register pressure estimation and pruning for h200/b200

_quarto.yml

@@ -128,9 +128,11 @@ 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/Dockerfile-uv.jinja

@@ -11,7 +11,7 @@ ENV NIGHTLY_BUILD="{{ NIGHTLY_BUILD }}"
 ENV HF_HOME="{{ HF_HOME }}"
 
 RUN apt-get update && \
-    apt-get install -y --allow-change-held-packages vim curl nano zstd libnccl2 libnccl-dev ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm
+    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm
 
 WORKDIR /workspace
 

cicd/Dockerfile.jinja

@@ -12,7 +12,7 @@ ENV HF_HOME="{{ HF_HOME }}"
 ENV AXOLOTL_DATASET_NUM_PROC="8"
 
 RUN apt-get update && \
-    apt-get install -y --allow-change-held-packages vim curl nano zstd libnccl2 libnccl-dev ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm
+    apt-get install -y --allow-change-held-packages vim curl nano libnccl2 libnccl-dev ibverbs-providers ibverbs-utils infiniband-diags librdmacm-dev librdmacm1 rdmacm-utils slurm-wlm
 
 WORKDIR /workspace
 

cicd/cicd.sh

@@ -3,13 +3,11 @@ set -e
 
 python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
 
-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 "${HF_HOME}/hub/" --use-compress-program unzstd --strip-components=1
-# hf download "NousResearch/Meta-Llama-3-8B"
-# hf download "NousResearch/Meta-Llama-3-8B-Instruct"
-# hf download "microsoft/Phi-4-reasoning"
-# hf download "microsoft/Phi-3.5-mini-instruct"
-# hf download "microsoft/Phi-3-medium-128k-instruct"
+# curl -L https://axolotl-ci.b-cdn.net/hf-cache.tar.zst | tar -xpf - -C "${HF_HOME}/hub/"  --use-compress-program unzstd --strip-components=1
+hf download "NousResearch/Meta-Llama-3-8B"
+hf download "NousResearch/Meta-Llama-3-8B-Instruct"
+hf download "microsoft/Phi-4-reasoning"
+hf download "microsoft/Phi-3.5-mini-instruct"
 
 # Run unit tests with initial coverage report
 pytest -v --durations=10 -n8 \

cicd/single_gpu.py

@@ -68,6 +68,10 @@ def run_cmd(cmd: str, run_folder: str):
     sp_env["AXOLOTL_DATASET_NUM_PROC"] = "8"
 
     # Propagate errors from subprocess.
-    exit_code = subprocess.call(cmd.split(), cwd=run_folder, env=sp_env)  # nosec
-    if exit_code:
-        raise RuntimeError(f"Command '{cmd}' failed with exit code {exit_code}")
+    try:
+        exit_code = subprocess.call(cmd.split(), cwd=run_folder, env=sp_env)  # nosec
+        if exit_code:
+            print(f"Command '{cmd}' failed with exit code {exit_code}")
+            return exit_code
+    except Exception as e:  # pylint: disable=broad-except
+        print(f"Command '{cmd}' failed with exception {e}")

codecov.yml

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

docs/gradient_checkpointing.qmd

@@ -1,5 +1,5 @@
 ---
-title: Gradient Checkpointing, Activation Offloading, and Layer Offloading
+title: Gradient Checkpointing and Activation Offloading
 ---
 
 Gradient checkpointing and activation offloading are techniques used to optimize the performance of deep learning
@@ -27,33 +27,3 @@ 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/multimodal.qmd

@@ -20,7 +20,6 @@ format:
 - [Gemma-3n](#sec-gemma-3n)
 - [Qwen2-VL](#sec-qwen2-vl)
 - [Qwen2.5-VL](#sec-qwen25-vl)
-- [Qwen3.5](#sec-qwen3-5)
 - [GLM-4.6V](#sec-glm-4-6v)
 - [SmolVLM2](#sec-smolvlm2)
 - [LFM2-VL](#sec-lfm2-vl)
@@ -192,14 +191,6 @@ base_model: Qwen/Qwen3-VL-4B-Instruct
 chat_template: qwen2_vl  # same as qwen2-vl
 ```
 
-### Qwen3.5 {#sec-qwen3-5}
-
-```yaml
-base_model: Qwen/Qwen3.5-9B
-
-chat_template: qwen3_5
-```
-
 ### GLM-4.6V {#sec-glm-4-6v}
 
 Both GLM-4.6V (106B MoE) and GLM-4.6V-Flash (9B) are supported.

docs/optimizations.qmd

@@ -54,13 +54,6 @@ 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/colab-notebooks/colab-axolotl-example.ipynb

@@ -40,7 +40,7 @@
     "%%capture\n",
     "# This step can take ~5-10 minutes to install dependencies\n",
     "!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n",
-    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@63b15e6\""
+    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fa9a7fe\""
    ]
   },
   {

examples/gemma3/gemma-3-1b-qlora.yml

@@ -1,5 +1,8 @@
 base_model: google/gemma-3-1b-it
 
+model_type: Gemma3ForCausalLM
+cls_model_config: Gemma3TextConfig
+
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 
@@ -24,11 +27,6 @@ datasets:
 val_set_size: 0.0
 output_dir: ./outputs/out
 
-# Freeze vision tower
-unfrozen_parameters:
-  - ^model\.language_model\..*
-  - ^lm_head\..*
-
 adapter: qlora
 lora_r: 32
 lora_alpha: 16

examples/gemma3/gemma-3-270m-qlora.yml

@@ -1,5 +1,8 @@
 base_model: google/gemma-3-270m-it
 
+model_type: Gemma3ForCausalLM
+cls_model_config: Gemma3TextConfig
+
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 
@@ -24,11 +27,6 @@ datasets:
 val_set_size: 0.0
 output_dir: ./outputs/out
 
-# Freeze vision tower
-unfrozen_parameters:
-  - ^model\.language_model\..*
-  - ^lm_head\..*
-
 adapter: qlora
 lora_r: 32
 lora_alpha: 16

examples/gemma3/gemma-3-4b-qlora.yml

@@ -1,5 +1,9 @@
 base_model: google/gemma-3-4b-it
 
+# Need to set else transformers tries to load vision too
+model_type: Gemma3ForCausalLM
+cls_model_config: Gemma3TextConfig
+
 load_in_4bit: true
 
 # gemma3 doesn't seem to play nice with ddp
@@ -20,11 +24,6 @@ dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./outputs/out
 
-# Freeze vision tower
-unfrozen_parameters:
-  - ^model\.language_model\..*
-  - ^lm_head\..*
-
 adapter: qlora
 lora_model_dir:
 

examples/mistral4/README.md

@@ -6,6 +6,9 @@ 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: axolotl-ai-co/Mistral-Small-4-119B-2603-BF16
+base_model: mistralai/Mistral-Small-4-119B-2603
 
 plugins:
   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

examples/mistral4/fft-vision.yml

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

examples/mistral4/qlora-text.yml

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

examples/mistral4/qlora-vision.yml

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

examples/nemotron/nemotron-mini-4b-qlora.yaml

@@ -1,57 +0,0 @@
-base_model: nvidia/Nemotron-Mini-4B-Instruct
-
-load_in_8bit: false
-load_in_4bit: true
-
-datasets:
-  - path: fozziethebeat/alpaca_messages_2k_test
-    type: chat_template
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./outputs/nemotron-mini-4b-qlora
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 4096
-sample_packing: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_target_modules:
-  - q_proj
-  - k_proj
-  - v_proj
-  - o_proj
-  - up_proj
-  - down_proj
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-bf16: auto
-tf32: false
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-special_tokens:

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

@@ -1,84 +0,0 @@
-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

@@ -32,11 +32,7 @@ lora_target_modules:
   - 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:
+#lora_target_parameters:
 #   - mlp.experts.gate_up_proj
 #   - mlp.experts.down_proj
 
@@ -56,6 +52,7 @@ learning_rate: 0.0002
 bf16: auto
 tf32: true
 
+
 lora_mlp_kernel: false
 lora_qkv_kernel: false
 lora_o_kernel: false

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

@@ -1,59 +0,0 @@
-base_model: Qwen/Qwen3.5-27B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-# Full fine-tune (FFT) of the text-only path of Qwen3.5-27B.
-
-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
-
-# Freeze vision encoder
-unfrozen_parameters:
-  - model\.language_model\..*
-  - lm_head\..*
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 2
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-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:

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

@@ -1,81 +0,0 @@
-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

@@ -1,7 +1,9 @@
 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

@@ -1,85 +0,0 @@
-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

@@ -32,11 +32,7 @@ lora_target_modules:
   - 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:
+#lora_target_parameters:
 #   - mlp.experts.gate_up_proj
 #   - mlp.experts.down_proj
 

examples/qwen3.5/7b-lora-vision.yaml

@@ -1,6 +1,10 @@
-base_model: Qwen/Qwen3.5-9B
+base_model: Qwen/Qwen3.5-7B
 processor_type: AutoProcessor
 
+# Qwen3.5-7B and above are early-fusion VLMs (Qwen3_5ForConditionalGeneration).
+# Vision and text tokens are processed together by the same transformer layers.
+# Note: Qwen3.5-2B is a text-only model — the smallest VLM is Qwen3.5-7B.
+
 # These 3 lines are required for vision/multimodal training
 skip_prepare_dataset: true
 remove_unused_columns: false
@@ -26,6 +30,8 @@ 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/qwen3.5/9b-fft-vision.yaml

@@ -1,49 +0,0 @@
-base_model: Qwen/Qwen3.5-9B
-processor_type: AutoProcessor
-
-# Required for multimodal training
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-chat_template: qwen3_5
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-sequence_len: 4096
-pad_to_sequence_len: false
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-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: 1
-saves_per_epoch: 1
-weight_decay: 0.0
-special_tokens:

examples/qwen3.5/README.md

@@ -1,6 +1,15 @@
 # Finetune Qwen3.5 with Axolotl
 
-[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`.
+[Qwen3.5](https://huggingface.co/collections/Qwen/qwen35-68452f3bc6e4b7cfb4e1c803) is a hybrid architecture model series combining Gated DeltaNet linear attention with standard Transformer attention. Models from 7B onwards are early-fusion vision-language models (`Qwen3_5ForConditionalGeneration`), meaning vision and text tokens are processed through the same transformer stack. The 2B variant is text-only.
+
+Available configs:
+
+| Config | Model | Type |
+|---|---|---|
+| `27b-qlora.yaml` | Qwen3.5-27B | Dense VLM, text-only path |
+| `35b-a3b-moe-qlora.yaml` | Qwen3.5-35B-A3B | MoE, text-only path |
+| `122b-a10b-moe-qlora.yaml` | Qwen3.5-122B-A10B | MoE, text-only path |
+| `7b-lora-vision.yaml` | Qwen3.5-7B | Vision+text (multimodal) |
 
 ## Getting started
 
@@ -9,69 +18,35 @@
 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
-  ```
-  > 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
+```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.
 
-### Routed Experts (MoE)
+4. Run a finetuning example:
 
-To apply LoRA to routed expert parameters, add `lora_target_parameters`:
+```bash
+# Dense 27B text-only (QLoRA, ~47 GiB VRAM with sample packing)
+axolotl train examples/qwen3.5/27b-qlora.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
 
-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'
+# 7B vision+text (LoRA, multimodal dataset)
+axolotl train examples/qwen3.5/7b-lora-vision.yaml
 ```
 
 ### TIPS
 
-- 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.
+- For inference, you can experiment with `temperature: 0.7`, `top_p: 0.8`, `top_k: 20`, and `min_p: 0`.
+- You can run a full finetuning 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`.
+- For **multimodal** finetuning, set `processor_type: AutoProcessor`, `skip_prepare_dataset: true`, and `remove_unused_columns: false` as shown in `7b-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

@@ -61,11 +61,5 @@ 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"]

scripts/cutcrossentropy_install.py

@@ -29,5 +29,5 @@ UV_PREFIX = "uv " if USE_UV else ""
 
 print(
     UNINSTALL_PREFIX
-    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@63b15e6"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fa9a7fe"'
 )

setup.py

@@ -81,23 +81,16 @@ def parse_requirements(extras_require_map):
                     f"https://download.pytorch.org/whl/{torch_cuda_version}"
                 )
 
-            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):
+            if (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

@@ -3,7 +3,6 @@
 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
@@ -48,7 +47,7 @@ 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, httpcore.ConnectError):
+    except HTTPError:
         LOG.warning(
             "Error accessing HuggingFace. This may be due to a network issue or rate limiting."
         )

src/axolotl/core/builders/base.py

@@ -353,30 +353,6 @@ 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."
@@ -508,8 +484,6 @@ 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/causal.py

@@ -421,13 +421,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             trainer_kwargs["dataset_tags"] = [
                 d["path"] for d in self.cfg.datasets if not Path(d["path"]).is_dir()
             ]
-        # TRL's RewardTrainer validates num_labels=1 on pre-loaded models; ensure the
-        # config reflects this regardless of how the model was instantiated.
-        if (
-            self.cfg.reward_model
-            and getattr(self.model.config, "num_labels", None) != 1
-        ):
-            self.model.config.num_labels = 1
         trainer = trainer_cls(
             model=self.model,
             train_dataset=self.train_dataset,

src/axolotl/core/builders/rl.py

@@ -208,11 +208,7 @@ 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 not in (RLType.GRPO, RLType.ORPO)
-        ):
+        if self.cfg.adapter and self.peft_config and self.cfg.rl is not RLType.GRPO:
             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

@@ -29,12 +29,10 @@ 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,
@@ -53,6 +51,8 @@ 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,
@@ -67,7 +67,6 @@ class AxolotlTrainer(
     OptimizerMixin,
     RngLoaderMixin,
     CheckpointSaveMixin,
-    LayerOffloadingMixin,
     ActivationOffloadingMixin,
     DistributedParallelMixin,
     Trainer,

src/axolotl/core/trainers/constants.py

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

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

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

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

@@ -4,7 +4,6 @@
 
 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

@@ -1,304 +0,0 @@
-"""
-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

@@ -235,13 +235,6 @@ 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/cut_cross_entropy/README.md

@@ -19,7 +19,7 @@ python scripts/cutcrossentropy_install.py | sh
 
 - If you are installing from pip
 ```bash
-pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@63b15e6"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fa9a7fe"
 ```
 
 ## Usage

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -35,7 +35,7 @@ LOG = get_logger(__name__)
 
 _CCE_INSTALL_MESSAGE = (
     "Please install Axolotl's fork of cut_cross_entropy with transformers support using "
-    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@63b15e6"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fa9a7fe"`'
 )
 
 

src/axolotl/integrations/kernels/constants.py

@@ -15,7 +15,6 @@ 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)
@@ -36,8 +35,6 @@ SPARSE_MOE_BLOCK = {
     "glm4v_moe": "Glm4vMoeTextMoE",
     # sigmoid -> topk routing (no group selection)
     "minimax_m2": "MiniMaxM2SparseMoeBlock",
-    # sigmoid -> topk routing, non-gated experts (up_proj + down_proj, no gate_up_proj)
-    "nemotron_h": "NemotronHMoE",
     # Models below need custom routing (not yet implemented):
     # "ernie4_5_moe": "Ernie4_5_MoeSparseMoeBlock",  # softmax->topk, e_score_correction_bias between softmax and topk
     # "deepseek_v2": "DeepseekV2Moe",  # softmax->topk, group_limited_greedy, different attr names (num_group)
@@ -61,16 +58,7 @@ 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}"
-    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
+    module = importlib.import_module(module_path)
 
     classes = []
     for cls_name in cls_names:

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

@@ -199,30 +199,24 @@ def _estimate_register_pressure(
     num_warps: int,
     *tile_sizes: tuple[int, int],
 ) -> float:
-    """Rough estimate of per-thread register footprint from live tile sizes.
+    """Estimate per-thread register count from live tile sizes.
 
-    This is a heuristic, NOT an accurate register count.  Triton uses tensor
-    core MMA fragments that pack multiple elements per register, and can spill
-    to local memory when the hardware limit (255 regs/thread) is exceeded.
-
-    The estimate is used to prune only truly extreme configs that would cause
-    excessive spilling or compilation failures.  The threshold is set high
-    (``_MAX_REGS_SOFT_LIMIT``) because the heuristic overestimates — it
-    doesn't account for MMA fragment packing.  Configs like M=64,N=64,K=64
-    (est ~520) work fine in practice via spilling.
+    Each tile of shape (rows, cols) requires rows*cols elements distributed
+    across 32 threads per warp, but each thread in the warp holds a fragment.
+    For Triton GEMM-style kernels, the register footprint per thread is
+    approximately sum(rows * cols) / 32 for each live tile, plus ~40 for
+    scalar overhead (loop counters, pointers, masks, etc.).
 
     Returns estimated registers per thread.
     """
-    # Each thread in a warp holds ~1/32 of the tile elements
+    # Each thread in a warp holds 1/32 of the tile elements
     tile_regs = sum(r * c for r, c in tile_sizes) / 32
     scalar_overhead = 40
     return tile_regs + scalar_overhead
 
 
-# Soft limit for register pressure pruning.  Only prune configs with extreme
-# tile products (e.g. M=128,K=256,N=256) that reliably crash on Blackwell.
-# Moderate configs (M=64,N=64,K=64, est ~520) work via register spilling.
-_MAX_REGS_SOFT_LIMIT = 1024
+# Maximum registers per thread on NVIDIA GPUs
+_MAX_REGS_PER_THREAD = 255
 
 
 # =============================================================================
@@ -363,7 +357,7 @@ def _scatter2scatter_lora_configs():
 
     Search space:
       BLOCK_M:    {32, 64, 128}
-      BLOCK_N:    {32, 64}
+      BLOCK_N:    {32, 64, 128, 256}
       BLOCK_K:    {32, 64, 128}
       num_warps:  {4, 8}
       num_stages: {3, 4, 5}
@@ -371,7 +365,7 @@ def _scatter2scatter_lora_configs():
     configs = []
     for block_m, block_n, block_k, warps, stages in product(
         [32, 64, 128],  # BLOCK_M
-        [32, 64],  # BLOCK_N
+        [32, 64, 128, 256],  # BLOCK_N
         [32, 64, 128],  # BLOCK_K
         [4, 8],  # num_warps
         [3, 4, 5],  # num_stages
@@ -425,7 +419,7 @@ def _prune_fwd_configs(configs, named_args, **kwargs):
             (block_r, block_k),  # a tile
             (block_n, block_r),  # b tile (epilogue)
         )
-        if est_regs > _MAX_REGS_SOFT_LIMIT:
+        if est_regs > _MAX_REGS_PER_THREAD:
             continue
 
         scored.append((smem, config))
@@ -943,16 +937,16 @@ def _scatter2scatter_lora_dX_configs():
 
     Search space:
       BLOCK_M:    {32, 64, 128}        (token tile)
-      BLOCK_K:    {32, 64, 128}   (output tile)
-      BLOCK_N:    {32, 64}   (reduction tile)
+      BLOCK_K:    {32, 64, 128, 256}   (output tile)
+      BLOCK_N:    {32, 64, 128, 256}   (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],  # BLOCK_K (output dimension)
-        [32, 64],  # BLOCK_N (reduction dimension)
+        [32, 64, 128, 256],  # BLOCK_K (output dimension)
+        [32, 64, 128, 256],  # BLOCK_N (reduction dimension)
         [4, 8],  # num_warps
         [3, 4, 5],  # num_stages
     ):
@@ -1005,7 +999,7 @@ def _prune_dX_configs(configs, named_args, **kwargs):
             (block_n, block_r),  # b tile
             (block_r, block_k),  # a tile (epilogue)
         )
-        if est_regs > _MAX_REGS_SOFT_LIMIT:
+        if est_regs > _MAX_REGS_PER_THREAD:
             continue
 
         scored.append((smem, config))
@@ -1278,9 +1272,9 @@ def _group_bwd_lora_configs():
     support GPUs with limited shared memory (e.g. ~99KB on some GPUs).
 
     Search space:
-      BLOCK_M:    {32, 64, 128}   (token-loop tile)
-      BLOCK_K:    {32, 64, 128}
-      BLOCK_N:    {32, 64}
+      BLOCK_M:    {32, 64, 128, 256}   (token-loop tile)
+      BLOCK_K:    {32, 64, 128, 256}
+      BLOCK_N:    {32, 64, 128, 256}
       num_warps:  {4, 8}
       num_stages: {3, 4, 5}
 
@@ -1289,9 +1283,9 @@ def _group_bwd_lora_configs():
     """
     configs = []
     for block_m, block_k, block_n, warps, stages in product(
-        [32, 64, 128],  # BLOCK_M
-        [32, 64, 128],  # BLOCK_K
-        [32, 64],  # BLOCK_N
+        [32, 64, 128, 256],  # BLOCK_M
+        [32, 64, 128, 256],  # BLOCK_K
+        [32, 64, 128, 256],  # BLOCK_N
         [4, 8],  # num_warps
         [3, 4, 5],  # num_stages
     ):
@@ -1338,7 +1332,7 @@ def _prune_bwd_lora_configs(configs, named_args, **kwargs):
             (block_n, block_r),  # b tile
             (block_m, block_r),  # xa intermediate
         )
-        if est_regs > _MAX_REGS_SOFT_LIMIT:
+        if est_regs > _MAX_REGS_PER_THREAD:
             continue
 
         scored.append((smem, config))
@@ -1587,7 +1581,7 @@ def _prune_split_configs(configs, named_args, **kwargs):
             (block_m, block_dim),  # other tile
             (block_r, BLOCK_INNER),  # lora weight
         )
-        if est_regs > _MAX_REGS_SOFT_LIMIT:
+        if est_regs > _MAX_REGS_PER_THREAD:
             continue
 
         if smem <= smem_cap - _SMEM_SLACK:

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

@@ -168,9 +168,6 @@ def _unwrap_experts_lora(experts_module):
           -> base_layer: ParamWrapper(gate_up_proj)
               -> base_layer: OlmoeExperts (the real module)
 
-    For non-gated experts (e.g. NemotronH), the chain targets ``up_proj``
-    instead of ``gate_up_proj``.
-
     This function walks the chain, collects LoRA params keyed by
     ``parameter_name``, and returns the base experts module.
 
@@ -179,7 +176,6 @@ def _unwrap_experts_lora(experts_module):
 
         Each ``*_lora`` is either ``(smoe_A, smoe_B, scaling)`` or ``None``.
         A/B are already in scattermoe layout.
-        For non-gated experts, ``gup_lora`` holds the ``up_proj`` LoRA.
     """
     # Collect ParamWrapper layers by their parameter_name
     wrappers = {}
@@ -199,15 +195,13 @@ def _unwrap_experts_lora(experts_module):
     num_experts = getattr(base_experts, "num_experts", None)
     if num_experts is None:
         # Fallback: infer from parameter shape
-        for attr in ("gate_up_proj", "up_proj"):
-            param = getattr(base_experts, attr, None)
-            if param is not None:
-                num_experts = param.shape[0]
-                break
+        gup = getattr(base_experts, "gate_up_proj", None)
+        if gup is not None:
+            num_experts = gup.shape[0]
 
-    # Extract gate_up_proj or up_proj LoRA (needs A<->B swap due to transposition)
+    # Extract gate_up_proj LoRA (needs A<->B swap due to transposition)
     gup_lora = None
-    gup_wrapper = wrappers.get("gate_up_proj") or wrappers.get("up_proj")
+    gup_wrapper = wrappers.get("gate_up_proj")
     if gup_wrapper is not None:
         lora_A, lora_B, scaling = get_lora_params_from_wrapper(gup_wrapper)
         if lora_A is not None:
@@ -447,12 +441,10 @@ class HFScatterMoEGatedMLP(nn.Module):
     Supports:
 
     * **Softmax→topk routing**: OLMoE, Qwen2/3MoE, Mixtral, MiniMax
-    * **Sigmoid→topk routing**: GLM, DeepSeek V3, MiniMax M2, NemotronH
+    * **Sigmoid→topk routing**: GLM, DeepSeek V3, MiniMax M2
     * **Full-parameter training**: uses ``parallel_linear`` (base ScatterMoE)
     * **LoRA fine-tuning**: detects peft ``ParamWrapper`` on ``self.experts``,
       extracts adapter weights, and uses ``parallel_linear_lora`` (fused kernel)
-    * **Non-gated experts**: NemotronH (up_proj + down_proj, no gate_up_proj)
-    * **Latent projections**: NemotronH (fc1/fc2_latent_proj wrapping experts)
     """
 
     @staticmethod
@@ -475,7 +467,7 @@ class HFScatterMoEGatedMLP(nn.Module):
         hidden_states_flat = layer_input.view(-1, hidden_dim)
 
         # ====================================================================
-        # Shared Expert (if present, e.g. Qwen2MoE, DeepSeek V3, NemotronH)
+        # Shared Expert (if present, e.g. Qwen2MoE, DeepSeek V3)
         # ====================================================================
         shared_expert_output = _compute_shared_expert(self, hidden_states_flat)
 
@@ -497,22 +489,6 @@ class HFScatterMoEGatedMLP(nn.Module):
         # ====================================================================
         experts, gup_lora, down_lora = _unwrap_experts_lora(self.experts)
 
-        # ====================================================================
-        # Detect non-gated experts (e.g. NemotronH: up_proj + down_proj only)
-        # ====================================================================
-        is_gated = hasattr(experts, "gate_up_proj")
-        up_proj_attr = "gate_up_proj" if is_gated else "up_proj"
-
-        # ====================================================================
-        # Optional latent projection (NemotronH: fc1/fc2_latent_proj)
-        # ====================================================================
-        fc1_latent_proj = getattr(self, "fc1_latent_proj", None)
-        fc2_latent_proj = getattr(self, "fc2_latent_proj", None)
-
-        expert_input = hidden_states_flat
-        if fc1_latent_proj is not None and not isinstance(fc1_latent_proj, nn.Identity):
-            expert_input = fc1_latent_proj(hidden_states_flat)
-
         # ====================================================================
         # Selective expert weight dequantization
         # ====================================================================
@@ -522,7 +498,7 @@ class HFScatterMoEGatedMLP(nn.Module):
         use_selective = (
             getattr(self, "_use_selective_dequant", False)
             and hasattr(experts, "parametrizations")
-            and up_proj_attr in experts.parametrizations
+            and "gate_up_proj" in experts.parametrizations
         )
 
         if use_selective:
@@ -541,11 +517,11 @@ class HFScatterMoEGatedMLP(nn.Module):
                 num_experts,
             )
             # Dequantize only active experts' weights
-            up_W = selective_expert_weights(
+            gate_up_W = selective_expert_weights(
                 experts,
-                up_proj_attr,
+                "gate_up_proj",
                 active_experts,
-            ).transpose(2, 1)
+            ).transpose(2, 1)  # [num_active, hidden, 2*inter]
 
             # Remap LoRA weights to match compact expert indices
             if gup_lora is not None:
@@ -562,18 +538,18 @@ class HFScatterMoEGatedMLP(nn.Module):
             sei_gup = remapped_expert_idxs
             eo_gup = compact_offsets
         else:
-            up_W = getattr(experts, up_proj_attr).transpose(2, 1)
+            gate_up_W = experts.gate_up_proj.transpose(2, 1)  # [E, hidden, 2*inter]
             sei_gup = sorted_expert_idxs
             eo_gup = expert_offsets
 
         # ====================================================================
-        # Up projection (gated: gate_up_proj; non-gated: up_proj)
+        # Gate + Up projection
         # ====================================================================
         if gup_lora is not None:
             gup_A, gup_B, gup_scaling = gup_lora
-            up_out = parallel_linear_lora(
-                expert_input,
-                up_W,
+            gup = parallel_linear_lora(
+                hidden_states_flat,
+                gate_up_W,
                 top_k,
                 sei_gup,
                 sorted_scattered_idxs,
@@ -587,9 +563,9 @@ class HFScatterMoEGatedMLP(nn.Module):
                 use_fused_gather=True,
             )
         else:
-            up_out = parallel_linear(
-                expert_input,
-                up_W,
+            gup = parallel_linear(
+                hidden_states_flat,
+                gate_up_W,
                 top_k,
                 sei_gup,
                 sorted_scattered_idxs,
@@ -598,14 +574,8 @@ class HFScatterMoEGatedMLP(nn.Module):
                 grouped_out=True,
             )
 
-        # ====================================================================
-        # Activation: gated (act_fn(gate) * up) vs non-gated (act_fn(up))
-        # ====================================================================
-        if is_gated:
-            gates, h = up_out.chunk(2, dim=-1)
-            h = experts.act_fn(gates) * h
-        else:
-            h = experts.act_fn(up_out)
+        gates, h = gup.chunk(2, dim=-1)
+        h = experts.act_fn(gates) * h
 
         # ====================================================================
         # Down projection
@@ -665,12 +635,6 @@ class HFScatterMoEGatedMLP(nn.Module):
                 gates=routing_weights,
             )
 
-        # ====================================================================
-        # Optional latent projection back to hidden_size (NemotronH)
-        # ====================================================================
-        if fc2_latent_proj is not None and not isinstance(fc2_latent_proj, nn.Identity):
-            expert_output = fc2_latent_proj(expert_output)
-
         # ====================================================================
         # Combine with shared expert and reshape
         # ====================================================================

src/axolotl/integrations/liger/args.py

@@ -30,15 +30,6 @@ 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

@@ -1,175 +0,0 @@
-"""
-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

@@ -1,198 +0,0 @@
-"""
-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

@@ -174,19 +174,6 @@ 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,
@@ -199,19 +186,6 @@ 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

@@ -1,147 +0,0 @@
-"""
-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

@@ -105,10 +105,6 @@ 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

@@ -1,333 +0,0 @@
-"""
-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

@@ -571,6 +571,15 @@ 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 (
@@ -582,6 +591,8 @@ 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

@@ -221,14 +221,6 @@ 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/accelerate/parallelism_config.py

@@ -78,21 +78,30 @@ def patch_parallelism_config():
 
 
 def patch_prepare_cp():
-    import contextlib
+    import functools
 
+    import torch
     from accelerate import Accelerator
 
     def patched_prepare_cp(self, *args):
         if self.parallelism_config.cp_backend == "deepspeed":
             return args
 
-        @contextlib.contextmanager
-        def _noop_cp_context(
-            buffers=None, buffer_seq_dims=None, no_restore_buffers=None
-        ):
-            yield
+        from accelerate.big_modeling import _attach_context_parallel_hooks
+        from torch.distributed.tensor.experimental import context_parallel
+        from torch.distributed.tensor.experimental._attention import set_rotate_method
+
+        cp_comm_strategy = self.parallelism_config.cp_handler.cp_comm_strategy
+        set_rotate_method(cp_comm_strategy)
+
+        self._cp_context = functools.partial(
+            context_parallel, mesh=self.torch_device_mesh["cp"]
+        )
+
+        for arg in args:
+            if isinstance(arg, torch.nn.Module):
+                _attach_context_parallel_hooks(arg)
 
-        self._cp_context = _noop_cp_context
         return args
 
     Accelerator._prepare_cp = patched_prepare_cp

src/axolotl/monkeypatch/llama_expand_mask.py

@@ -0,0 +1,24 @@
+"""
+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

@@ -0,0 +1,26 @@
+"""
+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

@@ -12,7 +12,6 @@ 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,
@@ -52,29 +51,6 @@ QKV_PATCHES = [
     value_states = value_states.view(hidden_shape).transpose(1, 2)
 """.lstrip("\n"),
     ),
-    (
-        """
-    query_states, gate = torch.chunk(
-        self.q_proj(hidden_states).view(*input_shape, -1, self.head_dim * 2), 2, dim=-1
-    )
-    gate = gate.reshape(*input_shape, -1)
-
-    query_states = self.q_norm(query_states.view(hidden_shape)).transpose(1, 2)
-    key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
-    value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-""".lstrip("\n"),
-        """
-    query_states, key_states, value_states = self.apply_qkv(hidden_states)
-    query_states, gate = torch.chunk(
-        query_states.view(*input_shape, -1, self.head_dim * 2), 2, dim=-1
-    )
-    gate = gate.reshape(*input_shape, -1)
-
-    query_states = self.q_norm(query_states.view(hidden_shape)).transpose(1, 2)
-    key_states = self.k_norm(key_states.view(hidden_shape)).transpose(1, 2)
-    value_states = value_states.view(hidden_shape).transpose(1, 2)
-""".lstrip("\n"),
-    ),
 ]
 
 ORIGINAL_O_CODE = """
@@ -323,8 +299,6 @@ def get_layers(model: PeftModelForCausalLM) -> list[nn.Module]:
     if hasattr(pretrained_model, "language_model"):
         return pretrained_model.language_model.layers
     if hasattr(pretrained_model, "model"):
-        if hasattr(pretrained_model.model, "language_model"):
-            return pretrained_model.model.language_model.layers
         return pretrained_model.model.layers
 
     raise NotImplementedError(
@@ -371,13 +345,13 @@ def apply_lora_kernel_patches(
         active_adapter = model.active_adapter
     lora_config = model.model.peft_config[active_adapter]
 
-    # 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")
+    # 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
 
     # This needs to be reset after patching
     original_level = LOG.getEffectiveLevel()
@@ -420,33 +394,44 @@ def apply_lora_kernel_patches(
                     for linear_proj in ["q_proj", "k_proj", "v_proj"]
                 ]
                 can_patch_qkv = all(
-                    hasattr(module, "lora_A") for module in layer_modules
+                    hasattr(module, "lora_A")
+                    and len(getattr(module, "lora_magnitude_vector", []) or []) == 0
+                    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"
+                        "Cannot patch some attention QKV projections - requires LoRA "
+                        "adapters and no lora_magnitude_vector (DoRA)"
                     )
             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") for module in layer_modules)
+                can_patch_o = all(
+                    hasattr(module, "lora_A")
+                    and len(getattr(module, "lora_magnitude_vector", []) or []) == 0
+                    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"
+                        "Cannot patch some attention output projection - requires LoRA "
+                        "adapters and no lora_magnitude_vector (DoRA)"
                     )
         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") for proj in (gate_proj, up_proj, down_proj)
+                    hasattr(proj, "lora_A")
+                    and len(getattr(proj, "lora_magnitude_vector", []) or []) == 0
+                    for proj in (gate_proj, up_proj, down_proj)
                 )
 
                 if can_patch_mlp:
@@ -454,50 +439,15 @@ 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"
+                        "Cannot patch some MLP layers - requires LoRA adapters and no "
+                        "lora_magnitude_vector (DoRA)"
                     )
 
-    # 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/multipack.py

@@ -59,7 +59,6 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "ministral3",
     "mistral4",
     "afmoe",
-    "nemotron",
 ]
 
 

src/axolotl/monkeypatch/utils.py

@@ -3,10 +3,15 @@ Shared utils for the monkeypatches
 """
 
 import re
-from typing import Tuple
+from typing import Optional, 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
@@ -165,6 +170,65 @@ 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

@@ -1,96 +0,0 @@
-"""
-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

@@ -82,7 +82,7 @@ def setup_model_and_tokenizer(
 
     model_loader = ModelLoader(cfg, tokenizer, processor=processor)
     model, peft_config = model_loader.load()
-    if getattr(model, "generation_config", None) is not None:
+    if model.generation_config is not None:
         model.generation_config.do_sample = True
 
     model_properties = model.config.to_dict()

src/axolotl/utils/callbacks/qat.py

@@ -25,11 +25,9 @@ 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
-        if hasattr(mod.weight_fake_quantizer, "enabled"):
-            mod.weight_fake_quantizer.enabled = enable
+        mod.weight_fake_quantizer.enabled = enable
 
 
 class QATCallback(TrainerCallback):

src/axolotl/utils/callbacks/tokens_per_second.py

@@ -12,11 +12,12 @@ 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

@@ -22,12 +22,7 @@ from axolotl.utils.schemas.config import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
     AxolotlInputConfig as AxolotlInputConfigBase,
 )
-from axolotl.utils.schemas.datasets import (
-    DPODataset,
-    KTODataset,
-    SFTDataset,
-    SyntheticDataset,
-)
+from axolotl.utils.schemas.datasets import DPODataset, KTODataset, SFTDataset
 
 LOG = get_logger(__name__)
 
@@ -313,14 +308,6 @@ 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

@@ -376,14 +376,10 @@ 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."""
-    # 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
-        )
+    # 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

@@ -10,11 +10,9 @@ 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,
 )
 
@@ -175,70 +173,6 @@ 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,
@@ -266,9 +200,13 @@ def prepare_model_for_qat(
         activation_dtype=activation_dtype,
         group_size=group_size,
     )
-    qat_config = _make_qat_config(
-        base_config, weight_dtype, activation_dtype, group_size
-    )
+    if isinstance(base_config, MXFakeQuantizeConfig):
+        qat_config = QATConfig(
+            activation_config=base_config,
+            weight_config=base_config,
+        )
+    else:
+        qat_config = QATConfig(base_config)
     quantize_(model, qat_config)
     if quantize_embedding:
         # activation fake quantization is not supported for embedding layers
@@ -277,9 +215,12 @@ def prepare_model_for_qat(
             activation_dtype=None,
             group_size=group_size,
         )
-        embedding_qat_config = _make_qat_config(
-            embedding_base_config, weight_dtype, None, 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)
         quantize_(
             model,
             embedding_qat_config,

src/axolotl/utils/schedulers.py

@@ -2,7 +2,7 @@
 
 import math
 from functools import partial
-from typing import Any, Sequence
+from typing import Sequence
 
 from torch import Tensor
 from torch.optim import Optimizer
@@ -340,19 +340,3 @@ 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

@@ -22,7 +22,6 @@ 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
@@ -186,13 +185,7 @@ class AxolotlInputConfig(
 
     datasets: (
         Annotated[
-            list[
-                SFTDataset
-                | DPODataset
-                | KTODataset
-                | StepwiseSupervisedDataset
-                | SyntheticDataset
-            ],
+            list[SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset],
             MinLen(1),
         ]
         | None
@@ -205,13 +198,7 @@ class AxolotlInputConfig(
 
     test_datasets: (
         Annotated[
-            list[
-                SFTDataset
-                | DPODataset
-                | KTODataset
-                | StepwiseSupervisedDataset
-                | SyntheticDataset
-            ],
+            list[SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset],
             MinLen(1),
         ]
         | None
@@ -446,12 +433,6 @@ 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,
@@ -703,12 +684,6 @@ 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,
@@ -1319,7 +1294,6 @@ 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
@@ -1367,12 +1341,7 @@ 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",
-                "lora_embedding_kernel",
-            ]
+            kernel_fields = ["lora_mlp_kernel", "lora_qkv_kernel", "lora_o_kernel"]
             if (
                 any(data.get(k) is not None for k in kernel_fields)
                 or any(data.get(k) for k in unsloth_fields)
@@ -1385,38 +1354,9 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
             if data.get("trust_remote_code"):
                 return data
 
-            # Skip auto-enable for MoE models when native grouped_mm is unavailable
-            # (torch < 2.9). The grouped_mm fallback in transformers uses torch.mm
-            # with out= which bypasses autocast and fails on mixed dtypes during eval.
-            env_capabilities = data.get("env_capabilities", {})
-            torch_version = env_capabilities.get("torch_version")
-            if torch_version is None:
-                import torch
-
-                torch_version = str(torch.__version__).split("+", maxsplit=1)[0]
-            has_grouped_mm = version.parse(torch_version) >= version.parse("2.9.0")
-            if not has_grouped_mm:
-                is_moe = False
-                model_type = data.get("model_config_type", "")
-                if model_type and "moe" in model_type.lower():
-                    is_moe = True
-                if not is_moe:
-                    try:
-                        from transformers import AutoConfig
-
-                        base_model = data.get("base_model")
-                        if base_model:
-                            auto_cfg = AutoConfig.from_pretrained(
-                                base_model, trust_remote_code=False
-                            )
-                            if getattr(auto_cfg, "num_local_experts", None) or getattr(
-                                auto_cfg, "num_experts", None
-                            ):
-                                is_moe = True
-                    except Exception:  # pylint: disable=broad-exception-caught
-                        pass
-                if is_moe:
-                    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")
@@ -1439,9 +1379,6 @@ 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

@@ -296,42 +296,4 @@ class KTODataset(BaseModel):
     revision: str | None = None
 
 
-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
-)
+DatasetConfig = SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset

src/axolotl/utils/schemas/enums.py

@@ -87,11 +87,6 @@ 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

@@ -253,23 +253,6 @@ class TrainingValidationMixin:
                 data["pad_to_sequence_len"] = True
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def set_reward_model_defaults(cls, data):
-        if data.get("reward_model"):
-            if data.get("num_labels") is None:
-                data["num_labels"] = 1
-            if not (data.get("type_of_model") or data.get("model_type")):
-                data["model_type"] = "AutoModelForSequenceClassification"
-
-        if data.get("process_reward_model"):
-            if data.get("num_labels") is None:
-                data["num_labels"] = 2
-            if not (data.get("type_of_model") or data.get("model_type")):
-                data["model_type"] = "AutoModelForTokenClassification"
-
-        return data
-
     @model_validator(mode="before")
     @classmethod
     def check_gas_bsz(cls, data):
@@ -681,7 +664,15 @@ class LoRAValidationMixin:
     @model_validator(mode="before")
     @classmethod
     def check_lora_kernels_dora(cls, data):
-        # DoRA is now supported by lora kernels
+        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."
+            )
         return data
 
     @model_validator(mode="before")
@@ -782,14 +773,6 @@ 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):
@@ -799,32 +782,15 @@ class OptimizationValidationMixin:
                     "Muon optimizer is currently incompatible with DeepSpeed"
                 )
             if data.get("fsdp") or data.get("fsdp_config"):
-                fsdp_version = cls._resolve_fsdp_version(data)
+                fsdp_version = data.get("fsdp_version")
+                if fsdp_version is None:
+                    fsdp_version = data.get("fsdp_config", {}).get("fsdp_version", 1)
                 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

@@ -15,8 +15,6 @@ 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
@@ -31,26 +29,6 @@ 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/core/test_builders.py

@@ -536,7 +536,7 @@ class TestHFCausalTrainerBuilder:
         "cfg_string",
         [
             "sft_cfg",
-            "rm_cfg",
+            # "rm_cfg",  # TODO fix for num_labels = 2 vs 1
             "prm_cfg",
         ],
     )

tests/e2e/integrations/test_scattermoe_lora_kernels.py

@@ -20,7 +20,6 @@ Test strategy:
 - Tolerances account for tf32 accumulation in Triton kernels
 """
 
-from functools import wraps
 from types import SimpleNamespace
 
 import pytest
@@ -35,21 +34,6 @@ 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
 # =============================================================================
@@ -225,7 +209,6 @@ def make_test_data(
 # =============================================================================
 
 
-@pytest.mark.slow
 class TestForwardPass:
     """Test forward pass of fused scatter2scatter_lora kernel."""
 
@@ -305,7 +288,6 @@ class TestForwardPass:
         )
 
 
-@pytest.mark.slow
 class TestForwardGrouped:
     """Test forward pass with grouped_in/grouped_out configurations."""
 
@@ -395,7 +377,6 @@ class TestForwardGrouped:
 # =============================================================================
 
 
-@pytest.mark.slow
 class TestLoRAGradients:
     """Test backward LoRA gradient computation (dA, dB)."""
 
@@ -471,7 +452,6 @@ class TestLoRAGradients:
 # =============================================================================
 
 
-@pytest.mark.slow
 class TestAutograd:
     """Test full autograd integration through ScatterMoELoRA."""
 
@@ -640,7 +620,6 @@ class TestAutograd:
 # =============================================================================
 
 
-@pytest.mark.slow
 class TestBaseEquivalence:
     """When scaling=0, fused kernel should match base scatter2scatter."""
 
@@ -713,7 +692,6 @@ class TestBaseEquivalence:
 # =============================================================================
 
 
-@pytest.mark.slow
 class TestLoRAAdditivity:
     """Test that the LoRA component is correctly additive."""
 
@@ -771,7 +749,6 @@ class TestLoRAAdditivity:
 # =============================================================================
 
 
-@pytest.mark.slow
 class TestParallelExpertsModule:
     """Test the ParallelExperts module with LoRA."""
 
@@ -839,7 +816,6 @@ class TestParallelExpertsModule:
 # =============================================================================
 
 
-@pytest.mark.slow
 class TestEdgeCases:
     """Edge cases and boundary conditions."""
 
@@ -937,7 +913,6 @@ class TestEdgeCases:
 # =============================================================================
 
 
-@pytest.mark.slow
 class TestFusedDX:
     """Test fused backward dX kernel: dX = dY @ W^T + scaling * (dY @ B) @ A."""
 
@@ -1005,7 +980,6 @@ 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)
 
@@ -1148,7 +1122,6 @@ class TestFusedDX:
 # =============================================================================
 
 
-@pytest.mark.slow
 class TestFusedGatherBackward:
     """Test fused gather + backward dA/dB kernel."""
 
@@ -1201,7 +1174,6 @@ 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)
 
@@ -1211,7 +1183,6 @@ 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)
 
@@ -1298,8 +1269,6 @@ class TestFusedGatherBackward:
 # =============================================================================
 
 
-@pytest.mark.slow
-@pytest.mark.xfail(reason="flaky", strict=False)
 class TestTokenRounding:
     """Test token rounding utility and its integration with backward kernels."""
 
@@ -1346,7 +1315,6 @@ 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
@@ -1446,7 +1414,6 @@ class TestTokenRounding:
 # =============================================================================
 
 
-@pytest.mark.slow
 class TestCombinedOptimizations:
     """Test all optimizations together."""
 
@@ -1616,7 +1583,6 @@ 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."""
 
@@ -1758,7 +1724,6 @@ 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.parent
+            Path(__file__).parent.parent.parent
             / "src"
             / "axolotl"
             / "integrations"
@@ -1219,7 +1219,7 @@ class TestSharedExpertHandling:
 
         # Kernelize
         repo_path = (
-            Path(__file__).parent.parent.parent.parent
+            Path(__file__).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
 
 
@@ -176,31 +176,24 @@ def test_lora_mlp_direct(sample_tensors, activation_forward, activation_backward
     X.requires_grad = True
     output = LoRA_MLP.apply(
         X,
-        None,  # X_drop
         gate_proj.weight,
         gate_proj.bias,
         None,  # gate_quant
         None,  # gate_A
         None,  # gate_B
         None,  # gate_scale
-        None,  # gate_lora_bias
-        None,  # gate_magnitude
         up_proj.weight,
         up_proj.bias,
         None,  # up_quant
         None,  # up_A
         None,  # up_B
         None,  # up_scale
-        None,  # up_lora_bias
-        None,  # up_magnitude
         down_proj.weight,
         down_proj.bias,
         None,  # down_quant
         None,  # down_A
         None,  # down_B
         None,  # down_scale
-        None,  # down_lora_bias
-        None,  # down_magnitude
         activation_forward,
         activation_backward,
         True,  # inplace
@@ -254,31 +247,24 @@ def test_lora_mlp_with_adapters(
     # Forward pass with adapters
     output = LoRA_MLP.apply(
         X,
-        None,  # X_drop
         gate_proj.weight,
         gate_proj.bias,
         None,
         gate_A,
         gate_B,
         scale,
-        None,  # gate_lora_bias
-        None,  # gate_magnitude
         up_proj.weight,
         up_proj.bias,
         None,
         up_A,
         up_B,
         scale,
-        None,  # up_lora_bias
-        None,  # up_magnitude
         down_proj.weight,
         down_proj.bias,
         None,
         down_A,
         down_B,
         scale,
-        None,  # down_lora_bias
-        None,  # down_magnitude
         activation_forward,
         activation_backward,
         True,
@@ -348,32 +334,25 @@ def test_lora_qkv(sample_tensors):
 
     Q1, K1, V1 = LoRA_QKV.apply(
         X,
-        None,  # X_drop
         q_weight,
         None,
         None,
         None,
         None,
         None,
-        None,
-        None,  # Q: weight, bias, quant, A, B, scale, lora_bias, magnitude
         k_weight,
         None,
         None,
         None,
         None,
         None,
-        None,
-        None,  # K
         v_weight,
         None,
         None,
         None,
         None,
         None,
-        None,
-        None,  # V
-        True,  # inplace
+        True,
     )
 
     assert Q1.shape == K1.shape == V1.shape == X.shape
@@ -387,32 +366,25 @@ def test_lora_qkv(sample_tensors):
     # Test with LoRA adapters
     Q2, K2, V2 = LoRA_QKV.apply(
         X,
-        None,  # X_drop
         q_weight,
         None,
         None,
         q_A,
         q_B,
         scale,
-        None,
-        None,  # Q
         k_weight,
         None,
         None,
         k_A,
         k_B,
         scale,
-        None,
-        None,  # K
         v_weight,
         None,
         None,
         v_A,
         v_B,
         scale,
-        None,
-        None,  # V
-        True,  # inplace
+        True,
     )
 
     assert Q2.shape == K2.shape == V2.shape == X.shape
@@ -455,9 +427,7 @@ def test_lora_o(sample_tensors):
 
     # Test forward pass
     X.requires_grad = True
-    output = LoRA_O.apply(
-        X, None, W, b, None, A, B, scale, None, None
-    )  # X_drop, ..., lora_bias, magnitude
+    output = LoRA_O.apply(X, W, b, None, A, B, scale)
 
     assert output.shape == (X.shape[0], X.shape[1], W.shape[0])
 
@@ -572,7 +542,6 @@ def test_inplace_operations(sample_tensors, apply_function):
             "down_proj": nn.Linear(shapes["out"], shapes["hidden"]).to(
                 device="cuda", dtype=torch.float16
             ),
-            "training": False,
         },
     )
 

tests/e2e/multigpu/test_fsdp2_lora_kernels.py

@@ -1,120 +0,0 @@
-"""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 that kernels ARE patched even with dropout and bias (now supported)."""
+    """Test various conditions that should prevent kernel patching."""
     test_configs = [
-        # Dropout — kernels now support this
+        # Dropout prevents patching
         {
             "peft_type": "LORA",
             "task_type": "CAUSAL_LM",
@@ -234,7 +234,7 @@ def test_kernel_patch_conditions():
             "lora_dropout": 0.1,
             "bias": "none",
         },
-        # Bias — kernels now support this
+        # Bias prevents patching
         {
             "peft_type": "LORA",
             "task_type": "CAUSAL_LM",
@@ -252,14 +252,13 @@ 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 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
-        )
+        # Verify no patches applied
+        assert layer.forward.__func__ is not apply_lora_mlp_swiglu
+        assert layer.forward.__func__ is not apply_lora_mlp_geglu
 
 
 def test_kernel_config_options():
@@ -512,7 +511,7 @@ 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 DOES patch (now supported)."""
+    """Test model loading with dropout non-zero should not patch."""
 
     from axolotl.cli.utils import load_model_and_tokenizer
 
@@ -547,18 +546,31 @@ 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 patches — should succeed even with dropout > 0
+    # Apply patch
     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 patches WERE applied (dropout is now supported by kernels)
+    # Verify patch was not applied
     layers = get_layers(model)
     for layer in layers:
         for self_attn in find_self_attn_in_layer(layer):
-            assert hasattr(self_attn, "apply_qkv")
-            assert hasattr(self_attn, "apply_o")
+            assert not hasattr(self_attn, "apply_qkv")
+            assert not hasattr(self_attn, "apply_o")

tests/e2e/patched/test_lora_llama_multipack.py

@@ -4,7 +4,8 @@ E2E tests for lora llama
 
 import unittest
 
-from transformers.utils import is_torch_bf16_gpu_available
+import pytest
+from transformers.utils import is_auto_gptq_available, is_torch_bf16_gpu_available
 
 from axolotl.common.datasets import load_datasets
 from axolotl.train import train
@@ -67,3 +68,51 @@ class TestLoraLlama(unittest.TestCase):
 
         train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)
+
+    @pytest.mark.skipif(not is_auto_gptq_available(), reason="auto-gptq not available")
+    @with_temp_dir
+    def test_lora_gptq_packed(self, temp_dir):
+        cfg = DictDefault(
+            {
+                "base_model": "lilmeaty/SmolLM2-135M-Instruct-GPTQ",
+                "model_type": "AutoModelForCausalLM",
+                "tokenizer_type": "AutoTokenizer",
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "flash_attention": True,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "gptq": True,
+                "gptq_disable_exllama": True,
+                "lora_r": 32,
+                "lora_alpha": 64,
+                "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": 2,
+                "max_steps": 20,
+                "save_steps": 0.5,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "save_first_step": False,
+            }
+        )
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        dataset_meta = load_datasets(cfg=cfg)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)

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

@@ -14,9 +14,6 @@ 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

@@ -262,7 +262,6 @@ 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].mlp.gate.weight.dtype
+            model.base_model.model.model.layers[0].block_sparse_moe.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].mlp.gate.weight.dtype
+            model.base_model.model.model.layers[0].block_sparse_moe.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].mlp.gate.weight.dtype
+            model.base_model.model.model.layers[0].block_sparse_moe.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].mlp.gate.weight.dtype
+            model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
             == torch.float32
         )
         check_model_output_exists(temp_dir, cfg)

tests/e2e/test_optimizers.py

@@ -4,8 +4,6 @@ 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
@@ -284,60 +282,3 @@ 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

@@ -35,14 +35,6 @@ 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(
@@ -165,18 +157,6 @@ 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(
@@ -272,24 +252,28 @@ class TestQuantization:
         if quantize_embedding:
             assert isinstance(model.model.embed_tokens, FakeQuantizedEmbedding)
             assert hasattr(model.model.embed_tokens, "weight_fake_quantizer")
-            embed_config = model.model.embed_tokens.weight_fake_quantizer.config
-            assert _get_fake_quant_config_dtype(embed_config) == weight_dtype.value
+            assert (
+                model.model.embed_tokens.weight_fake_quantizer.config.dtype
+                == weight_dtype.value
+            )
             if group_size:
-                assert embed_config.group_size == group_size
+                assert (
+                    model.model.embed_tokens.weight_fake_quantizer.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")
-                w_config = child.weight_fake_quantizer.config
-                assert _get_fake_quant_config_dtype(w_config) == weight_dtype.value
+                assert child.weight_fake_quantizer.config.dtype == weight_dtype.value
                 if group_size:
-                    assert w_config.group_size == group_size
+                    assert child.weight_fake_quantizer.config.group_size == group_size
                 if activation_dtype:
                     assert hasattr(child, "activation_fake_quantizer")
-                    a_config = child.activation_fake_quantizer.config
                     assert (
-                        _get_fake_quant_config_dtype(a_config) == activation_dtype.value
+                        child.activation_fake_quantizer.config.dtype
+                        == activation_dtype.value
                     )
                 else:
                     assert child.activation_fake_quantizer is None
@@ -390,16 +374,9 @@ 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)
-
-        # 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
+        assert model.lm_head.weight_fake_quantizer.enabled
 
         qat_callback = QATCallback(cfg)
 
@@ -411,10 +388,9 @@ class TestQuantizationCallback:
             model=model,
         )
 
-        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
+        # 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(
@@ -424,10 +400,9 @@ class TestQuantizationCallback:
             model=model,
         )
 
-        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
+        # 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):
@@ -449,10 +424,9 @@ 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)
-        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
+        assert model.lm_head.weight_fake_quantizer.enabled
 
         qat_callback = QATCallback(cfg)
         # simulate first training step
@@ -464,6 +438,5 @@ class TestQuantizationCallback:
         )
 
         # quantization should be enabled from the get-go
-        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
+        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 = 0.05,
+    rtol: float = 0.02,
     gt_zero: bool = True,
 ) -> None:
     """

tests/kernels/test_rms_norm_gated.py

@@ -1,229 +0,0 @@
-"""
-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

@@ -13,7 +13,6 @@ 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")
@@ -278,34 +277,6 @@ class TestValidation(BaseValidation):
         new_cfg = validate_config(cfg)
         assert new_cfg.type_of_model == "AutoModelForCausalLM"
 
-    def test_reward_model_defaults(self, minimal_cfg):
-        cfg = (
-            DictDefault(
-                {
-                    "reward_model": True,
-                }
-            )
-            | minimal_cfg
-        )
-
-        new_cfg = validate_config(cfg)
-        assert new_cfg.num_labels == 1
-        assert new_cfg.type_of_model == "AutoModelForSequenceClassification"
-
-    def test_process_reward_model_defaults(self, minimal_cfg):
-        cfg = (
-            DictDefault(
-                {
-                    "process_reward_model": True,
-                }
-            )
-            | minimal_cfg
-        )
-
-        new_cfg = validate_config(cfg)
-        assert new_cfg.num_labels == 2
-        assert new_cfg.type_of_model == "AutoModelForTokenClassification"
-
     def test_model_revision_remap(self, minimal_cfg):
         cfg = (
             DictDefault(
@@ -1732,52 +1703,3 @@ 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

@@ -1,125 +0,0 @@
-"""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

@@ -0,0 +1,45 @@
+"""
+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

@@ -28,22 +28,20 @@ class TestLoRAConfigValidation:
         result = validate_config(valid_config)
         assert result["adapter"] == "lora"
 
-        # 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
+        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)
 
     def test_qlora_4bit_validation(self):
         """Test QLoRA 4-bit configuration validation"""

tests/utils/lora/test_freeze_lora.py

@@ -38,11 +38,6 @@ 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
@@ -53,7 +48,7 @@ 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
@@ -67,7 +62,7 @@ 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
@@ -82,7 +77,7 @@ 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
@@ -99,7 +94,7 @@ 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
@@ -115,7 +110,7 @@ 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)
@@ -129,7 +124,7 @@ 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
@@ -143,7 +138,7 @@ 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
 
@@ -162,7 +157,7 @@ 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)
@@ -197,13 +192,13 @@ 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