test_k1(self):
         self._run_fused_gather_test(M=64, K=64, N=128, E=4, R=8, k=1)
 
+    @skip_on_out_of_resources
     def test_many_experts(self):
         self._run_fused_gather_test(M=128, K=64, N=128, E=16, R=8, k=4)
 
@@ -1269,6 +1298,8 @@ class TestFusedGatherBackward:
 # =============================================================================
 
 
+@pytest.mark.slow
+@pytest.mark.xfail(reason="flaky", strict=False)
 class TestTokenRounding:
     """Test token rounding utility and its integration with backward kernels."""
 
@@ -1315,6 +1346,7 @@ class TestTokenRounding:
                 )
             prev = padded_offsets[e].item()
 
+    @skip_on_out_of_resources
     def test_round_with_fused_gather(self):
         """Token rounding + fused gather gives same result as plain fused gather."""
         from importlib import import_module
@@ -1414,6 +1446,7 @@ class TestTokenRounding:
 # =============================================================================
 
 
+@pytest.mark.slow
 class TestCombinedOptimizations:
     """Test all optimizations together."""
 
@@ -1583,6 +1616,7 @@ def _make_mock_sigmoid_moe_block(
     return moe_block, T, H, FF, E, K
 
 
+@pytest.mark.slow
 class TestHFScatterMoESigmoidRouting:
     """Test HFScatterMoEGatedMLP forward with sigmoid routing on GPU."""
 
@@ -1724,6 +1758,7 @@ class TestHFScatterMoESigmoidRouting:
         )
 
 
+@pytest.mark.slow
 class TestHFScatterMoESigmoidWithSharedExperts:
     """Test HFScatterMoEGatedMLP with sigmoid routing + shared experts."""
 

tests/e2e/integrations/test_scattermoe_lora_olmoe.py

@@ -933,7 +933,7 @@ class TestKernelizeIntegration:
     def _get_repo_path():
         """Get the path to scattermoe_lora within axolotl's plugin."""
         return (
-            Path(__file__).parent.parent.parent
+            Path(__file__).parent.parent.parent.parent
             / "src"
             / "axolotl"
             / "integrations"
@@ -1219,7 +1219,7 @@ class TestSharedExpertHandling:
 
         # Kernelize
         repo_path = (
-            Path(__file__).parent.parent.parent
+            Path(__file__).parent.parent.parent.parent
             / "src"
             / "axolotl"
             / "integrations"

tests/e2e/kernels/test_lora.py

@@ -102,7 +102,7 @@ def mock_proj():
 def test_get_lora_parameters(mock_proj):
     """Tests get_lora_parameters function"""
     # Test with LoRA enabled
-    W, b, _, A, B, s = get_lora_parameters(mock_proj)
+    W, b, _, A, B, s, *_ = get_lora_parameters(mock_proj)
 
     assert isinstance(W, torch.Tensor)
     assert W.shape == (128, 64)
@@ -113,13 +113,13 @@ def test_get_lora_parameters(mock_proj):
 
     # Test with LoRA disabled
     mock_proj.disable_adapters = True
-    W, b, _, A, B, s = get_lora_parameters(mock_proj)
+    W, b, _, A, B, s, *_ = get_lora_parameters(mock_proj)
     assert A is None and B is None and s is None
 
     # Test with merged state
     mock_proj.disable_adapters = False
     mock_proj.merged = True
-    W, b, _, A, B, s = get_lora_parameters(mock_proj)
+    W, b, _, A, B, s, *_ = get_lora_parameters(mock_proj)
     assert A is None and B is None and s is None
 
 

tests/e2e/multigpu/test_fsdp2_lora_kernels.py

@@ -0,0 +1,120 @@
+"""Test LoRA kernels under FSDP2 multi-GPU training.
+
+Verifies that lora_qkv_kernel, lora_o_kernel, lora_mlp_kernel, and
+lora_embedding_kernel work correctly with FSDP2 sharding, including
+with bias, dropout, and DoRA enabled.
+"""
+
+from pathlib import Path
+
+import yaml
+from accelerate.test_utils import execute_subprocess_async
+from transformers.testing_utils import get_torch_dist_unique_port
+
+from axolotl.utils.dict import DictDefault
+
+from tests.e2e.utils import require_torch_2_7_0
+
+AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
+
+
+def _run_training(temp_dir, cfg):
+    """Write config and launch multi-GPU training."""
+    Path(temp_dir).mkdir(parents=True, exist_ok=True)
+    with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+        fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+    execute_subprocess_async(
+        [
+            "axolotl",
+            "train",
+            str(Path(temp_dir) / "config.yaml"),
+            "--num-processes",
+            "2",
+            "--main-process-port",
+            f"{get_torch_dist_unique_port()}",
+        ]
+    )
+
+
+def _base_lora_fsdp2_config(temp_dir, **overrides):
+    """Base config for LoRA + FSDP2 + kernel tests."""
+    cfg = {
+        "base_model": "Qwen/Qwen3-0.6B",
+        "sequence_len": 512,
+        "val_set_size": 0.0,
+        "datasets": [
+            {
+                "path": "tatsu-lab/alpaca",
+                "type": "alpaca",
+                "split": "train[:1%]",
+            },
+        ],
+        "adapter": "lora",
+        "lora_r": 8,
+        "lora_alpha": 16,
+        "lora_target_linear": True,
+        "num_epochs": 1,
+        "max_steps": 3,
+        "micro_batch_size": 1,
+        "gradient_accumulation_steps": 1,
+        "output_dir": temp_dir,
+        "learning_rate": 1e-4,
+        "optimizer": "adamw_torch_fused",
+        "lr_scheduler": "cosine",
+        "flash_attention": True,
+        "bf16": True,
+        "fsdp_version": 2,
+        "fsdp_config": {
+            "offload_params": False,
+            "cpu_ram_efficient_loading": False,
+            "transformer_layer_cls_to_wrap": "Qwen3DecoderLayer",
+            "state_dict_type": "FULL_STATE_DICT",
+            "auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
+            "reshard_after_forward": True,
+        },
+        # Enable all LoRA kernels
+        "lora_mlp_kernel": True,
+        "lora_qkv_kernel": True,
+        "lora_o_kernel": True,
+        "lora_embedding_kernel": True,
+        "save_safetensors": True,
+    }
+    cfg.update(overrides)
+    return DictDefault(cfg)
+
+
+class TestFSDP2LoRAKernels:
+    """Test LoRA kernels under FSDP2."""
+
+    @require_torch_2_7_0
+    def test_lora_kernels_basic(self, temp_dir):
+        """Basic LoRA + kernels + FSDP2: no dropout, no bias, no DoRA."""
+        cfg = _base_lora_fsdp2_config(temp_dir)
+        _run_training(temp_dir, cfg)
+        assert (Path(temp_dir) / "adapter_model.safetensors").exists()
+
+    @require_torch_2_7_0
+    def test_lora_kernels_with_dropout(self, temp_dir):
+        """LoRA kernels + dropout + FSDP2."""
+        cfg = _base_lora_fsdp2_config(temp_dir, lora_dropout=0.1)
+        _run_training(temp_dir, cfg)
+        assert (Path(temp_dir) / "adapter_model.safetensors").exists()
+
+    @require_torch_2_7_0
+    def test_lora_kernels_with_dora(self, temp_dir):
+        """LoRA kernels + DoRA + FSDP2."""
+        cfg = _base_lora_fsdp2_config(temp_dir, peft_use_dora=True)
+        _run_training(temp_dir, cfg)
+        assert (Path(temp_dir) / "adapter_model.safetensors").exists()
+
+    @require_torch_2_7_0
+    def test_lora_kernels_with_dora_and_dropout(self, temp_dir):
+        """LoRA kernels + DoRA + dropout + FSDP2."""
+        cfg = _base_lora_fsdp2_config(
+            temp_dir,
+            peft_use_dora=True,
+            lora_dropout=0.05,
+        )
+        _run_training(temp_dir, cfg)
+        assert (Path(temp_dir) / "adapter_model.safetensors").exists()

tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py

@@ -222,9 +222,9 @@ def test_model_specific_activation(model_name, expected_activation):
 
 
 def test_kernel_patch_conditions():
-    """Test various conditions that should prevent kernel patching."""
+    """Test that kernels ARE patched even with dropout and bias (now supported)."""
     test_configs = [
-        # Dropout prevents patching
+        # Dropout — kernels now support this
         {
             "peft_type": "LORA",
             "task_type": "CAUSAL_LM",
@@ -234,7 +234,7 @@ def test_kernel_patch_conditions():
             "lora_dropout": 0.1,
             "bias": "none",
         },
-        # Bias prevents patching
+        # Bias — kernels now support this
         {
             "peft_type": "LORA",
             "task_type": "CAUSAL_LM",
@@ -252,13 +252,14 @@ def test_kernel_patch_conditions():
         model = PeftModelForCausalLM(model, peft_config)
         cfg = DictDefault({"lora_mlp_kernel": True})
 
-        # Should not patch
         patched_model = apply_lora_kernel_patches(model, cfg)
         layer = patched_model.model.model.layers[0].mlp
 
-        # Verify no patches applied
-        assert layer.forward.__func__ is not apply_lora_mlp_swiglu
-        assert layer.forward.__func__ is not apply_lora_mlp_geglu
+        # Verify patches ARE applied (dropout and bias are now supported)
+        assert (
+            layer.forward.__func__ is apply_lora_mlp_swiglu
+            or layer.forward.__func__ is apply_lora_mlp_geglu
+        )
 
 
 def test_kernel_config_options():
@@ -511,7 +512,7 @@ def test_kernel_training_integration_auto_enable(temp_dir):
 
 
 def test_kernel_training_integration_dropout_non_zero(temp_dir):
-    """Test model loading with dropout non-zero should not patch."""
+    """Test model loading with dropout non-zero DOES patch (now supported)."""
 
     from axolotl.cli.utils import load_model_and_tokenizer
 
@@ -546,31 +547,18 @@ def test_kernel_training_integration_dropout_non_zero(temp_dir):
     # Load config
     cfg = load_cfg(str(path))
 
-    # Get original attention class
-    attention_cls = get_attention_cls_from_config(cfg)
-
-    # Store original state before patching
-    original_forward_method = attention_cls.forward
-
     # Load model
     model, tokenizer, _ = load_model_and_tokenizer(cfg=cfg)
 
-    # We call modelloader as that's where the patches are applied
-    # despite the fact that we're not using it to load the model
     model_loader = ModelLoader(cfg, tokenizer)
 
-    # Apply patch
+    # Apply patches — should succeed even with dropout > 0
     model_loader.patch_manager._apply_self_attention_lora_patch()
-
-    # Verify patch was not applied
-    assert attention_cls.forward == original_forward_method
-
-    # Apply apply_lora_kernel_patches
     model_loader.patch_manager._apply_lora_kernel_patch(model)
 
-    # Verify patch was not applied
+    # Verify patches WERE applied (dropout is now supported by kernels)
     layers = get_layers(model)
     for layer in layers:
         for self_attn in find_self_attn_in_layer(layer):
-            assert not hasattr(self_attn, "apply_qkv")
-            assert not hasattr(self_attn, "apply_o")
+            assert hasattr(self_attn, "apply_qkv")
+            assert hasattr(self_attn, "apply_o")

tests/e2e/patched/test_resume.py

@@ -9,8 +9,8 @@ import subprocess
 from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.common.datasets import load_datasets
+from axolotl.core.trainers.constants import TOKENS_STATE_FILE
 from axolotl.train import train
-from axolotl.utils.callbacks.tokens_per_second import TOKENS_STATE_FILE
 from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.dict import DictDefault
 

tests/e2e/test_deepseekv3.py

@@ -14,6 +14,9 @@ from axolotl.utils.dict import DictDefault
 from tests.hf_offline_utils import enable_hf_offline
 
 
+@pytest.mark.skip(
+    reason="DeepSeek-V3-11M remote model code needs _supports_flash_attn=True for newer transformers"
+)
 class TestDeepseekV3:
     """
     Test case for DeepseekV3 models

tests/e2e/test_dpo.py

@@ -262,6 +262,7 @@ class TestDPOLlamaLora(unittest.TestCase):
         train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(Path(temp_dir) / "checkpoint-20", cfg)
 
+    @pytest.mark.skip(reason="TRL ORPO trainer has internal zip() length mismatch bug")
     @with_temp_dir
     def test_orpo_lora(self, temp_dir):
         cfg = DictDefault(

tests/e2e/test_mixtral.py

@@ -70,7 +70,7 @@ class TestMixtral(unittest.TestCase):
 
         model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
         assert (
-            model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
+            model.base_model.model.model.layers[0].mlp.gate.weight.dtype
             == torch.float32
         )
         check_model_output_exists(temp_dir, cfg)
@@ -125,7 +125,7 @@ class TestMixtral(unittest.TestCase):
 
         model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
         assert (
-            model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
+            model.base_model.model.model.layers[0].mlp.gate.weight.dtype
             == torch.float32
         )
         check_model_output_exists(temp_dir, cfg)
@@ -183,7 +183,7 @@ class TestMixtral(unittest.TestCase):
 
         model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
         assert (
-            model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
+            model.base_model.model.model.layers[0].mlp.gate.weight.dtype
             == torch.float32
         )
         check_model_output_exists(temp_dir, cfg)
@@ -241,7 +241,7 @@ class TestMixtral(unittest.TestCase):
 
         model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
         assert (
-            model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
+            model.base_model.model.model.layers[0].mlp.gate.weight.dtype
             == torch.float32
         )
         check_model_output_exists(temp_dir, cfg)

tests/e2e/test_optimizers.py

@@ -4,6 +4,8 @@ E2E tests for custom optimizers using Llama
 
 import unittest
 
+import pytest
+
 from axolotl.common.datasets import load_datasets
 from axolotl.train import train
 from axolotl.utils.config import normalize_config, validate_config
@@ -282,3 +284,60 @@ class TestCustomOptimizers(unittest.TestCase):
 
         train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)
+
+
+@require_torch_2_7_0
+@pytest.mark.parametrize(
+    "optimizer_name,expected_class,learning_rate",
+    [
+        ("flash_adamw", "FlashAdamW", 0.00001),
+        ("flash_adam", "FlashAdam", 0.00001),
+        ("flash_sgd", "FlashSGD", 0.01),
+        ("flash_sgdw", "FlashSGDW", 0.01),
+        ("flash_lion", "FlashLion", 0.0001),
+    ],
+)
+def test_flash_optimizers(tmp_path, optimizer_name, expected_class, learning_rate):
+    pytest.importorskip("flashoptim")
+    temp_dir = str(tmp_path)
+    cfg = DictDefault(
+        {
+            "base_model": "HuggingFaceTB/SmolLM2-135M",
+            "model_type": "AutoModelForCausalLM",
+            "tokenizer_type": "AutoTokenizer",
+            "sequence_len": 1024,
+            "load_in_8bit": True,
+            "adapter": "lora",
+            "lora_r": 8,
+            "lora_alpha": 16,
+            "lora_dropout": 0.05,
+            "lora_target_linear": True,
+            "val_set_size": 0.02,
+            "special_tokens": {
+                "pad_token": "<|endoftext|>",
+            },
+            "datasets": [
+                {
+                    "path": "mhenrichsen/alpaca_2k_test",
+                    "type": "alpaca",
+                },
+            ],
+            "num_epochs": 1,
+            "micro_batch_size": 8,
+            "gradient_accumulation_steps": 1,
+            "output_dir": temp_dir,
+            "learning_rate": learning_rate,
+            "optimizer": optimizer_name,
+            "max_steps": 5,
+            "lr_scheduler": "cosine",
+            "save_first_step": False,
+        }
+    )
+
+    cfg = validate_config(cfg)
+    normalize_config(cfg)
+    dataset_meta = load_datasets(cfg=cfg)
+
+    _, _, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
+    check_model_output_exists(temp_dir, cfg)
+    assert trainer.optimizer.optimizer.__class__.__name__ == expected_class

tests/e2e/test_quantization.py

@@ -35,6 +35,14 @@ from tests.e2e.utils import (
 )
 
 
+def _get_fake_quant_config_dtype(config):
+    """Get the weight dtype from a fake quantize config, handling different config types."""
+    if hasattr(config, "dtype"):
+        return config.dtype
+    # Int4WeightFakeQuantizeConfig doesn't have .dtype — weight is always int4
+    return torch.int4
+
+
 @pytest.fixture()
 def model():
     dummy_model = AutoModelForCausalLM.from_pretrained(
@@ -157,6 +165,18 @@ class TestQuantization:
         expected_exception,
         expected_tensor_class,
     ):
+        # TODO: add mslk-cuda as a CI dependency once pytorch 2.10.x is available
+        # (see https://pypi.org/project/mslk-cuda/)
+        if expected_tensor_class is Int4Tensor and activation_dtype is None:
+            try:
+                from torchao.quantization.quantize_.workflows.int4.int4_tensor import (
+                    int4_row_quantize_zp,
+                )
+
+                if int4_row_quantize_zp is None:
+                    pytest.skip("Int4Tensor requires mslk >= 1.0.0")
+            except ImportError:
+                pytest.skip("Int4Tensor requires mslk >= 1.0.0")
         if expected_exception:
             with pytest.raises(expected_exception):
                 quantize_model(
@@ -252,28 +272,24 @@ class TestQuantization:
         if quantize_embedding:
             assert isinstance(model.model.embed_tokens, FakeQuantizedEmbedding)
             assert hasattr(model.model.embed_tokens, "weight_fake_quantizer")
-            assert (
-                model.model.embed_tokens.weight_fake_quantizer.config.dtype
-                == weight_dtype.value
-            )
+            embed_config = model.model.embed_tokens.weight_fake_quantizer.config
+            assert _get_fake_quant_config_dtype(embed_config) == weight_dtype.value
             if group_size:
-                assert (
-                    model.model.embed_tokens.weight_fake_quantizer.config.group_size
-                    == group_size
-                )
+                assert embed_config.group_size == group_size
 
         for child in list(model.children()):
             if isinstance(child, torch.nn.Linear):
                 assert isinstance(child, FakeQuantizedLinear)
                 assert hasattr(child, "weight_fake_quantizer")
-                assert child.weight_fake_quantizer.config.dtype == weight_dtype.value
+                w_config = child.weight_fake_quantizer.config
+                assert _get_fake_quant_config_dtype(w_config) == weight_dtype.value
                 if group_size:
-                    assert child.weight_fake_quantizer.config.group_size == group_size
+                    assert w_config.group_size == group_size
                 if activation_dtype:
                     assert hasattr(child, "activation_fake_quantizer")
+                    a_config = child.activation_fake_quantizer.config
                     assert (
-                        child.activation_fake_quantizer.config.dtype
-                        == activation_dtype.value
+                        _get_fake_quant_config_dtype(a_config) == activation_dtype.value
                     )
                 else:
                     assert child.activation_fake_quantizer is None
@@ -374,9 +390,16 @@ class TestQuantizationCallback:
 
         # ensure model has been quantized
         assert isinstance(model.model.embed_tokens, FakeQuantizedEmbedding)
-        assert model.model.embed_tokens.weight_fake_quantizer.enabled
         assert isinstance(model.lm_head, FakeQuantizedLinear)
-        assert model.lm_head.weight_fake_quantizer.enabled
+
+        # Only test enable/disable toggling if the fake quantizer supports it
+        # (Int4WeightFakeQuantizer does not have an 'enabled' attribute)
+        supports_toggle = hasattr(
+            model.model.embed_tokens.weight_fake_quantizer, "enabled"
+        )
+        if supports_toggle:
+            assert model.model.embed_tokens.weight_fake_quantizer.enabled
+            assert model.lm_head.weight_fake_quantizer.enabled
 
         qat_callback = QATCallback(cfg)
 
@@ -388,9 +411,10 @@ class TestQuantizationCallback:
             model=model,
         )
 
-        # quantization should have been disabled
-        assert not model.model.embed_tokens.weight_fake_quantizer.enabled
-        assert not model.lm_head.weight_fake_quantizer.enabled
+        if supports_toggle:
+            # quantization should have been disabled
+            assert not model.model.embed_tokens.weight_fake_quantizer.enabled
+            assert not model.lm_head.weight_fake_quantizer.enabled
 
         trainer_state.global_step = 100
         qat_callback.on_step_begin(
@@ -400,9 +424,10 @@ class TestQuantizationCallback:
             model=model,
         )
 
-        # quantization should have been enabled
-        assert model.model.embed_tokens.weight_fake_quantizer.enabled
-        assert model.lm_head.weight_fake_quantizer.enabled
+        if supports_toggle:
+            # quantization should have been enabled
+            assert model.model.embed_tokens.weight_fake_quantizer.enabled
+            assert model.lm_head.weight_fake_quantizer.enabled
 
     @require_torch_2_8_0
     def test_qat_callback_fake_quant_after_n_steps_is_none(self, model, trainer_state):
@@ -424,9 +449,10 @@ class TestQuantizationCallback:
 
         # ensure model has been quantized
         assert isinstance(model.model.embed_tokens, FakeQuantizedEmbedding)
-        assert model.model.embed_tokens.weight_fake_quantizer.enabled
         assert isinstance(model.lm_head, FakeQuantizedLinear)
-        assert model.lm_head.weight_fake_quantizer.enabled
+        if hasattr(model.model.embed_tokens.weight_fake_quantizer, "enabled"):
+            assert model.model.embed_tokens.weight_fake_quantizer.enabled
+            assert model.lm_head.weight_fake_quantizer.enabled
 
         qat_callback = QATCallback(cfg)
         # simulate first training step
@@ -438,5 +464,6 @@ class TestQuantizationCallback:
         )
 
         # quantization should be enabled from the get-go
-        assert model.model.embed_tokens.weight_fake_quantizer.enabled
-        assert model.lm_head.weight_fake_quantizer.enabled
+        if hasattr(model.model.embed_tokens.weight_fake_quantizer, "enabled"):
+            assert model.model.embed_tokens.weight_fake_quantizer.enabled
+            assert model.lm_head.weight_fake_quantizer.enabled

tests/e2e/utils.py

@@ -179,7 +179,7 @@ def check_tensorboard(
     tag: str,
     lt_val: float,
     assertion_err: str,
-    rtol: float = 0.02,
+    rtol: float = 0.05,
     gt_zero: bool = True,
 ) -> None:
     """

tests/kernels/test_rms_norm_gated.py

@@ -0,0 +1,229 @@
+"""
+Correctness tests for fused RMSNorm + SiLU Gate kernel.
+
+Tests against the eager Qwen3_5RMSNormGated implementation.
+"""
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+pytest.importorskip("triton", reason="triton required for fused kernels")
+
+if not torch.cuda.is_available():
+    pytest.skip("CUDA required for fused kernel tests", allow_module_level=True)
+
+from axolotl.kernels.rms_norm_gated import FusedRMSNormGated
+
+
+class EagerRMSNormGated(torch.nn.Module):
+    """Reference implementation matching Qwen3_5RMSNormGated exactly."""
+
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states, gate=None):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        hidden_states = self.weight * hidden_states.to(input_dtype)
+        hidden_states = hidden_states * F.silu(gate.to(torch.float32))
+        return hidden_states.to(input_dtype)
+
+
+def _sync_weights(eager_mod, fused_mod):
+    """Copy weights from eager to fused module."""
+    fused_mod.weight.data.copy_(eager_mod.weight.data)
+
+
+@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16, torch.float16])
+@pytest.mark.parametrize(
+    "shape",
+    [
+        (2, 128, 256),
+        (4, 64, 512),
+        (1, 32, 1024),
+        (2, 16, 2560),  # Qwen3.5-4B hidden_size
+        (2, 16, 4096),  # Qwen3.5-9B hidden_size
+        (1, 8, 5120),  # Qwen3.5-27B hidden_size
+        (4, 16, 2048),  # Qwen3.5-35B-A3B (MoE) hidden_size
+        (4, 16, 3072),  # Qwen3.5-122B-A10B (MoE) hidden_size
+    ],
+)
+class TestRMSNormGatedForward:
+    def test_output_matches_eager(self, dtype, shape):
+        torch.manual_seed(42)
+        B, T, H = shape
+        X = torch.randn(B, T, H, dtype=dtype, device="cuda")
+        G = torch.randn(B, T, H, dtype=dtype, device="cuda")
+
+        eager = EagerRMSNormGated(H).to(dtype=dtype, device="cuda")
+        fused = FusedRMSNormGated(H).to(dtype=dtype, device="cuda")
+        _sync_weights(eager, fused)
+
+        y_eager = eager(X, gate=G)
+        y_fused = fused(X, gate=G)
+
+        if dtype == torch.float32:
+            torch.testing.assert_close(y_fused, y_eager, atol=1e-5, rtol=1e-5)
+        else:
+            torch.testing.assert_close(y_fused, y_eager, atol=1e-2, rtol=1e-2)
+
+    def test_output_shape(self, dtype, shape):
+        B, T, H = shape
+        X = torch.randn(B, T, H, dtype=dtype, device="cuda")
+        G = torch.randn(B, T, H, dtype=dtype, device="cuda")
+
+        fused = FusedRMSNormGated(H).to(dtype=dtype, device="cuda")
+        y = fused(X, gate=G)
+        assert y.shape == (B, T, H)
+        assert y.dtype == dtype
+
+
+@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16, torch.float16])
+@pytest.mark.parametrize(
+    "shape",
+    [
+        (2, 32, 256),
+        (2, 16, 512),
+        (2, 16, 2560),  # Qwen3.5-4B
+        (1, 8, 4096),  # Qwen3.5-9B
+        (1, 8, 5120),  # Qwen3.5-27B
+        (2, 16, 2048),  # Qwen3.5-35B-A3B (MoE)
+        (2, 16, 3072),  # Qwen3.5-122B-A10B (MoE)
+    ],
+)
+class TestRMSNormGatedBackward:
+    def test_grad_x(self, dtype, shape):
+        torch.manual_seed(42)
+        B, T, H = shape
+        X = torch.randn(B, T, H, dtype=dtype, device="cuda", requires_grad=True)
+        G = torch.randn(B, T, H, dtype=dtype, device="cuda", requires_grad=True)
+        X_ref = X.detach().clone().requires_grad_(True)
+        G_ref = G.detach().clone().requires_grad_(True)
+
+        eager = EagerRMSNormGated(H).to(dtype=dtype, device="cuda")
+        fused = FusedRMSNormGated(H).to(dtype=dtype, device="cuda")
+        _sync_weights(eager, fused)
+
+        y_eager = eager(X_ref, gate=G_ref)
+        y_fused = fused(X, gate=G)
+
+        grad_out = torch.randn_like(y_eager)
+        y_eager.backward(grad_out)
+        y_fused.backward(grad_out)
+
+        if dtype == torch.float32:
+            atol, rtol = 1e-4, 1e-4
+        else:
+            atol, rtol = 5e-2, 5e-2
+
+        torch.testing.assert_close(X.grad, X_ref.grad, atol=atol, rtol=rtol)
+
+    def test_grad_gate(self, dtype, shape):
+        torch.manual_seed(42)
+        B, T, H = shape
+        X = torch.randn(B, T, H, dtype=dtype, device="cuda", requires_grad=True)
+        G = torch.randn(B, T, H, dtype=dtype, device="cuda", requires_grad=True)
+        X_ref = X.detach().clone().requires_grad_(True)
+        G_ref = G.detach().clone().requires_grad_(True)
+
+        eager = EagerRMSNormGated(H).to(dtype=dtype, device="cuda")
+        fused = FusedRMSNormGated(H).to(dtype=dtype, device="cuda")
+        _sync_weights(eager, fused)
+
+        y_eager = eager(X_ref, gate=G_ref)
+        y_fused = fused(X, gate=G)
+
+        grad_out = torch.randn_like(y_eager)
+        y_eager.backward(grad_out)
+        y_fused.backward(grad_out)
+
+        if dtype == torch.float32:
+            atol, rtol = 1e-4, 1e-4
+        else:
+            atol, rtol = 5e-2, 5e-2
+
+        torch.testing.assert_close(G.grad, G_ref.grad, atol=atol, rtol=rtol)
+
+    def test_grad_weight(self, dtype, shape):
+        torch.manual_seed(42)
+        B, T, H = shape
+        X = torch.randn(B, T, H, dtype=dtype, device="cuda", requires_grad=True)
+        G = torch.randn(B, T, H, dtype=dtype, device="cuda", requires_grad=True)
+        X_ref = X.detach().clone().requires_grad_(True)
+        G_ref = G.detach().clone().requires_grad_(True)
+
+        eager = EagerRMSNormGated(H).to(dtype=dtype, device="cuda")
+        fused = FusedRMSNormGated(H).to(dtype=dtype, device="cuda")
+        _sync_weights(eager, fused)
+
+        y_eager = eager(X_ref, gate=G_ref)
+        y_fused = fused(X, gate=G)
+
+        grad_out = torch.randn_like(y_eager)
+        y_eager.backward(grad_out)
+        y_fused.backward(grad_out)
+
+        if dtype == torch.float32:
+            atol, rtol = 1e-4, 1e-4
+        else:
+            atol, rtol = 5e-2, 5e-2
+
+        torch.testing.assert_close(
+            fused.weight.grad, eager.weight.grad, atol=atol, rtol=rtol
+        )
+
+
+class TestRMSNormGatedEdgeCases:
+    def test_gate_none_raises(self):
+        fused = FusedRMSNormGated(256).cuda()
+        X = torch.randn(2, 4, 256, device="cuda")
+        with pytest.raises(ValueError, match="requires a gate tensor"):
+            fused(X, gate=None)
+
+    def test_2d_input(self):
+        """Test with (BxT, H) shaped input instead of (B, T, H)."""
+        torch.manual_seed(42)
+        H = 512
+        X = torch.randn(64, H, dtype=torch.bfloat16, device="cuda", requires_grad=True)
+        G = torch.randn(64, H, dtype=torch.bfloat16, device="cuda", requires_grad=True)
+        X_ref = X.detach().clone().requires_grad_(True)
+        G_ref = G.detach().clone().requires_grad_(True)
+
+        eager = EagerRMSNormGated(H).to(dtype=torch.bfloat16, device="cuda")
+        fused = FusedRMSNormGated(H).to(dtype=torch.bfloat16, device="cuda")
+        _sync_weights(eager, fused)
+
+        y_eager = eager(X_ref, gate=G_ref)
+        y_fused = fused(X, gate=G)
+
+        torch.testing.assert_close(y_fused, y_eager, atol=1e-2, rtol=1e-2)
+
+        grad_out = torch.randn_like(y_eager)
+        y_eager.backward(grad_out)
+        y_fused.backward(grad_out)
+
+        torch.testing.assert_close(X.grad, X_ref.grad, atol=5e-2, rtol=5e-2)
+        torch.testing.assert_close(G.grad, G_ref.grad, atol=5e-2, rtol=5e-2)
+
+    def test_random_weight_init(self):
+        """Test with non-default weight values."""
+        torch.manual_seed(123)
+        H = 256
+        X = torch.randn(2, 16, H, dtype=torch.bfloat16, device="cuda")
+        G = torch.randn(2, 16, H, dtype=torch.bfloat16, device="cuda")
+
+        eager = EagerRMSNormGated(H).to(dtype=torch.bfloat16, device="cuda")
+        # Randomize weights
+        eager.weight.data = torch.randn_like(eager.weight.data)
+
+        fused = FusedRMSNormGated(H).to(dtype=torch.bfloat16, device="cuda")
+        _sync_weights(eager, fused)
+
+        y_eager = eager(X, gate=G)
+        y_fused = fused(X, gate=G)
+        torch.testing.assert_close(y_fused, y_eager, atol=1e-2, rtol=1e-2)

tests/patched/test_validation.py

@@ -13,6 +13,7 @@ from axolotl.utils.config import validate_config
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
 from axolotl.utils.schemas.config import AxolotlConfigWCapabilities
+from axolotl.utils.schemas.datasets import SFTDataset
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 warnings.filterwarnings("error")
@@ -1731,3 +1732,52 @@ class TestDataloaderValidation(BaseValidation):
         assert new_cfg.dataloader_num_workers == 8
         assert new_cfg.dataloader_pin_memory is True
         assert new_cfg.dataloader_prefetch_factor == 256
+
+
+class TestSyntheticDatasetValidation(BaseValidation):
+    """
+    Tests for synthetic dataset config validation
+    """
+
+    @staticmethod
+    def _make_cfg(minimal_cfg, datasets):
+        raw = dict(minimal_cfg)
+        raw["datasets"] = datasets
+        return DictDefault(raw)
+
+    def test_synthetic_dict_config_validates(self, minimal_cfg):
+        """Synthetic dataset passed as a raw dict should not raise."""
+        cfg = self._make_cfg(
+            minimal_cfg,
+            [
+                {
+                    "path": "synthetic",
+                    "type": "_synthetic",
+                    "length": 100,
+                    "sequence_length": 64,
+                }
+            ],
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg.datasets[0]["path"] == "synthetic"
+
+    def test_synthetic_already_sft_does_not_crash(self, minimal_cfg):
+        """Synthetic dataset already parsed as SFTDataset should not raise AttributeError."""
+        sft = SFTDataset(path="synthetic", type="_synthetic")
+        cfg = self._make_cfg(minimal_cfg, [sft])
+
+        # Before the fix, this raised:
+        #   AttributeError: 'SFTDataset' object has no attribute 'get'
+        new_cfg = validate_config(cfg)
+        assert new_cfg.datasets[0]["path"] == "synthetic"
+
+    def test_non_synthetic_sft_validates(self, minimal_cfg):
+        """A regular SFT dataset should validate without being treated as synthetic."""
+        cfg = self._make_cfg(
+            minimal_cfg,
+            [{"path": "mhenrichsen/alpaca_2k_test", "type": "alpaca"}],
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg.datasets[0]["path"] == "mhenrichsen/alpaca_2k_test"

tests/prompt_strategies/test_synthetic.py

@@ -0,0 +1,125 @@
+"""Tests for the synthetic dataset generator."""
+
+import unittest
+from unittest.mock import MagicMock
+
+from datasets import Dataset
+
+from axolotl.prompt_strategies._synthetic import SyntheticDatasetStrategy, load
+from axolotl.utils.dict import DictDefault
+
+
+class TestSyntheticDatasetStrategy(unittest.TestCase):
+    def test_generates_correct_shape(self):
+        strategy = SyntheticDatasetStrategy(
+            sequence_length=128,
+            length=50,
+            min_input_id=1,
+            max_input_id=1000,
+            seed=42,
+        )
+        dummy = Dataset.from_dict({"text": [""]})
+        result = strategy.wrap_dataset(dummy)
+
+        assert len(result) == 50
+        assert len(result[0]["input_ids"]) == 128
+        assert len(result[0]["attention_mask"]) == 128
+        assert len(result[0]["labels"]) == 128
+
+    def test_attention_mask_all_ones(self):
+        strategy = SyntheticDatasetStrategy(sequence_length=64, length=10, seed=0)
+        dummy = Dataset.from_dict({"text": [""]})
+        result = strategy.wrap_dataset(dummy)
+
+        for row in result:
+            assert all(v == 1 for v in row["attention_mask"])
+
+    def test_labels_equal_input_ids(self):
+        strategy = SyntheticDatasetStrategy(sequence_length=64, length=10, seed=0)
+        dummy = Dataset.from_dict({"text": [""]})
+        result = strategy.wrap_dataset(dummy)
+
+        for row in result:
+            assert row["input_ids"] == row["labels"]
+
+    def test_input_id_range(self):
+        strategy = SyntheticDatasetStrategy(
+            sequence_length=64,
+            length=100,
+            min_input_id=500,
+            max_input_id=600,
+            seed=42,
+        )
+        dummy = Dataset.from_dict({"text": [""]})
+        result = strategy.wrap_dataset(dummy)
+
+        for row in result:
+            for token_id in row["input_ids"]:
+                assert 500 <= token_id < 600
+
+    def test_seed_reproducibility(self):
+        kwargs = dict(
+            sequence_length=64, length=20, min_input_id=1, max_input_id=1000, seed=123
+        )
+        dummy = Dataset.from_dict({"text": [""]})
+
+        result1 = SyntheticDatasetStrategy(**kwargs).wrap_dataset(dummy)
+        result2 = SyntheticDatasetStrategy(**kwargs).wrap_dataset(dummy)
+
+        for r1, r2 in zip(result1, result2, strict=True):
+            assert r1["input_ids"] == r2["input_ids"]
+
+    def test_different_seeds_differ(self):
+        common = dict(sequence_length=64, length=20, min_input_id=1, max_input_id=1000)
+        dummy = Dataset.from_dict({"text": [""]})
+
+        result1 = SyntheticDatasetStrategy(seed=1, **common).wrap_dataset(dummy)
+        result2 = SyntheticDatasetStrategy(seed=2, **common).wrap_dataset(dummy)
+
+        any_different = any(
+            r1["input_ids"] != r2["input_ids"]
+            for r1, r2 in zip(result1, result2, strict=True)
+        )
+        assert any_different
+
+    def test_load_function_with_ds_cfg(self):
+        tokenizer = MagicMock()
+        tokenizer.vocab_size = 32000
+        cfg = DictDefault({"sequence_len": 512, "train_on_inputs": False})
+        ds_cfg = {
+            "sequence_length": 256,
+            "length": 5,
+            "min_input_id": 10,
+            "max_input_id": 100,
+            "seed": 0,
+        }
+
+        strategy = load(tokenizer, cfg, ds_cfg=ds_cfg)
+        assert isinstance(strategy, SyntheticDatasetStrategy)
+        assert strategy.sequence_length == 256
+        assert strategy.length == 5
+        assert strategy.min_input_id == 10
+        assert strategy.max_input_id == 100
+
+    def test_load_defaults_from_cfg(self):
+        tokenizer = MagicMock()
+        tokenizer.vocab_size = 32000
+        cfg = DictDefault({"sequence_len": 1024, "train_on_inputs": False})
+
+        strategy = load(tokenizer, cfg, ds_cfg={})
+        assert strategy.sequence_length == 1024
+        assert strategy.max_input_id == 32000
+        assert strategy.length == 1000
+
+    def test_load_with_no_ds_cfg(self):
+        tokenizer = MagicMock()
+        tokenizer.vocab_size = 50000
+        cfg = DictDefault({"sequence_len": 2048, "train_on_inputs": False})
+
+        strategy = load(tokenizer, cfg)
+        assert strategy.sequence_length == 2048
+        assert strategy.max_input_id == 50000
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_expand_mask.py

@@ -1,45 +0,0 @@
-"""
-Unit tests for the monkey patch for expand mask to handle packed sequences
-"""
-
-import unittest
-
-import torch
-
-from axolotl.monkeypatch.llama_expand_mask import _expand_mask
-
-
-class TestExpandMask(unittest.TestCase):
-    """
-    Test class for attention mask expansion for packed sequences
-    """
-
-    def test_output(self):
-        mask = torch.tensor([[1, 1, 1, 2], [2, 3, 3, 0]])
-        dtype = torch.float32
-        expected_output = torch.tensor(
-            [
-                [
-                    [
-                        [0.0000e00, -3.4028e38, -3.4028e38, -3.4028e38],
-                        [0.0000e00, 0.0000e00, -3.4028e38, -3.4028e38],
-                        [0.0000e00, 0.0000e00, 0.0000e00, -3.4028e38],
-                        [-3.4028e38, -3.4028e38, -3.4028e38, 0.0000e00],
-                    ]
-                ],
-                [
-                    [
-                        [0.0000e00, -3.4028e38, -3.4028e38, -3.4028e38],
-                        [-3.4028e38, 0.0000e00, -3.4028e38, -3.4028e38],
-                        [-3.4028e38, 0.0000e00, 0.0000e00, -3.4028e38],
-                        [-3.4028e38, -3.4028e38, -3.4028e38, -3.4028e38],
-                    ]
-                ],
-            ]
-        )
-        # Check that the output matches the expected output
-        self.assertTrue(torch.allclose(_expand_mask(mask, dtype), expected_output))
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/utils/lora/test_config_validation_lora.py

@@ -28,20 +28,22 @@ class TestLoRAConfigValidation:
         result = validate_config(valid_config)
         assert result["adapter"] == "lora"
 
-        with pytest.raises(ValueError, match="not compatible with DoRA"):
-            invalid_config = DictDefault(
-                {
-                    "adapter": "lora",
-                    "lora_mlp_kernel": True,
-                    "peft_use_dora": True,
-                    "datasets": [{"path": "dummy_dataset", "type": "alpaca"}],
-                    "micro_batch_size": 1,
-                    "gradient_accumulation_steps": 1,
-                    "learning_rate": 1e-5,
-                    "base_model": "dummy_model",
-                }
-            )
-            validate_config(invalid_config)
+        # DoRA is now compatible with lora kernels
+        dora_kernel_config = DictDefault(
+            {
+                "adapter": "lora",
+                "lora_mlp_kernel": True,
+                "peft_use_dora": True,
+                "datasets": [{"path": "dummy_dataset", "type": "alpaca"}],
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "learning_rate": 1e-5,
+                "base_model": "dummy_model",
+            }
+        )
+        result = validate_config(dora_kernel_config)
+        assert result["lora_mlp_kernel"] is True
+        assert result["peft_use_dora"] is True
 
     def test_qlora_4bit_validation(self):
         """Test QLoRA 4-bit configuration validation"""

tests/utils/lora/test_freeze_lora.py

@@ -38,6 +38,11 @@ class TestLoRAParameterFreezing:
 
             mock_layer.lora_A["default"].weight = torch.randn(16, 256, dtype=self.dtype)
             mock_layer.lora_B["default"].weight = torch.randn(512, 16, dtype=self.dtype)
+            mock_layer.lora_B["default"].bias = None
+
+            # Required by get_lora_parameters for dropout/DoRA extraction
+            mock_layer.lora_dropout = {}
+            mock_layer.lora_magnitude_vector = None
         else:
             mock_layer.weight = base_layer.weight
             mock_layer.bias = base_layer.bias
@@ -48,7 +53,7 @@ class TestLoRAParameterFreezing:
         """Test that LoRA parameters are None when adapters are disabled."""
         layer = self.create_mock_lora_layer(has_adapters=True, adapters_disabled=True)
 
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
+        W, b, quant_state, A, B, s, *_ = get_lora_parameters(layer)
 
         # Base parameters should be returned
         assert W is not None
@@ -62,7 +67,7 @@ class TestLoRAParameterFreezing:
         """Test that LoRA parameters are None when adapters are merged."""
         layer = self.create_mock_lora_layer(has_adapters=True, merged=True)
 
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
+        W, b, quant_state, A, B, s, *_ = get_lora_parameters(layer)
 
         # Base parameters should be returned
         assert W is not None
@@ -77,7 +82,7 @@ class TestLoRAParameterFreezing:
         """Test parameter behavior when no adapters are present."""
         layer = self.create_mock_lora_layer(has_adapters=False)
 
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
+        W, b, quant_state, A, B, s, *_ = get_lora_parameters(layer)
 
         # Base parameters should be returned
         assert W is not None
@@ -94,7 +99,7 @@ class TestLoRAParameterFreezing:
             has_adapters=True, adapters_disabled=False, merged=False
         )
 
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
+        W, b, quant_state, A, B, s, *_ = get_lora_parameters(layer)
 
         # All parameters should be returned
         assert W is not None
@@ -110,7 +115,7 @@ class TestLoRAParameterFreezing:
             has_adapters=True, adapters_disabled=False, merged=False
         )
 
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
+        W, b, quant_state, A, B, s, *_ = get_lora_parameters(layer)
 
         # Check shape consistency
         assert W.shape == (512, 256)
@@ -124,7 +129,7 @@ class TestLoRAParameterFreezing:
             has_adapters=True, adapters_disabled=False, merged=False
         )
 
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
+        W, b, quant_state, A, B, s, *_ = get_lora_parameters(layer)
 
         assert W.dtype == self.dtype
         assert b.dtype == self.dtype
@@ -138,7 +143,7 @@ class TestLoRAParameterFreezing:
         quant_state_mock = Mock()
         layer.base_layer.weight.quant_state = quant_state_mock
 
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
+        W, b, quant_state, A, B, s, *_ = get_lora_parameters(layer)
 
         assert quant_state == quant_state_mock
 
@@ -157,7 +162,7 @@ class TestLoRAParameterFreezing:
 
         layer.active_adapters = ["adapter2"]
 
-        W, b, quant_state, A, B, s = get_lora_parameters(layer)
+        W, b, quant_state, A, B, s, *_ = get_lora_parameters(layer)
 
         assert s == 0.2
         assert torch.equal(A, layer.lora_A["adapter2"].weight)
@@ -192,13 +197,13 @@ class TestLoRAParameterFreezingIntegration:
         model = get_peft_model(base_model, lora_config)
         lora_layer = model.base_model.model.linear
         # Test with adapters enabled
-        W, b, quant_state, A, B, s = get_lora_parameters(lora_layer)
+        W, b, quant_state, A, B, s, *_ = get_lora_parameters(lora_layer)
         assert A is not None
         assert B is not None
         assert s is not None
         # Test with adapters disabled
         model.disable_adapter_layers()
-        W, b, quant_state, A, B, s = get_lora_parameters(lora_layer)
+        W, b, quant_state, A, B, s, *_ = get_lora_parameters(lora_layer)
         assert A is None
         assert B is None
         assert s is None