fix test dims

* bump transformers to 5.5.4 and trl to latest 1.1.0

* more upgrades

* update peft too

* adapt lora_merge to peft 0.19 layer config API

PEFT 0.19 requires a LoraConfig object on Linear/ParamWrapper/Conv
layer constructors and moved use_rslora, use_dora, fan_in_fan_out,
lora_dropout, and lora_bias into that config. Build the config
per branch in _build_peft_layer_and_get_delta so the merge utility
works with the upgraded peft.

* allow lora_dropout on mixed attention+MoE configs under peft 0.19

PEFT 0.19's convert_peft_config_for_transformers auto-remaps old MoE
target_modules (w1/w2/w3 on Mixtral, etc.) into target_parameters for
transformers v5's fused 3D expert Parameters. Those targets get wrapped
with ParamWrapper, which rejects lora_dropout != 0 because the 3D
einsum can't factor dropout out of lora_B(lora_A(dropout(x))).

Monkeypatch ParamWrapper.__init__ to internally use a copy of the
LoraConfig with lora_dropout=0, so its dropout slot becomes nn.Identity
while the shared config still delivers real dropout to sibling Linear
LoRA layers (attention q/k/v/o). A probe runs the same conversion on a
deep copy to detect the situation and emit a warning before patching.

.github/workflows/tests.yml

@@ -220,6 +220,16 @@ jobs:
         run: |
           axolotl --help
 
+      - name: Verify agent docs are discoverable
+        run: |
+          # Agent docs live in docs/agents/ (source of truth) and are resolved
+          # at runtime from the repo checkout or via `axolotl fetch docs`
+          axolotl agent-docs --list
+          axolotl agent-docs | grep -q "Fine-tuning framework"
+          axolotl agent-docs grpo | grep -q "GRPO"
+          axolotl agent-docs sft | grep -q "SFT"
+          python -c "from axolotl.cli.agent_docs import get_doc, list_topics; assert len(list_topics()) >= 5; assert 'GRPO' in get_doc('grpo')"
+
       - name: Show HF cache
         run: hf cache ls
 

AGENTS.md

@@ -16,6 +16,9 @@ axolotl inference config.yaml          # Interactive inference
 axolotl merge-lora config.yaml         # Merge LoRA adapter into base model
 axolotl vllm-serve config.yaml         # Start vLLM server for GRPO/EBFT training
 axolotl fetch examples                 # Download example configs
+axolotl agent-docs                     # Show agent-optimized docs (bundled with pip package)
+axolotl agent-docs grpo                # Topic-specific agent reference
+axolotl config-schema                  # Dump config JSON schema
 ```
 
 ## Training Methods
@@ -35,6 +38,8 @@ Agent-specific references:
 - [docs/agents/grpo.md](docs/agents/grpo.md) — GRPO online RL with reward functions
 - [docs/agents/reward_modelling.md](docs/agents/reward_modelling.md) — outcome and process reward models
 - [docs/agents/pretraining.md](docs/agents/pretraining.md) — continual pretraining
+- [docs/agents/model_architectures.md](docs/agents/model_architectures.md) — model-specific quirks (Gemma4, Qwen3.5 MoE, etc.)
+- [docs/agents/new_model_support.md](docs/agents/new_model_support.md) — debugging and adding support for new model architectures
 
 ## Config Pattern
 

MANIFEST.in

@@ -3,4 +3,6 @@ include README.md
 include LICENSE
 include src/setuptools_axolotl_dynamic_dependencies.py
 include src/axolotl/utils/chat_templates/templates/*.jinja
+include AGENTS.md
+recursive-include docs/agents *.md
 recursive-include axolotl *.py

README.md

@@ -86,7 +86,7 @@ Features:
 **Requirements**:
 
 - NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU
-- Python 3.11
+- Python >=3.11 (3.12 recommended)
 - PyTorch ≥2.9.1
 
 ### Google Colab
@@ -95,6 +95,34 @@ Features:
 
 ### Installation
 
+#### Using uv (recommended)
+
+```bash
+# install uv if you don't already have it installed
+curl -LsSf https://astral.sh/uv/install.sh | sh
+source $HOME/.local/bin/env
+
+# CUDA 12.8.1 tends to have better package compatibility
+export UV_TORCH_BACKEND=cu128
+
+# create a new virtual environment
+uv venv --python 3.12
+source .venv/bin/activate
+
+uv pip install torch==2.10.0 torchvision
+uv pip install --no-build-isolation axolotl[deepspeed]
+
+# recommended - install cut-cross-entropy
+uv pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@main"
+
+# (optional) - prefetch flash-attn2 and causal-conv1d kernels
+uv run --python 3.12 python -c "from kernels import get_kernel; get_kernel('kernels-community/flash-attn2'); get_kernel('kernels-community/causal-conv1d')"
+
+# Download example axolotl configs, deepspeed configs
+axolotl fetch examples
+axolotl fetch deepspeed_configs  # OPTIONAL
+```
+
 #### Using pip
 
 ```bash
@@ -157,6 +185,29 @@ That's it! Check out our [Getting Started Guide](https://docs.axolotl.ai/docs/ge
 - [API Reference](https://docs.axolotl.ai/docs/api/) - Auto-generated code documentation
 - [FAQ](https://docs.axolotl.ai/docs/faq.html) - Frequently asked questions
 
+## AI Agent Support
+
+Axolotl ships with built-in documentation optimized for AI coding agents (Claude Code, Cursor, Copilot, etc.). These docs are bundled with the pip package — no repo clone needed.
+
+```bash
+# Show overview and available training methods
+axolotl agent-docs
+
+# Topic-specific references
+axolotl agent-docs sft                 # supervised fine-tuning
+axolotl agent-docs grpo                # GRPO online RL
+axolotl agent-docs preference_tuning   # DPO, KTO, ORPO, SimPO
+axolotl agent-docs reward_modelling    # outcome and process reward models
+axolotl agent-docs pretraining         # continual pretraining
+axolotl agent-docs --list              # list all topics
+
+# Dump config schema for programmatic use
+axolotl config-schema
+axolotl config-schema --field adapter
+```
+
+If you're working with the source repo, agent docs are also available at `docs/agents/` and the project overview is in `AGENTS.md`.
+
 ## 🤝 Getting Help
 
 - Join our [Discord community](https://discord.gg/HhrNrHJPRb) for support

docs/agents/model_architectures.md

@@ -0,0 +1,198 @@
+# Model Architectures — Agent Reference
+
+Model-specific quirks, required settings, and known issues. Check this before debugging training failures on specific model families.
+
+## VLM (Vision Language Model) Quick Start
+
+All VLM configs require these four lines:
+```yaml
+processor_type: AutoProcessor
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+```
+
+Decision tree for VLM config:
+```text
+Is the model multimodal (has vision/audio encoder)?
+  ├─ YES: Add `freeze_mm_modules: true` if training text only
+  │       Add `chat_template: <model_template>` (e.g. gemma4, qwen3_5, gemma3)
+  │       LoRA: use regex `lora_target_modules` to restrict to language model
+  └─ NO: Train as a regular text model
+
+Is the model MoE (e.g. Gemma4 26B-A4B, Qwen3.5 35B-A3B)?
+  ├─ YES: Add `lora_target_parameters` for expert LoRA
+  │       Consider ScatterMoE kernels (see Plugins section)
+  └─ NO: Standard LoRA config
+```
+
+## Plugins & Optimizations
+
+### Cut Cross Entropy (CCE)
+
+Computes loss from hidden states + lm_head weight without materializing the full logits tensor, saving significant VRAM. Install if not already present:
+
+```bash
+uv pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@main"
+```
+
+```yaml
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+```
+
+### ScatterMoE Kernels
+
+Fuses expert + LoRA computation into a single kernel for MoE models. Significant speedup for models with many experts.
+
+```yaml
+plugins:
+  - axolotl.integrations.kernels.KernelsPlugin
+use_kernels: true
+use_scattermoe: true
+experts_implementation: scattermoe
+
+# Expert LoRA targets (3D parameter tensors, not nn.Linear):
+lora_target_parameters:
+  - experts.gate_up_proj
+  - experts.down_proj
+```
+
+Supported: Gemma4 (`gemma4_text`), Mixtral, Qwen MoE variants. The plugin auto-detects model type and routing function. Without ScatterMoE, expert LoRA still works but runs base expert matmul and LoRA as separate operations.
+
+## Gemma 4
+
+**Models**: `google/gemma-4-26B-A4B` (MoE), `google/gemma-4-31B` (dense), `google/gemma-4-E2B`, `google/gemma-4-E4B`
+
+**Architecture**: Multimodal wrapper (`Gemma4ForConditionalGeneration`) over a text backbone (`Gemma4TextModel`), with optional vision/audio encoders. All Gemma4 HF repos have `model_type: "gemma4"` — even text-only variants load as multimodal with a vision tower.
+
+### Required settings
+
+```yaml
+# Always needed for Gemma4:
+freeze_mm_modules: true          # Freeze vision/audio encoders for text-only training
+gradient_checkpointing_kwargs:
+  use_reentrant: false           # Shared per-layer norms cause "marked ready twice" with reentrant
+
+# LoRA target — restrict to language model only (DO NOT use lora_target_linear: true):
+lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
+```
+
+### Auto-detection
+
+Axolotl auto-detects Gemma4 and applies:
+- `use_reentrant: false` for gradient checkpointing
+- `ddp_find_unused_parameters: true` for DDP (skipped when `activation_offloading: true`)
+
+### Multi-GPU
+
+| Strategy | Works? | Notes |
+|----------|--------|-------|
+| DDP | Yes | Auto-sets `ddp_find_unused_parameters=True` |
+| DDP + activation_offloading | Yes | `find_unused_parameters` is skipped (conflicts with checkpoint wrappers) |
+| FSDP1 | No | OOM during dequantization/sharding with QLoRA |
+| FSDP2 | Yes | Use `Gemma4TextDecoderLayer` (not `Gemma4DecoderLayer`) as wrap class |
+| FSDP2 + activation_offloading | Yes | Lowest VRAM (~26 GiB/GPU for 26B-A4B) |
+
+FSDP2 config:
+```yaml
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_version: 2
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: Gemma4TextDecoderLayer
+```
+
+### MoE (26B-A4B)
+
+- `enable_moe_block: true`, 256 experts, top-k routing
+- No separate `SparseMoeBlock` — MoE is embedded in each decoder layer
+- Expert LoRA targets 3D parameter tensors:
+  ```yaml
+  lora_target_parameters:
+    - experts.gate_up_proj
+    - experts.down_proj
+  ```
+- ScatterMoE kernel acceleration:
+  ```yaml
+  plugins:
+    - axolotl.integrations.kernels.KernelsPlugin
+  use_kernels: true
+  use_scattermoe: true
+  experts_implementation: scattermoe
+  ```
+
+### VLM (Vision) Training
+
+All Gemma4 models load as `Gemma4ForConditionalGeneration` with a vision tower. No custom `ProcessingStrategy` needed — the base class auto-detects the image token.
+
+```yaml
+base_model: google/gemma-4-E2B-it   # or E4B-it, 26B-A4B
+processor_type: AutoProcessor
+freeze_mm_modules: true
+chat_template: gemma4
+
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+```
+
+A starting VLM loss of ~8-15 is typical. In most runs, loss converges below 1.0 within ~30-50 steps, though results may vary across configurations.
+
+For the 26B-A4B MoE variant with ScatterMoE + expert LoRA + CCE, add:
+```yaml
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+  - axolotl.integrations.kernels.KernelsPlugin
+use_kernels: true
+use_scattermoe: true
+experts_implementation: scattermoe
+lora_target_parameters:
+  - experts.gate_up_proj
+  - experts.down_proj
+```
+
+### Common issues
+
+| Symptom | Cause | Fix |
+|---------|-------|-----|
+| `mm_token_type_ids is required` in DDP | `model.config` not accessible through DDP wrapper | Already fixed — `unwrap_model()` in `compute_loss` and `prediction_step` |
+| `marked a variable ready twice` in DDP | `ddp_find_unused_parameters=True` + activation_offloading checkpoint wrappers | Auto-handled — `find_unused_parameters` is skipped when `activation_offloading: true` |
+| Loss ~12 instead of ~0.5 | Using `lora_target_linear: true` (applies LoRA to vision/audio modules) | Use the regex `lora_target_modules` pattern instead |
+| FSDP2 `Could not find Gemma4AudioLayer` | Auto-wrap detects `_no_split_modules` including audio layers that don't exist | Explicitly set `fsdp_transformer_layer_cls_to_wrap: Gemma4TextDecoderLayer` |
+| `Gemma4ClippableLinear not supported` by PEFT | Vision tower uses a non-standard linear wrapper | Axolotl patches this automatically via `_patch_peft_clippable_linear()` |
+
+### E2B/E4B dense models
+
+These have `hidden_size_per_layer_input: 256` (per-layer input embeddings) and `attention_k_eq_v: False`. Known issue: loss starts higher than expected (~12 vs ~0.5 for 26B). Root cause under investigation — may be related to the per-layer input mechanism or the `Gemma4ForConditionalGeneration` loss computation.
+
+## Gemma 3
+
+**Models**: `google/gemma-3-*`
+
+- `ddp_find_unused_parameters: true` needed (multimodal unused params)
+- `use_reentrant: false` recommended
+- Attention mask must be dropped for sample packing (handled automatically)
+- Multi-GPU test currently skipped (`tests/e2e/multigpu/test_gemma3.py`)
+
+## Qwen 3.5 MoE
+
+**Models**: `Qwen/Qwen3.5-35B-A3B`
+
+- Hybrid architecture: DeltaNet linear attention (30 layers) + full attention (10 layers)
+- 256 experts, 8 active per token
+- Known weight scale drift in late DeltaNet layers (36-38) due to AdamW + rare expert interaction
+- Fix: `normalize_weight_scales` config to detect and rescale outliers:
+  ```yaml
+  normalize_weight_scales:
+    - name_pattern: 'linear_attn\.conv1d\.weight'
+      threshold: 1.3
+  ```
+
+## General MoE Notes
+
+- `lora_target_linear: true` with multimodal MoE models will apply LoRA to ALL linear modules including vision/audio encoders — use regex `lora_target_modules` to restrict to language model only
+- Rare experts get larger effective learning rate from AdamW (small second-moment estimates) — can cause weight drift in recurrent/SSM components. Use `normalize_weight_scales` with `dry_run: true` to detect.
+- For ScatterMoE kernel support, set `experts_implementation: scattermoe` and add the KernelsPlugin

docs/agents/new_model_support.md

@@ -0,0 +1,181 @@
+# New Model Support — Agent Reference
+
+Guide for debugging and adding support for new model architectures in axolotl. Based on lessons learned from Gemma4, Gemma3, Qwen2-VL, and other multimodal/MoE models.
+
+## Quick Validation Checklist
+
+When testing a new model, run through these checks in order:
+
+1. **Does the model load?** `axolotl preprocess config.yaml` — catches config schema errors
+2. **Does LoRA apply?** Check for "Unsupported layer type" warnings from PEFT
+3. **Is the initial loss sane?** First-step loss for a pretrained model should be 0.5–2.0 for SFT
+4. **Does sample packing work?** Compare loss with `sample_packing: true` vs `false` — should be similar
+5. **Is CCE active?** Check for "Applying Cut Cross Entropy" log and verify peak VRAM is lower
+
+## Loss Debugging
+
+### Expected initial loss
+A pretrained model doing SFT should start with loss roughly in the 0.5–2.0 range. If loss starts above 3.0, something is wrong. If it's near `log(vocab_size)` (≈ 12 for 262K vocab), the model is predicting at random — attention masking or model weights are broken.
+
+### Direct comparison technique
+The fastest way to isolate a loss issue — bypass the trainer entirely:
+
+```python
+# Load model via axolotl's pipeline (applies all patches)
+from axolotl.cli.config import load_cfg
+from axolotl.utils.config import normalize_config, prepare_plugins
+from axolotl.loaders.tokenizer import load_tokenizer
+from axolotl.loaders.model import ModelLoader
+
+cfg = load_cfg("your_config.yaml")
+normalize_config(cfg)
+prepare_plugins(cfg)
+tokenizer = load_tokenizer(cfg)
+model, _ = ModelLoader(cfg, tokenizer).load()
+
+# Forward pass on preprocessed data
+model.train()
+out = model(input_ids, labels=labels)
+print(f"Direct loss: {out.loss.item()}")  # Compare to trainer's reported loss
+```
+
+If direct loss is correct (~1.0) but trainer reports 3–4x higher, check `model_accepts_loss_kwargs` (see below).
+
+### `model_accepts_loss_kwargs` inflation
+HF Trainer checks if the model's `forward()` has `**kwargs` and sets `model_accepts_loss_kwargs=True`. This changes loss normalization: the trainer does NOT divide loss by `gradient_accumulation_steps` before logging. The gradient is correct — only the logged loss is inflated.
+
+**Symptom**: Logged loss ≈ actual_loss × gradient_accumulation_steps.
+
+**Which models are affected**: Any model with `**kwargs` in forward (common in multimodal models for extra inputs like `mm_token_type_ids`, `pixel_values`, etc.).
+
+**Fix location**: `src/axolotl/core/trainers/base.py` `__init__()` — after `super().__init__()`, check if the unwrapped model actually has `num_items_in_batch` in its forward signature. If not, set `self.model_accepts_loss_kwargs = False`.
+
+## Multimodal Models (ForConditionalGeneration)
+
+Many recent models use `ForConditionalGeneration` as the top-level class, not `ForCausalLM`:
+- Gemma3 → `Gemma3ForConditionalGeneration`
+- Gemma4 → `Gemma4ForConditionalGeneration`
+- Qwen2-VL → `Qwen2VLForConditionalGeneration`
+- LLaVA → `LlavaForConditionalGeneration`
+
+### Why this matters
+
+| Component | Targets `ForCausalLM` | Needs `ForConditionalGeneration` |
+|-----------|----------------------|--------------------------------|
+| CCE patches | ✅ (default) | ❌ silently inactive if not patched |
+| PEFT LoRA | ✅ | May fail on custom layer types |
+| HF Trainer label handling | ✅ | May need extra inputs |
+
+### Required extra inputs
+Multimodal models require special inputs during training even for text-only data:
+
+| Model | Required Input | Value for Text-Only |
+|-------|---------------|-------------------|
+| Gemma4 | `mm_token_type_ids` | `torch.zeros_like(input_ids)` |
+| Gemma3 | `token_type_ids` | `torch.zeros_like(input_ids)` |
+
+Auto-inject in `compute_loss()` when not provided by the data collator. See `core/trainers/base.py`.
+
+### Custom layer types and PEFT
+Vision towers often use custom module wrappers that PEFT doesn't support:
+
+| Model | Custom Layer | Wraps | Fix |
+|-------|-------------|-------|-----|
+| Gemma4 | `Gemma4ClippableLinear` | `nn.Linear` | Redirect to `.linear` child |
+
+Fix location: `src/axolotl/loaders/adapter.py` `_patch_peft_clippable_linear()`.
+
+## Sample Packing
+
+### How packed sequence detection works (transformers ≥ 5.x)
+`transformers.masking_utils._preprocess_mask_arguments()` detects packed sequences from `position_ids` resets. But **only when `attention_mask is None`**:
+
+```python
+# From masking_utils.py:
+if position_ids is not None and attention_mask is None and past_key_values is None:
+    packed_sequence_mask = find_packed_sequence_indices(position_ids)
+```
+
+If the collator provides an all-ones `attention_mask`, packing detection is **skipped** and the model builds a single causal mask spanning all packed sequences → cross-sequence attention leakage → very high loss.
+
+### Fix for models using `create_causal_mask_mapping`
+For Gemma3, Gemma4, and similar models that use the new transformers masking system, remove `attention_mask` from inputs when sample packing is active:
+
+```python
+# In compute_loss():
+if (
+    self.args.sample_packing
+    and model_type in ("gemma4", "gemma3")
+    and "attention_mask" in inputs
+    and "position_ids" in inputs
+):
+    del inputs["attention_mask"]
+```
+
+Fix location: `src/axolotl/core/trainers/base.py` `compute_loss()`.
+
+### Models that DON'T need this fix
+Older models that use `_prepare_4d_causal_attention_mask` (Llama, Mistral, Qwen2, etc.) handle sample packing via axolotl's multipack attention monkeypatch instead. Only models using the new `create_causal_mask_mapping` / `create_causal_mask` masking system need the `attention_mask` removal.
+
+## Attention Backend Selection
+
+| Backend | Config | head_dim limit | torch_compile | Notes |
+|---------|--------|---------------|---------------|-------|
+| FA2 | `flash_attention: true` | 256 | ✅ | Fastest when supported |
+| FA4 | auto with `flash_attention: true` | 256 (SM90+) | ✅ | Auto-detected on H100+ |
+| SDPA | `sdp_attention: true` | None | ✅ | Universal fallback |
+| flex | `flex_attention: true` | None | ⚠️ Triton OOM for large head_dim | Good for variable head dims |
+| eager | neither set | None | ✅ | Slowest, always works |
+
+**Check model support**: Look at `_supports_flash_attn_2`, `_supports_flex_attn`, `_supports_sdpa` attributes on the model class.
+
+**head_dim gotcha**: The 256 limit is specific to flash-attn CUDA kernels, NOT PyTorch-level. SDPA and flex_attention both handle arbitrary head_dim. Models with `global_head_dim > 256` (Gemma4: 512) must use SDPA or flex.
+
+**flex + compile gotcha**: `torch_compile` with flex_attention can hit Triton shared memory OOM for large head_dim. Falls back to eager per-function (not a crash, but slower). Unsloth disables flex for Gemma4 for this reason.
+
+## Cut Cross Entropy (CCE)
+
+### How CCE patches work
+CCE replaces the model's `forward()` with a fused version that computes loss from hidden states + lm_head weight without materializing the full logits tensor. This saves ~`batch × seq_len × vocab_size × dtype_bytes` of VRAM.
+
+### Adding CCE for a new model
+1. Check if the model type is in `cut_cross_entropy.transformers.patch.PATCH_FNS`
+2. If not, axolotl's generic fallback (`integrations/cut_cross_entropy/__init__.py` `patch_llama_like()`) patches `{Prefix}ForCausalLM.forward` with `cce_forward`
+3. For multimodal models (`ForConditionalGeneration`), a model-specific patch is needed in `ml-cross-entropy` repo
+4. The multimodal `cce_forward` must accept all extra kwargs (pixel_values, mm_token_type_ids, etc.) and pop any that would conflict before calling `self.model()`
+
+### Common CCE pitfall
+If CCE appears active (log says "Applying Cut Cross Entropy") but peak VRAM doesn't decrease, check which class was patched. If the model loads as `ForConditionalGeneration` but CCE patched `ForCausalLM`, the patch is silently inactive.
+
+## MoE Models
+
+### Dense MLP vs MoE experts
+Some MoE models (e.g., Gemma4) have BOTH dense MLP layers and MoE expert layers at every decoder layer:
+- `gate_proj/up_proj/down_proj` → targets the **dense MLP** (`Gemma4TextMLP`)
+- `experts.gate_up_proj/experts.down_proj` → targets the **MoE experts** (`Gemma4TextExperts`)
+
+LoRA on the dense MLP works normally. Expert LoRA via `lora_target_parameters` requires PEFT support for the specific expert module type (may warn "Unsupported layer type").
+
+### ScatterMoE kernels
+`use_scattermoe: true` with `experts_implementation: scattermoe` registers fused expert kernels via transformers' `ExpertsInterface`. Significant speedup for MoE models. Requires the kernels plugin:
+```yaml
+plugins:
+  - axolotl.integrations.kernels.KernelsPlugin
+use_kernels: true
+use_scattermoe: true
+experts_implementation: scattermoe
+```
+
+## Where to Add Model-Specific Fixes
+
+| What | Where | Example |
+|------|-------|---------|
+| Missing forward inputs | `core/trainers/base.py` `compute_loss()` | mm_token_type_ids injection |
+| Attention mask fixes | `core/trainers/base.py` `compute_loss()` | Sample packing mask removal |
+| Loss logging fixes | `core/trainers/base.py` `__init__()` | model_accepts_loss_kwargs override |
+| PEFT/LoRA patches | `loaders/adapter.py` | ClippableLinear redirect |
+| Attention patches | `monkeypatch/attention/` | FA4 tuple fix |
+| Model-specific patches | `loaders/patch_manager.py` `_apply_model_specific_patches()` | Llama4, Kimi, NemotronH |
+| CCE patches | `ml-cross-entropy` repo `transformers/` | Per-model cce_forward |
+| Example configs | `examples/<model>/` | Validated YAML |
+| Config validation | `utils/schemas/validation.py` | Compatibility checks |

docs/agents/sft.md

@@ -91,6 +91,30 @@ Watch for: loss never decreasing (check `train_on_inputs`, dataset, LR), loss go
 | FSDP save hangs | Use `fsdp_state_dict_type: FULL_STATE_DICT` |
 | DeepSpeed CheckpointError | Set `use_reentrant: true` in `gradient_checkpointing_kwargs` |
 
+## Profiling
+
+To profile training and identify optimization opportunities:
+
+```yaml
+# Profile steps 3-7 (after warmup/autotuning settles)
+profiler_steps_start: 3
+profiler_steps: 5
+```
+
+This produces `profiler_trace.json` (Chrome trace) and `snapshot.pickle` (memory snapshot) in `output_dir`.
+View the Chrome trace at `chrome://tracing`.
+
+To programmatically inspect the trace:
+```bash
+python scripts/analyze_profile.py output_dir/
+```
+
+The trace shows per-kernel CUDA times, memory allocations, and operator-level breakdown. Look for:
+- **Large matmul kernels**: candidates for fusion or quantization
+- **Memory copies (H2D/D2H)**: unnecessary data movement
+- **Small frequent kernels**: candidates for kernel fusion
+- **Gaps between kernels**: pipeline bubbles from CPU overhead
+
 Full troubleshooting: [training_stability.qmd](../training_stability.qmd), [debugging.qmd](../debugging.qmd)
 
 ## File Map

docs/dataset-formats/conversation.qmd

@@ -108,6 +108,14 @@ datasets:
     type: chat_template
 ```
 
+::: {.callout-tip}
+`chat_template_jinja` also accepts a file path to a `.jinja2` file instead of an inline string:
+
+```yaml
+chat_template_jinja: ./path/to/my_template.jinja2
+```
+:::
+
 ::: {.callout-important}
 Please make sure that your `tokenizer.eos_token` is same as EOS (End-of-Sequence) token in template. Otherwise, set `eos_token` under `special_tokens: `.
 :::
@@ -294,6 +302,113 @@ datasets:
 It is not necessary to set both `message_field_training` and `message_field_training_detail` at once.
 :::
 
+#### Content parts with per-part training control
+
+Instead of using character offsets with `train_detail`, you can split a message's content into a list of parts, each with its own training flag. This is useful when you want to mask specific sections of a response (e.g., mask reasoning but train on the answer).
+
+```{.json filename="data.jsonl"}
+{
+  "messages": [
+    {"role": "user", "content": [{"type": "text", "text": "What is 2+2?"}]},
+    {
+      "role": "assistant",
+      "content": [
+        {"type": "text", "text": "Let me think step by step...", "train": false},
+        {"type": "text", "text": " The answer is 4.", "train": true}
+      ]
+    }
+  ]
+}
+```
+
+The configuration is the same as standard `chat_template` — no extra fields needed:
+
+```yaml
+datasets:
+  - path: ...
+    type: chat_template
+    roles_to_train: ["assistant"]
+```
+
+Each content part supports:
+
+- `type`: `"text"` (required)
+- `text`: the text value (also accepts `content` or `value` as the key)
+- `train`: `true`/`false` (optional) — whether to train on this part
+- `weight`: `0`/`1` (optional) — alternative to `train`
+
+If a part has no `train` or `weight` flag, it inherits the turn-level training decision (from `roles_to_train`, `message_field_training`, or `train_on_inputs`).
+
+::: {.callout-warning title="Whitespace at part boundaries"}
+BPE tokenizers (used by Llama, Qwen, Mistral, GPT, etc.) prepend spaces to word tokens. For example, `" answer"` is a single token — the space is part of it. This means **where you place whitespace between content parts matters**:
+
+**Split BEFORE spaces** (space goes with the next part):
+
+```json
+[
+  {"type": "text", "text": "Let me think...", "train": false},
+  {"type": "text", "text": " The answer is 4.", "train": true}
+]
+```
+
+**DON'T put trailing spaces** on a part (the space merges with the next word into one token that straddles the boundary, and straddling tokens are masked):
+
+```json
+[
+  {"type": "text", "text": "Let me think... ", "train": false},
+  {"type": "text", "text": "The answer is 4.", "train": true}
+]
+```
+
+In the bad example, `" The"` becomes a single token that spans both parts. Because it straddles the boundary, it is conservatively **masked** (not trained) — even though the second part has `train: true`.
+
+**Newlines** typically merge with preceding punctuation (e.g., `":\n"` is one token). Keep newlines with the preceding part:
+
+```json
+[
+  {"type": "text", "text": "Thinking:\n", "train": false},
+  {"type": "text", "text": "The answer is 4.", "train": true}
+]
+```
+
+Axolotl will log a warning if it detects trailing whitespace at a boundary between parts with different training flags.
+:::
+
+::: {.callout-note}
+When all content parts in a message are strings, they are concatenated before being passed to the chat template. This means content parts work with **any** Jinja template — the template sees a plain string, and the per-part training flags are applied during tokenization.
+:::
+
+##### Per-part training on reasoning_content
+
+For templates that support a separate `reasoning_content` field (e.g., `qwen3`), the same content-parts format works on `reasoning_content`. This is useful for masking incorrect reasoning steps while training on self-corrections:
+
+```{.json filename="data.jsonl"}
+{
+  "messages": [
+    {"role": "user", "content": [{"type": "text", "text": "What is 2+2?"}]},
+    {
+      "role": "assistant",
+      "reasoning_content": [
+        {"type": "text", "text": "Hmm maybe 2+2=5.", "train": false},
+        {"type": "text", "text": " Wait no, 2+2=4.", "train": true}
+      ],
+      "content": [
+        {"type": "text", "text": "The answer is 4.", "train": true}
+      ]
+    }
+  ]
+}
+```
+
+The `reasoning_content` and `content` fields are handled independently — each has its own token boundaries and per-part masking. No additional configuration is needed beyond what the template already requires.
+
+::: {.callout-tip}
+When `reasoning_content` is provided as a separate field, `split_thinking` is not needed — the reasoning is already separated from the content in the data.
+:::
+
+The same whitespace rules apply to `reasoning_content` parts as to `content` parts — split before spaces, keep newlines with the preceding part.
+
+
 #### Reasoning split
 
 (For Qwen3 template only) Enable reasoning split, where the reasoning is split from the content and passed as a separate field into the template.

docs/multimodal.qmd

@@ -8,6 +8,7 @@ format:
 
 ## Supported Models
 
+- [Gemma-4](#sec-gemma-4) *(NEW)*
 - [Mllama](#sec-mllama)
 - [Llama4](#sec-llama4)
 - [Pixtral](#sec-pixtral)
@@ -138,6 +139,40 @@ base_model: mistralai/Voxtral-Mini-3B-2507
 processor_type: VoxtralProcessor
 ```
 
+### Gemma-4 {#sec-gemma-4}
+
+All Gemma 4 variants (E2B, E4B, 26B-A4B, 31B) load as multimodal models even for text-only training.
+
+```yaml
+base_model: google/gemma-4-E2B-it  # or E4B-it, 26B-A4B, 31B
+
+chat_template: gemma4
+freeze_mm_modules: true  # freeze vision/audio encoders for text-only or vision LoRA
+
+# For the 26B-A4B MoE model, enable ScatterMoE and expert LoRA:
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+  - axolotl.integrations.kernels.KernelsPlugin
+use_kernels: true
+use_scattermoe: true
+experts_implementation: scattermoe
+
+lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+# MoE expert LoRA (3D tensors, not nn.Linear) — only for 26B-A4B:
+lora_target_parameters:
+  - experts.gate_up_proj
+  - experts.down_proj
+```
+
+::: {.callout-warning}
+Gemma 4 VLM training starts with high loss (~8-15). This is expected — see the [training stability guide](training_stability.qmd) for details.
+:::
+
+::: {.callout-tip}
+For DDP training, axolotl auto-detects Gemma4 and sets `use_reentrant=False` and `ddp_find_unused_parameters=True`. However, when `activation_offloading: true`, `ddp_find_unused_parameters` is skipped (checkpoint wrappers conflict with it); use `freeze_mm_modules: true` instead to handle unused vision/audio params. For FSDP2, use `fsdp_transformer_layer_cls_to_wrap: Gemma4TextDecoderLayer`.
+:::
+
 ### Gemma-3 {#sec-gemma-3}
 
 ::: {.callout-tip}

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@fec1a88\""
    ]
   },
   {

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

@@ -26,8 +26,8 @@ output_dir: ./outputs/out
 
 # Freeze vision tower
 unfrozen_parameters:
-  - ^model\.language_model\..*
-  - ^lm_head\..*
+  - ^model.language_model.*
+  - ^lm_head.*
 
 adapter: qlora
 lora_r: 32

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

@@ -26,8 +26,8 @@ output_dir: ./outputs/out
 
 # Freeze vision tower
 unfrozen_parameters:
-  - ^model\.language_model\..*
-  - ^lm_head\..*
+  - ^model.language_model.*
+  - ^lm_head.*
 
 adapter: qlora
 lora_r: 32

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

@@ -22,8 +22,8 @@ output_dir: ./outputs/out
 
 # Freeze vision tower
 unfrozen_parameters:
-  - ^model\.language_model\..*
-  - ^lm_head\..*
+  - ^model.language_model.*
+  - ^lm_head.*
 
 adapter: qlora
 lora_model_dir:

examples/gemma4/26b-a4b-moe-qlora.yaml

@@ -1,19 +1,12 @@
 # Gemma 4 26B-A4B MoE QLoRA with ScatterMoE kernels
 #
-# Validated: 50 steps on FineTome-100k, loss 7.4 -> 2.4, single RTX 5090 (32GB)
+# Validated: 50 steps on FineTome-100k, loss 8.8 -> 1.8, single RTX 5090 (32GB)
+# torch_compile=true: 21 GiB peak VRAM, ~230 tok/s, 336s total
 #
 # Key notes:
-# - Flash Attention 2 is NOT supported (global_head_dim=512 > FA2 max of 256).
-#   Use sdp_attention instead.
-# - Gemma 4 is multimodal (text+vision+audio). For text-only SFT, restrict
-#   LoRA to the text backbone via lora_target_linear_modules regex.
-# - MoE experts use `experts_implementation: scattermoe` — Gemma 4 embeds MoE
-#   directly in the decoder layer (no SparseMoeBlock), so we register ScatterMoE
-#   via the transformers ExpertsInterface.
-# - Expert LoRA targets are `experts.gate_up_proj` / `experts.down_proj`
-#   (no `mlp.` prefix, unlike Qwen/Mixtral).
-# - micro_batch_size: 1 fits 2048 seq_len on 32GB GPU with SDP attention.
-#   Use micro_batch_size: 4 with 1024 seq_len, or on 48GB+ GPUs.
+# - Max sequence length on 32GB GPU: 2048 (micro_batch_size=1, SDP attention).
+#   4096 seq_len OOMs due to head_dim=512 math SDP materializing full score matrix.
+#   Use 48GB+ GPUs for longer sequences or multi-GPU with FSDP.
 
 base_model: google/gemma-4-26B-A4B
 
@@ -24,7 +17,7 @@ plugins:
 use_kernels: true
 use_scattermoe: true
 experts_implementation: scattermoe
-torch_compile: false
+torch_compile: true
 liger_layer_norm: true
 liger_rope: true
 liger_rms_norm: true
@@ -54,12 +47,9 @@ lora_r: 16
 lora_alpha: 32
 lora_dropout: 0
 
-# Restrict LoRA to text backbone only (skip vision/audio encoders).
-# lora_target_modules is intentionally empty — all module targeting is done
-# via regex in lora_target_linear_modules below.
-lora_target_modules: []
-lora_target_linear_modules:
-  - language_model\.model\.layers\.\d+\.self_attn\.(q|k|v|o)_proj
+# Restrict LoRA to text backbone only (skip vision/audio encoders)
+# using regex to match only the text decoder attention projections.
+lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
 
 # MoE expert LoRA (3D Parameter tensors, not nn.Linear)
 lora_target_parameters:
@@ -73,7 +63,7 @@ lora_o_kernel: false
 bnb_config_kwargs:
   bnb_4bit_use_double_quant: true
 
-wandb_project: gemma4-qlora
+wandb_project:
 wandb_entity:
 wandb_watch:
 wandb_name:
@@ -93,8 +83,7 @@ gradient_checkpointing: true
 activation_offloading: true
 logging_steps: 1
 
-# FA2 not supported — Gemma4 global_head_dim=512 exceeds FA2 max of 256
-flash_attention: false
+# FA2 not supported
 sdp_attention: true
 
 warmup_ratio: 0.1

examples/gemma4/31b-qlora-flex.yaml

@@ -0,0 +1,71 @@
+base_model: google/gemma-4-31B
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+  - axolotl.integrations.liger.LigerPlugin
+torch_compile: true
+liger_layer_norm: true
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_rms_norm_gated: true
+strict: false
+
+chat_template: gemma4
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:10%]
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+val_set_size: 0.05
+output_dir: ./outputs/gemma4-31b-qlora-flex
+
+sequence_len: 2048
+sample_packing: true
+
+load_in_4bit: true
+adapter: qlora
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0
+
+# Restrict LoRA to text backbone only (skip vision/audio encoders)
+lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+lora_mlp_kernel: false
+lora_qkv_kernel: false
+lora_o_kernel: false
+
+bnb_config_kwargs:
+  bnb_4bit_use_double_quant: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+optimizer: adamw_torch_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+activation_offloading: true
+logging_steps: 1
+
+# FA not supported
+flex_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 4
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/gemma4/31b-qlora.yaml

@@ -0,0 +1,69 @@
+base_model: google/gemma-4-31B
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+  - axolotl.integrations.liger.LigerPlugin
+torch_compile: false
+liger_layer_norm: true
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_rms_norm_gated: true
+strict: false
+
+chat_template: gemma4
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:10%]
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+val_set_size: 0.05
+output_dir: ./outputs/gemma4-31b-qlora
+
+sequence_len: 2048
+sample_packing: true
+
+load_in_4bit: true
+adapter: qlora
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0
+
+# Restrict LoRA to text backbone only (skip vision/audio encoders)
+# using regex to match only the text decoder attention projections.
+lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+bnb_config_kwargs:
+  bnb_4bit_use_double_quant: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+activation_offloading: true
+logging_steps: 1
+
+# FA not supported
+sdp_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 4
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/gemma4/README.md

@@ -0,0 +1,60 @@
+# Finetune Google's Gemma 4 with Axolotl
+
+[Gemma 4](https://huggingface.co/collections/google/gemma-4) is a family of multimodal models from Google. This guide covers how to train them with Axolotl.
+
+## Getting started
+
+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.
+
+3. Run the finetuning example:
+
+```bash
+# 26B MoE QLoRA (1x80GB @ ~50 GiB)
+axolotl train examples/gemma4/26b-a4b-moe-qlora.yaml
+
+# 31B Dense QLoRA (1x80GB @ ~44 GiB)
+axolotl train examples/gemma4/31b-qlora.yaml
+
+# 31B Dense QLoRA Flex Attn (1x80GB @ ~26 GiB)
+axolotl train examples/gemma4/31b-qlora-flex.yaml
+```
+
+### MoE Expert Quantization & Expert LoRA (26B-A4B only)
+
+The 26B-A4B config uses ScatterMoE kernels via the transformers `ExpertsInterface` and quantizes expert weights on load. To learn about expert quantization, expert LoRA targeting, and related limitations, see the [MoE Expert Quantization](https://docs.axolotl.ai/docs/expert_quantization.html) docs.
+
+## Flex Attention
+
+Reduce ~40% VRAM (at the cost of up to half throughput) by setting the below (shown in `examples/gemma4/31b-qlora-flex.yaml`):
+
+```yaml
+torch_compile: true
+flex_attention: true
+```
+
+This works for both the MoE and Dense model.
+
+## Limitations
+
+- **Flash Attention**: FA2 (max head_dim=256) and FA4 (max head_dim=128) cannot support Gemma 4's `global_head_dim=512`. Use SDP or flex attention instead.
+- **LoRA kernels**: Not supported due to KV-sharing layers.
+- **lora_target_linear**: Incompatible for multimodal models — use `lora_target_modules` with a regex to restrict LoRA to the text backbone.
+
+### TIPS
+
+- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+- You can run full finetuning by removing `adapter: qlora`, `load_in_4bit: true`, and `quantize_moe_experts: true` from the config. This is heavy and has not been tested.
+
+## Optimization Guides
+
+Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
+
+## Related Resources
+
+- [Gemma 4 Blog](https://huggingface.co/blog/gemma4)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl Website](https://axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/gemma4/e2b-vision-lora.yaml

@@ -0,0 +1,62 @@
+# Gemma 4 E2B Vision LoRA
+#
+# Fine-tuning LM LoRA adapters on multimodal Gemma4 with vision/multimodal modules frozen.
+# Uses the base ProcessingStrategy (auto-detects image_token from processor).
+
+base_model: google/gemma-4-E2B-it
+processor_type: AutoProcessor
+freeze_mm_modules: true
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+strict: false
+
+# Required for vision/multimodal training
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+chat_template: gemma4
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:100]
+
+val_set_size: 0
+output_dir: ./outputs/gemma4-e2b-vision-lora
+
+adapter: lora
+sequence_len: 2048
+pad_to_sequence_len: false
+
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0
+# Target language model only — vision encoder is frozen via freeze_mm_modules
+lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+max_steps: 10
+optimizer: adamw_torch_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+logging_steps: 1
+sdp_attention: true
+
+warmup_ratio: 0.1
+weight_decay: 0.0
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:

examples/nemotron-h/120b-a12b-qlora.yaml

@@ -1,5 +1,15 @@
 base_model: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
 
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+  - axolotl.integrations.liger.LigerPlugin
+
+liger_layer_norm: true
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_rms_norm_gated: true
+
 # LoRA kernel patches are incompatible with this architecture — see README.
 lora_mlp_kernel: false
 lora_qkv_kernel: false
@@ -22,8 +32,6 @@ dataset_prepared_path: last_run_prepared
 sequence_len: 4096
 sample_packing: true
 
-use_cut_cross_entropy: true
-
 load_in_4bit: true
 quantize_moe_experts: true
 adapter: qlora
@@ -31,16 +39,16 @@ lora_r: 16
 lora_alpha: 32
 lora_dropout: 0.0
 lora_target_modules:
-  # Attention projection layers (present in ~12 attention layers out of 88)
   - q_proj
   - k_proj
   - v_proj
   - o_proj
-  # To also train MoE expert weights, add them via lora_target_parameters
-  # (they are 3D nn.Parameter tensors, not nn.Linear — no gate_proj):
-  #   lora_target_parameters:
-  #     - up_proj
-  #     - down_proj
+
+# To also train MoE expert weights, add them via lora_target_parameters
+# (they are 3D nn.Parameter tensors, not nn.Linear — no gate_proj):
+# lora_target_parameters:
+#   - up_proj
+#   - down_proj
 
 wandb_project:
 wandb_entity:

examples/nemotron-h/nano-30b-a3b-qlora.yaml

@@ -1,6 +1,16 @@
 # See examples/nemotron-h/README.md for architecture notes and requirements.
 base_model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
 
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+  - axolotl.integrations.liger.LigerPlugin
+
+liger_layer_norm: true
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_rms_norm_gated: true
+
 # LoRA kernel patches are incompatible with this architecture — see README.
 lora_mlp_kernel: false
 lora_qkv_kernel: false
@@ -23,8 +33,6 @@ dataset_prepared_path: last_run_prepared
 sequence_len: 4096
 sample_packing: true
 
-use_cut_cross_entropy: true
-
 load_in_4bit: true
 quantize_moe_experts: true
 adapter: qlora
@@ -36,11 +44,12 @@ lora_target_modules:
   - k_proj
   - v_proj
   - o_proj
-  # To also train MoE expert weights, add them via lora_target_parameters
-  # (they are 3D nn.Parameter tensors, not nn.Linear — no gate_proj):
-  #   lora_target_parameters:
-  #     - up_proj
-  #     - down_proj
+
+# To also train MoE expert weights, add them via lora_target_parameters
+# (they are 3D nn.Parameter tensors, not nn.Linear — no gate_proj):
+# lora_target_parameters:
+#   - up_proj
+#   - down_proj
 
 wandb_project:
 wandb_entity:

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

@@ -26,8 +26,8 @@ sample_packing: true
 
 # Freeze vision encoder
 unfrozen_parameters:
-  - model\.language_model\..*
-  - lm_head\..*
+  - model.language_model.*
+  - lm_head.*
 
 wandb_project:
 wandb_entity:

examples/qwen3.5/35b-a3b-moe-vision-lora.yaml

@@ -0,0 +1,62 @@
+# Qwen 3.5 35B-A3B MoE Vision LoRA
+#
+# Vision fine-tuning of the hybrid DeltaNet + Attention MoE model.
+# 256 experts, 8 active per token, with early-fusion vision support.
+
+base_model: Qwen/Qwen3.5-35B-A3B
+processor_type: AutoProcessor
+
+# Required for vision/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[:100]
+
+val_set_size: 0
+output_dir: ./outputs/qwen35-35b-a3b-vision-lora
+
+adapter: lora
+sequence_len: 4096
+pad_to_sequence_len: false
+
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0
+lora_target_modules:
+  - q_proj
+  - k_proj
+  - v_proj
+  - o_proj
+  - down_proj
+  - up_proj
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+max_steps: 10
+optimizer: adamw_torch_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+weight_decay: 0.0
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:

requirements.txt

@@ -10,15 +10,15 @@ liger-kernel==0.7.0
 
 packaging==26.0
 huggingface_hub>=1.1.7
-peft>=0.18.1
+peft>=0.19.0,<0.20.0
 tokenizers>=0.22.1
-transformers==5.5.0
+transformers==5.5.4
 accelerate==1.13.0
-datasets==4.5.0
+datasets>=4.8.4,<4.9.0
 deepspeed>=0.18.6,<0.19.0
-trl==0.29.0
-hf_xet==1.3.2
-kernels==0.12.2
+trl==1.1.0
+hf_xet==1.4.3
+kernels==0.13.0
 
 fla-core==0.4.1
 flash-linear-attention==0.4.1

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@fec1a88"'
 )

setup.py

@@ -89,7 +89,7 @@ def parse_requirements(extras_require_map):
                 ]
                 if not install_xformers:
                     _install_requires.pop(_install_requires.index(xformers_version))
-                extras_require_map["vllm"] = ["vllm>=0.17.1"]
+                extras_require_map["vllm"] = ["vllm>=0.19.1"]
             elif (major, minor) >= (2, 9):
                 extras_require_map.pop("fbgemm-gpu")
                 extras_require_map["fbgemm-gpu"] = [

src/axolotl/cli/agent_docs/__init__.py

@@ -0,0 +1,108 @@
+"""Bundled agent documentation for axolotl.
+
+These docs are optimized for consumption by AI coding agents.
+The source of truth is docs/agents/*.md and AGENTS.md in the repo root.
+This module resolves those paths at runtime — no files are duplicated
+into the package.
+
+For pip-only installs (no repo checkout), run `axolotl fetch docs` first
+to download the docs locally.
+"""
+
+from pathlib import Path
+
+# Topic name -> (filename in docs/agents/, fallback filename for AGENTS.md)
+TOPICS = {
+    "overview": "AGENTS.md",
+    "sft": "docs/agents/sft.md",
+    "grpo": "docs/agents/grpo.md",
+    "preference_tuning": "docs/agents/preference_tuning.md",
+    "reward_modelling": "docs/agents/reward_modelling.md",
+    "pretraining": "docs/agents/pretraining.md",
+    "model_architectures": "docs/agents/model_architectures.md",
+    "new_model_support": "docs/agents/new_model_support.md",
+}
+
+
+def _find_repo_root() -> Path | None:
+    """Walk up from this file to find the repo root (contains AGENTS.md)."""
+    # In an editable install or repo checkout, walk up from
+    # src/axolotl/cli/agent_docs/ to find the repo root
+    current = Path(__file__).resolve().parent
+    while current != current.parent:
+        if (current / "AGENTS.md").exists() and (current / "docs" / "agents").is_dir():
+            return current
+        current = current.parent
+    return None
+
+
+def _find_docs_dir() -> Path | None:
+    """Find a fetched docs directory (from `axolotl fetch docs`)."""
+    # axolotl fetch docs --dest defaults to ./docs/ in cwd
+    cwd_docs = Path.cwd() / "docs" / "agents"
+    if cwd_docs.is_dir():
+        return Path.cwd()
+    return None
+
+
+def _resolve_path(topic: str) -> Path:
+    """Resolve a topic name to the actual file path."""
+    if topic not in TOPICS:
+        available = ", ".join(sorted(TOPICS.keys()))
+        raise FileNotFoundError(f"Unknown topic: {topic!r}. Available: {available}")
+
+    relative_path = TOPICS[topic]
+
+    # Try repo root first (editable install / repo checkout)
+    repo_root = _find_repo_root()
+    if repo_root:
+        candidate = repo_root / relative_path
+        if candidate.exists():
+            return candidate
+
+    # Try cwd (fetched docs via `axolotl fetch docs`)
+    docs_root = _find_docs_dir()
+    if docs_root:
+        candidate = docs_root / relative_path
+        if candidate.exists():
+            return candidate
+
+    # Also check cwd directly for AGENTS.md
+    if topic == "overview":
+        cwd_agents = Path.cwd() / "AGENTS.md"
+        if cwd_agents.exists():
+            return cwd_agents
+
+    raise FileNotFoundError(
+        f"Could not find {relative_path!r}. "
+        f"If you installed axolotl via pip, run `axolotl fetch docs` first "
+        f"to download the documentation."
+    )
+
+
+def get_doc(topic: str = "overview") -> str:
+    """Return the content of an agent doc by topic name.
+
+    Args:
+        topic: One of the keys in TOPICS, or "overview" (default).
+
+    Returns:
+        The markdown content of the doc.
+
+    Raises:
+        FileNotFoundError: If the topic can't be found.
+    """
+    return _resolve_path(topic).read_text()
+
+
+def list_topics() -> dict[str, str]:
+    """Return a dict of topic name -> first line (title) of each doc."""
+    result = {}
+    for topic in sorted(TOPICS.keys()):
+        try:
+            path = _resolve_path(topic)
+            first_line = path.read_text().split("\n", 1)[0].lstrip("# ").strip()
+            result[topic] = first_line
+        except FileNotFoundError:
+            result[topic] = "(not found — run `axolotl fetch docs`)"
+    return result

src/axolotl/cli/main.py

@@ -294,7 +294,9 @@ def merge_lora(config: str, **kwargs):
 
 
 @cli.command()
-@click.argument("directory", type=click.Choice(["examples", "deepspeed_configs"]))
+@click.argument(
+    "directory", type=click.Choice(["examples", "deepspeed_configs", "docs"])
+)
 @click.option("--dest", help="Destination directory")
 def fetch(directory: str, dest: Optional[str]):
     """
@@ -303,9 +305,10 @@ def fetch(directory: str, dest: Optional[str]):
     Available directories:
     - examples: Example configuration files
     - deepspeed_configs: DeepSpeed configuration files
+    - docs: Full documentation (Quarto markdown files)
 
     Args:
-        directory: One of `examples`, `deepspeed_configs`.
+        directory: One of `examples`, `deepspeed_configs`, `docs`.
         dest: Optional destination directory.
     """
     fetch_from_github(f"{directory}/", dest)
@@ -340,6 +343,112 @@ def delinearize_llama4(model: str, output: str):
     do_delinearize_llama4(model, output)
 
 
+@cli.command("agent-docs")
+@click.argument("topic", required=False, default=None)
+@click.option("--list", "list_topics", is_flag=True, help="List available topics")
+def agent_docs(topic: Optional[str], list_topics: bool):
+    """Show agent-optimized documentation.
+
+    Prints reference docs designed for AI coding agents.
+    These docs are bundled with the package — no network access needed.
+
+    \b
+    Examples:
+        axolotl agent-docs              # overview (start here)
+        axolotl agent-docs grpo         # GRPO reference
+        axolotl agent-docs sft          # SFT reference
+        axolotl agent-docs --list       # list all topics
+    """
+    from axolotl.cli.agent_docs import get_doc, list_topics as _list_topics
+
+    if list_topics:
+        for name, title in _list_topics().items():
+            click.echo(f"  {name:25s} {title}")
+        return
+
+    if topic is None:
+        topic = "overview"
+
+    try:
+        click.echo(get_doc(topic))
+    except FileNotFoundError as exc:
+        raise click.BadParameter(str(exc)) from exc
+
+
+@cli.command("config-schema")
+@click.option(
+    "--format",
+    "output_format",
+    type=click.Choice(["json", "yaml"]),
+    default="json",
+    help="Output format (default: json)",
+)
+@click.option("--field", help="Show schema for a specific field only")
+def config_schema(output_format: str, field: Optional[str]):
+    """Dump the full config JSON schema.
+
+    Useful for AI agents and tooling to discover all available config options,
+    their types, defaults, and descriptions.
+
+    \b
+    Examples:
+        axolotl config-schema                    # full JSON schema
+        axolotl config-schema --format yaml      # YAML format
+        axolotl config-schema --field adapter     # single field
+    """
+    import json
+
+    try:
+        schema = AxolotlInputConfig.model_json_schema()
+    except (TypeError, ValueError, AttributeError) as exc:
+        # Fallback: dump field names, types, and defaults when full schema
+        # generation fails (e.g. torch.dtype not JSON-serializable)
+        LOG.warning(
+            "Full JSON schema generation failed, using simplified fallback: %s", exc
+        )
+        fields = {}
+        for name, field_info in AxolotlInputConfig.model_fields.items():
+            entry = {}
+            if field_info.description:
+                entry["description"] = field_info.description
+            if field_info.default is not None:
+                try:
+                    json.dumps(field_info.default)
+                    entry["default"] = field_info.default
+                except (TypeError, ValueError):
+                    entry["default"] = str(field_info.default)
+            annotation = field_info.annotation
+            if annotation is not None:
+                entry["type"] = str(annotation)
+            fields[name] = entry
+        schema = {
+            "properties": fields,
+            "_note": "simplified schema (full generation failed)",
+        }
+
+    if field:
+        props = schema.get("properties", {})
+        if field not in props:
+            # Try case-insensitive match
+            matches = [k for k in props if k.lower() == field.lower()]
+            if matches:
+                field = matches[0]
+            else:
+                raise click.BadParameter(
+                    f"Unknown field: {field!r}. "
+                    f"Omit --field to dump the full schema, "
+                    f"or pipe to jq: axolotl config-schema | jq '.properties | keys'"
+                )
+        schema = {field: props[field]}
+
+    if output_format == "yaml":
+        import yaml  # pylint: disable=import-outside-toplevel
+
+        click.echo(yaml.dump(schema, default_flow_style=False, sort_keys=False))
+    else:
+        click.echo(json.dumps(schema, indent=2))
+
+
 cli.add_command(lm_eval)
 
 

src/axolotl/cli/merge_lora.py

@@ -115,6 +115,7 @@ def _do_merge_lora_efficient(*, cfg: DictDefault) -> None:
         simulate_nf4_experts=simulate_nf4_experts,
         nf4_blocksize=nf4_blocksize,
         nf4_double_quant=nf4_double_quant,
+        trust_remote_code=bool(getattr(cfg, "trust_remote_code", False)),
     )
 
     LOG.debug("Memory-efficient LoRA merge completed successfully!")

src/axolotl/cli/utils/lora_merge.py

@@ -17,6 +17,93 @@ from axolotl.utils.logging import get_logger
 LOG = get_logger(__name__)
 
 
+def _build_layer_type_map(
+    base_model_path: Path, trust_remote_code: bool = False
+) -> dict[str, str]:
+    """Build a map of module_name -> layer_type using a meta-device model.
+
+    Instantiates the model architecture on the meta device (zero memory)
+    to inspect which modules are Linear vs Conv1d/Conv2d/Conv3d.
+    This avoids relying on weight tensor ndim heuristics.
+    """
+    import json as _json
+
+    import torch.nn as nn
+    from transformers import AutoConfig
+
+    config_path = base_model_path / "config.json"
+    if not config_path.exists():
+        return {}
+
+    try:
+        with open(config_path) as f:
+            model_config = _json.load(f)
+    except (OSError, _json.JSONDecodeError):
+        return {}
+
+    architectures = model_config.get("architectures", [])
+    if not architectures:
+        return {}
+
+    try:
+        config = AutoConfig.from_pretrained(
+            str(base_model_path), trust_remote_code=trust_remote_code
+        )
+    except Exception:
+        LOG.debug("Could not load config for layer type introspection")
+        return {}
+
+    # Determine the right Auto class from architectures
+    from transformers import (
+        AutoModel,
+        AutoModelForCausalLM,
+    )
+
+    auto_classes = [AutoModelForCausalLM, AutoModel]
+    try:
+        from transformers import AutoModelForImageTextToText
+
+        auto_classes.insert(0, AutoModelForImageTextToText)
+    except ImportError:
+        pass
+
+    model = None
+    for auto_cls in auto_classes:
+        try:
+            with torch.device("meta"):
+                model = auto_cls.from_config(
+                    config, trust_remote_code=trust_remote_code
+                )
+            break
+        except Exception:  # noqa: BLE001
+            LOG.debug(
+                "Could not instantiate meta model with %s, trying next",
+                auto_cls.__name__,
+            )
+
+    if model is None:
+        LOG.debug("Could not instantiate meta model for layer type introspection")
+        return {}
+
+    layer_types = {}
+    for name, module in model.named_modules():
+        if isinstance(module, nn.Conv3d):
+            layer_types[name] = "Conv3d"
+        elif isinstance(module, nn.Conv2d):
+            layer_types[name] = "Conv2d"
+        elif isinstance(module, nn.Conv1d):
+            layer_types[name] = "Conv1d"
+        elif isinstance(module, nn.Linear):
+            layer_types[name] = "Linear"
+
+    del model
+    LOG.debug(
+        f"Layer type map: {len(layer_types)} modules "
+        f"({sum(1 for v in layer_types.values() if 'Conv' in v)} conv layers)"
+    )
+    return layer_types
+
+
 def _simulate_nf4_roundtrip(
     tensor: torch.Tensor,
     blocksize: Optional[int] = None,
@@ -191,6 +278,7 @@ def _build_peft_layer_and_get_delta(
     adapter_name: str = "default",
     is_param_wrapper: bool = False,
     magnitude: Optional[torch.Tensor] = None,
+    layer_type: Optional[str] = None,
 ) -> torch.Tensor:
     """
     Use PEFT's own layer classes to compute the LoRA delta weight.
@@ -211,7 +299,7 @@ def _build_peft_layer_and_get_delta(
     out_features = lora_b.shape[0]
     lora_alpha = lora_config_dict.get("lora_alpha", lora_config_dict.get("r", 1))
     use_rslora = bool(lora_config_dict.get("use_rslora", False))
-    use_dora = bool(lora_config_dict.get("use_dora", False)) and magnitude is not None
+    use_dora = bool(lora_config_dict.get("use_dora", False))
 
     if is_param_wrapper:
         from peft.tuners.lora.layer import ParamWrapper
@@ -227,18 +315,106 @@ def _build_peft_layer_and_get_delta(
             "weight", nn.Parameter(base_tensor.clone(), requires_grad=False)
         )
 
+        # ParamWrapper rejects dropout/fan_in_fan_out/lora_bias/use_dora, so
+        # build a minimal config with only the fields it accepts.
+        pw_config = LoraConfig(
+            r=r,
+            lora_alpha=lora_alpha,
+            lora_dropout=0.0,
+            fan_in_fan_out=False,
+            use_rslora=use_rslora,
+            use_dora=False,
+            lora_bias=False,
+        )
+
         with warnings.catch_warnings():
             warnings.simplefilter("ignore", UserWarning)
             layer = ParamWrapper(
                 fake,
                 adapter_name=adapter_name,
                 parameter_name="weight",
+                config=pw_config,
                 r=r,
                 lora_alpha=lora_alpha,
-                use_rslora=use_rslora,
             )
         layer.lora_A[adapter_name].weight.data = lora_a
         layer.lora_B[adapter_name].weight.data = lora_b
+        return layer.get_delta_weight(adapter_name)
+    elif (
+        layer_type and "Conv" in layer_type or (layer_type is None and lora_a.ndim > 2)
+    ):
+        # Conv layer detected via model introspection (or ndim fallback)
+
+        from peft.tuners.lora import layer as peft_lora_layer
+
+        # Determine conv type from layer_type map or fall back to ndim
+        if layer_type and "Conv" in layer_type:
+            conv_type: str = layer_type
+        else:
+            ndim = lora_a.ndim
+            _conv_map = {3: "Conv1d", 4: "Conv2d", 5: "Conv3d"}
+            if ndim not in _conv_map:
+                raise ValueError(
+                    f"Unsupported LoRA weight dimensionality {ndim} for conv layer"
+                )
+            conv_type = _conv_map[ndim]
+            LOG.warning(
+                f"Using ndim-based fallback for conv detection (ndim={ndim}). "
+                f"Consider providing layer_type from meta-device introspection."
+            )
+
+        conv_cls_map = {"Conv1d": nn.Conv1d, "Conv2d": nn.Conv2d, "Conv3d": nn.Conv3d}
+        ConvCls = conv_cls_map[conv_type]
+        PeftConvCls = getattr(peft_lora_layer, conv_type)
+
+        # Reconstruct conv parameters from base tensor and lora_a shapes
+        # base_tensor: [out_channels, in_channels/groups, *kernel_size]
+        # lora_a:      [r, in_channels/groups, *kernel_size]
+        # lora_b:      [out_channels, r, *ones]
+        out_channels = base_tensor.shape[0]
+        in_channels = base_tensor.shape[1]
+        kernel_size = tuple(base_tensor.shape[2:])
+        stride = (1,) * (base_tensor.ndim - 2)
+        padding = (0,) * (base_tensor.ndim - 2)
+
+        base_layer = ConvCls(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False,
+        )
+        base_layer.weight.data = base_tensor.clone()
+
+        conv_config = LoraConfig(
+            r=r_total,
+            lora_alpha=lora_alpha,
+            use_rslora=use_rslora,
+            use_dora=use_dora,
+        )
+        layer = PeftConvCls(
+            base_layer,
+            adapter_name=adapter_name,
+            config=conv_config,
+            r=r_total,
+            lora_alpha=lora_alpha,
+        )
+        layer.lora_A[adapter_name].weight.data = lora_a
+        layer.lora_B[adapter_name].weight.data = lora_b
+
+        if use_dora:
+            if magnitude is None:
+                raise ValueError(
+                    f"DoRA merge requires a magnitude vector but none was found "
+                    f"for conv layer (adapter={adapter_name}). Check that the "
+                    f"adapter checkpoint contains lora_magnitude_vector weights."
+                )
+            mag_layer = layer.lora_magnitude_vector[adapter_name]
+            mag_layer.weight = nn.Parameter(magnitude)
+            layer.merge(adapter_names=[adapter_name])
+            return base_layer.weight.data - base_tensor
+
         return layer.get_delta_weight(adapter_name)
     else:
         from peft.tuners.lora.layer import Linear as LoraLinear
@@ -251,15 +427,20 @@ def _build_peft_layer_and_get_delta(
             or lora_config_dict.get("lora_fan_in_fan_out", False)
         )
 
-        layer = LoraLinear(
-            base_layer,
-            adapter_name=adapter_name,
+        linear_config = LoraConfig(
             r=r_total,
             lora_alpha=lora_alpha,
             fan_in_fan_out=fan_in_fan_out,
             use_rslora=use_rslora,
             use_dora=use_dora,
         )
+        layer = LoraLinear(
+            base_layer,
+            adapter_name=adapter_name,
+            config=linear_config,
+            r=r_total,
+            lora_alpha=lora_alpha,
+        )
         layer.lora_A[adapter_name].weight.data = lora_a
         layer.lora_B[adapter_name].weight.data = lora_b
 
@@ -267,6 +448,12 @@ def _build_peft_layer_and_get_delta(
             # DoRA merges magnitude normalization into the weight directly.
             # Use PEFT's merge() which handles DoRA internally, then
             # compute the delta as merged_weight - original_weight.
+            if magnitude is None:
+                raise ValueError(
+                    f"DoRA merge requires a magnitude vector but none was found "
+                    f"for linear layer (adapter={adapter_name}). Check that the "
+                    f"adapter checkpoint contains lora_magnitude_vector weights."
+                )
             mag_layer = layer.lora_magnitude_vector[adapter_name]
             mag_layer.weight = nn.Parameter(magnitude)
             layer.merge(adapter_names=[adapter_name])
@@ -382,6 +569,7 @@ def _merge_tensor_with_lora(
     nf4_double_quant: bool = True,
     use_dora: bool = False,
     weight_renamings: Optional[Dict[str, str]] = None,
+    layer_type_map: Optional[Dict[str, str]] = None,
 ) -> tuple[torch.Tensor, bool]:
     """
     Helper function to merge a single tensor with its corresponding LoRA weights.
@@ -426,12 +614,30 @@ def _merge_tensor_with_lora(
             if use_dora
             else None
         )
+
+        # Look up layer type from meta-device model introspection
+        _layer_type = None
+        if layer_type_map:
+            mod_path = key.rsplit(".weight", 1)[0] if key.endswith(".weight") else key
+            _layer_type = layer_type_map.get(mod_path)
+            # Try common prefix variations (e.g. with/without "model." prefix)
+            if _layer_type is None:
+                for prefix in [
+                    "model.",
+                    "model.language_model.",
+                    "model.language_model.model.",
+                ]:
+                    _layer_type = layer_type_map.get(prefix + mod_path)
+                    if _layer_type:
+                        break
+
         delta = _build_peft_layer_and_get_delta(
             lora_a.to(device),
             lora_b.to(device),
             lora_config_dict,
             tensor.to(device),
             magnitude=magnitude.to(device) if magnitude is not None else None,
+            layer_type=_layer_type,
         )
         merged_tensor = (
             (tensor.to(device).to(torch.float32) + delta.to(torch.float32))
@@ -556,6 +762,7 @@ def _fuse_and_unfuse_with_merge(
     nf4_double_quant: bool = True,
     use_dora: bool = False,
     weight_renamings: Optional[Dict[str, str]] = None,
+    layer_type_map: Optional[Dict[str, str]] = None,
 ) -> tuple[Dict[str, torch.Tensor], int, set]:
     """
     For tensors matching WeightConverter patterns (MoE expert weights):
@@ -696,12 +903,32 @@ def _fuse_and_unfuse_with_merge(
                     if use_dora
                     else None
                 )
+                # Look up layer type for the fused key
+                _layer_type = None
+                if layer_type_map:
+                    mod_path = (
+                        fused_key.rsplit(".weight", 1)[0]
+                        if fused_key.endswith(".weight")
+                        else fused_key
+                    )
+                    _layer_type = layer_type_map.get(mod_path)
+                    if _layer_type is None:
+                        for prefix in [
+                            "model.",
+                            "model.language_model.",
+                            "model.language_model.model.",
+                        ]:
+                            _layer_type = layer_type_map.get(prefix + mod_path)
+                            if _layer_type:
+                                break
+
                 delta = _build_peft_layer_and_get_delta(
                     lora_a.to(device),
                     lora_b.to(device),
                     lora_config_dict,
                     fused_tensor.to(device),
                     magnitude=magnitude.to(device) if magnitude is not None else None,
+                    layer_type=_layer_type,
                 )
                 fused_tensor = (
                     (
@@ -740,6 +967,7 @@ def merge_lora_sharded_efficient(
     simulate_nf4_experts: bool = False,
     nf4_blocksize: Optional[int] = None,
     nf4_double_quant: bool = True,
+    trust_remote_code: bool = False,
 ) -> None:
     """
     Memory-efficient LoRA merging that processes shards individually
@@ -750,6 +978,8 @@ def merge_lora_sharded_efficient(
         simulate_nf4_experts: Apply NF4 roundtrip only to MoE expert tensors
             (for quantize_moe_experts). Expert tensors are identified by having
             "expert" in the key name and ndim >= 3.
+        trust_remote_code: Whether to trust remote code when loading model
+            config for layer-type introspection. Defaults to False for safety.
     """
     base_model_path = Path(base_model_path)
     lora_adapter_path = Path(lora_adapter_path)
@@ -780,6 +1010,10 @@ def merge_lora_sharded_efficient(
 
     use_dora = bool(lora_config_dict.get("use_dora", False))
 
+    # Build layer type map via meta-device model introspection
+    layer_type_map = _build_layer_type_map(
+        base_model_path, trust_remote_code=trust_remote_code
+    )
     unsupported_methods = []
 
     # Check for AdaLoRA (Adaptive LoRA)
@@ -904,6 +1138,7 @@ def merge_lora_sharded_efficient(
                 nf4_double_quant=nf4_double_quant,
                 use_dora=use_dora,
                 weight_renamings=weight_renamings,
+                layer_type_map=layer_type_map,
             )
             merged_count += fused_merged
 
@@ -926,6 +1161,7 @@ def merge_lora_sharded_efficient(
                 nf4_double_quant=nf4_double_quant,
                 use_dora=use_dora,
                 weight_renamings=weight_renamings,
+                layer_type_map=layer_type_map,
             )
             merged_tensors[key] = merged_tensor
             if was_merged:

src/axolotl/core/builders/base.py

@@ -41,6 +41,7 @@ from axolotl.utils.callbacks import (
     GCCallback,
     SaveAxolotlConfigtoWandBCallback,
     SaveModelOnFirstStepCallback,
+    SkipEvalOnResumeCallback,
 )
 from axolotl.utils.callbacks.profiler import PytorchProfilerCallback
 from axolotl.utils.distributed import build_parallelism_config
@@ -118,6 +119,9 @@ class TrainerBuilderBase(abc.ABC):
             plugin_manager.add_callbacks_pre_trainer(cfg=self.cfg, model=self.model)
         )
 
+        if self.cfg.resume_from_checkpoint:
+            callbacks.append(SkipEvalOnResumeCallback())
+
         if self.cfg.gc_steps:
             callbacks.append(GCCallback(gc_steps=self.cfg.gc_steps))
 

src/axolotl/core/trainers/base.py

@@ -100,6 +100,27 @@ class AxolotlTrainer(
         self._signature_columns = None  # workaround for pylint
 
         super().__init__(*_args, **kwargs)
+
+        # Gemma4 (and similar multimodal models) declare **kwargs in forward() for
+        # extra inputs like mm_token_type_ids.  HF Trainer interprets VAR_KEYWORD as
+        # "the model handles num_items_in_batch internally" and skips the loss ÷
+        # gradient_accumulation_steps normalisation, which inflates the *logged* loss
+        # (the gradient itself is still correct). Override to False when the model
+        # doesn't actually consume num_items_in_batch.
+        if self.model_accepts_loss_kwargs:
+            model_to_check = self.accelerator.unwrap_model(self.model)
+            if hasattr(model_to_check, "base_model"):  # PEFT wrapper
+                model_to_check = model_to_check.base_model
+            if hasattr(model_to_check, "model"):
+                model_to_check = model_to_check.model
+            fwd = getattr(model_to_check, "forward", None)
+            if fwd is not None:
+                import inspect
+
+                params = inspect.signature(fwd).parameters
+                if "num_items_in_batch" not in params:
+                    self.model_accepts_loss_kwargs = False
+
         self.train_data_collator = self.data_collator
         self._stored_metrics = defaultdict(
             lambda: defaultdict(lambda: {"values": [], "reduction": "mean"})
@@ -383,13 +404,29 @@ class AxolotlTrainer(
 
         # Gemma4 requires mm_token_type_ids during training (even for text-only).
         # Inject zeros (= text token type) when not provided by the data collator.
+        # Use unwrap_model to handle DDP/FSDP wrappers that don't proxy .config.
+        _unwrapped = self.accelerator.unwrap_model(model)
+        _model_type = getattr(getattr(_unwrapped, "config", None), "model_type", None)
         if (
             "mm_token_type_ids" not in inputs
             and "input_ids" in inputs
-            and getattr(getattr(model, "config", None), "model_type", None) == "gemma4"
+            and _model_type == "gemma4"
         ):
             inputs["mm_token_type_ids"] = torch.zeros_like(inputs["input_ids"])
 
+        # Gemma4 (and Gemma3): transformers' masking_utils detects packed sequences
+        # from position_ids, but only when attention_mask is None.  When sample
+        # packing is active the collator provides an all-ones attention_mask that
+        # prevents this detection — remove it so the model builds the correct
+        # per-sequence causal masks.
+        if (
+            self.args.sample_packing
+            and _model_type in ("gemma4", "gemma3")
+            and "attention_mask" in inputs
+            and "position_ids" in inputs
+        ):
+            del inputs["attention_mask"]
+
         if self.args.orpo_alpha:
             return self.orpo_compute_loss(
                 model,
@@ -398,6 +435,23 @@ class AxolotlTrainer(
                 num_items_in_batch=num_items_in_batch,
             )
 
+        # Gemma4ForConditionalGeneration computes loss with a manual
+        # nn.CrossEntropyLoss() that bypasses proper num_items_in_batch
+        # normalization and does redundant attention_mask filtering.
+        # Compute loss externally using the standard loss_function instead.
+        if _model_type == "gemma4" and "labels" in inputs:
+            labels = inputs.pop("labels")
+            outputs = model(**inputs)
+            logits = outputs.logits
+            unwrapped = self.accelerator.unwrap_model(model)
+            vocab_size = unwrapped.config.get_text_config().vocab_size
+            loss = unwrapped.loss_function(
+                logits, labels, vocab_size, num_items_in_batch=num_items_in_batch
+            )
+            if return_outputs:
+                return loss, outputs
+            return loss
+
         return super().compute_loss(
             model,
             inputs,
@@ -410,6 +464,21 @@ class AxolotlTrainer(
         LOG.info("Running evaluation step...")
         return super().evaluate(*args, **kwargs)
 
+    @override
+    def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys=None):
+        # Gemma4 requires mm_token_type_ids even during evaluation.
+        _unwrapped = self.accelerator.unwrap_model(model)
+        _model_type = getattr(getattr(_unwrapped, "config", None), "model_type", None)
+        if (
+            "mm_token_type_ids" not in inputs
+            and "input_ids" in inputs
+            and _model_type == "gemma4"
+        ):
+            inputs["mm_token_type_ids"] = torch.zeros_like(inputs["input_ids"])
+        return super().prediction_step(
+            model, inputs, prediction_loss_only, ignore_keys=ignore_keys
+        )
+
     @staticmethod
     def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
         concatenated_batch = {}

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@fec1a88"
 ```
 
 ## Usage
@@ -44,6 +44,7 @@ plugins:
 - gemma3_text
 - gemma3n
 - gemma3n_text
+- gemma4
 - glm
 - glm4
 - glm4_moe

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 uninstall -y cut-cross-entropy && pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@fec1a88"`'
 )
 
 

src/axolotl/integrations/kernels/README.md

@@ -146,10 +146,6 @@ Gemma 4 (e.g. `google/gemma-4-26B-A4B`) has a unique hybrid MoE architecture:
 
 Because there is no SparseMoeBlock class to patch, Gemma 4 uses a different integration path: we register `"scattermoe"` as a custom implementation in the transformers `ExpertsInterface`, and set `experts_implementation: scattermoe` in the config. The `@use_experts_implementation` decorator on `Gemma4TextExperts` then dispatches to our ScatterMoE kernel automatically. The router is untouched — it runs as-is.
 
-**Important limitations:**
-- **Flash Attention 2 is not supported** — Gemma 4 uses `global_head_dim: 512` for full attention layers, which exceeds FA2's maximum head dimension of 256. Use `sdp_attention: true` instead.
-- **Multimodal model**: Gemma 4 includes vision and audio encoders. For text-only SFT, use `lora_target_linear_modules` with a regex to restrict LoRA to the text backbone (e.g. `language_model\.model\.layers\.\d+\.self_attn\.(q|k|v|o)_proj`).
-
 ## Limitations
 
 - **ScatterMoE + GLM4-MoE Lite**: ScatterMoE does not work reliably for GLM 4.7 Flash (`glm4_moe_lite`).

src/axolotl/integrations/kernels/args.py

@@ -53,28 +53,6 @@ class KernelsArgs(BaseModel):
 
         return data
 
-    @model_validator(mode="before")
-    @classmethod
-    def warn_sonicmoe_lora_overhead(cls, data):
-        if data.get("use_sonicmoe") is True and data.get("adapter") in (
-            "lora",
-            "qlora",
-        ):
-            lora_target = data.get("lora_target_modules") or []
-            lora_linear = data.get("lora_target_linear_modules") or []
-            targets = (
-                lora_target if isinstance(lora_target, list) else [lora_target]
-            ) + (lora_linear if isinstance(lora_linear, list) else [lora_linear])
-            expert_keywords = ("gate_up_proj", "down_proj", "experts")
-            if any(kw in t for t in targets for kw in expert_keywords):
-                LOG.info(
-                    "SonicMoE + LoRA on expert modules uses runtime weight materialization "
-                    "(W_eff = W + scaling*B@A per forward). This has slightly higher overhead "
-                    "than ScatterMoE's fused Triton LoRA kernels but works with any CUTLASS kernel."
-                )
-
-        return data
-
     @model_validator(mode="before")
     @classmethod
     def disable_mlp_kernel(cls, data):

src/axolotl/integrations/liger/plugin.py

@@ -222,6 +222,56 @@ class LigerPlugin(BasePlugin):
                 rms_norm=cfg.liger_rms_norm,
                 swiglu=cfg.liger_glu_activation,
             )
+        elif cfg.model_config_type in ("gemma4", "gemma4_text"):
+            # Gemma4: offset=0 (NOT 1 like Gemma3), in_place=False required for
+            # gradient checkpointing compatibility, RoPE incompatible (separate q/k).
+            from liger_kernel.transformers.geglu import LigerGEGLUMLP
+            from transformers.models.gemma4 import modeling_gemma4
+
+            if cfg.liger_rms_norm:
+                _OrigGemma4RMSNorm = modeling_gemma4.Gemma4RMSNorm
+
+                class _LigerGemma4RMSNorm(LigerRMSNorm):
+                    """LigerRMSNorm for Gemma4 with in_place=False and with_scale support."""
+
+                    def __new__(cls, dim, eps=1e-6, with_scale=True):
+                        if not with_scale:
+                            return _OrigGemma4RMSNorm(dim, eps, with_scale=False)
+                        return super().__new__(cls)
+
+                    def __init__(self, dim, eps=1e-6, with_scale=True):
+                        if not with_scale:
+                            return
+                        # offset=0.0 (standard), in_place=False (gradient checkpointing safe)
+                        super().__init__(
+                            dim, eps, offset=0.0, casting_mode="llama", in_place=False
+                        )
+
+                modeling_gemma4.Gemma4RMSNorm = _LigerGemma4RMSNorm
+            if cfg.liger_glu_activation:
+
+                class _LigerGemma4MLP(LigerGEGLUMLP):
+                    def __init__(self, config, layer_idx=None):
+                        super().__init__(config)
+
+                modeling_gemma4.Gemma4TextMLP = _LigerGemma4MLP
+            if cfg.liger_rope:
+                LOG.warning(
+                    "Liger RoPE is not compatible with Gemma4 (separate q/k application). Skipping."
+                )
+            if cfg.liger_layer_norm:
+                modeling_gemma4.nn.LayerNorm = LigerLayerNorm
+            if cfg.liger_cross_entropy:
+                modeling_gemma4.nn.CrossEntropyLoss = LigerCrossEntropyLoss
+            if cfg.liger_fused_linear_cross_entropy:
+                LOG.warning(
+                    "Liger fused linear cross entropy is not compatible with Gemma4. Skipping."
+                )
+            LOG.info(
+                f"Applied Liger kernels for gemma4: "
+                f"rms_norm={cfg.liger_rms_norm}, glu={cfg.liger_glu_activation}, "
+                f"rope=False (incompatible), layer_norm={cfg.liger_layer_norm}"
+            )
         elif cfg.liger_fused_linear_cross_entropy:
             try:
                 from .models.base import patch_lce_forward

src/axolotl/kernels/gemma4_fused_rope.py

@@ -0,0 +1,529 @@
+"""
+Fused RMSNorm + RoPE Triton kernel for Gemma 4.
+
+Fuses three operations into one kernel launch:
+  1. RMSNorm: x_norm = (x / sqrt(mean(x^2) + eps)) * weight
+  2. RoPE:    y = x_norm * cos + rotate_half(x_norm) * sin
+  3. (optional) RMSNorm without scale (for v_norm)
+
+This eliminates two intermediate tensor materializations per Q/K path;
+churn from rotate_half / apply_rotary_pos_emb.
+
+Shapes:
+  X:      (rows, head_dim)  — flattened from (batch, seq_len, num_heads, head_dim)
+  W:      (head_dim,)       — RMSNorm weight (None for with_scale=False)
+  cos:    (rows, head_dim)  — flattened from (batch, seq_len, 1, head_dim) after broadcast
+  sin:    (rows, head_dim)  — same as cos
+"""
+
+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_rope_forward_kernel(
+    Y_ptr,
+    Y_row_stride,
+    X_ptr,
+    X_row_stride,
+    W_ptr,
+    COS_ptr,
+    COS_row_stride,
+    SIN_ptr,
+    SIN_row_stride,
+    RSTD_ptr,
+    RSTD_row_stride,
+    n_cols,
+    n_heads,
+    eps,
+    HAS_WEIGHT: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    """
+    Fused forward:
+      x_norm = x / rms(x) [* weight]   (RMSNorm)
+      y = x_norm * cos + rotate_half(x_norm) * sin  (RoPE)
+
+    rotate_half swaps first/second halves and negates the first:
+      rotate_half([a, b]) = [-b, a]
+
+    cos/sin are indexed by row_idx // n_heads to handle per-head broadcast
+    (cos/sin have shape (B*S, D) while X has shape (B*S*H, D)).
+    """
+    row_idx = tl.program_id(0).to(tl.int64)
+    # cos/sin row: divide by n_heads since cos/sin are (B*S, D)
+    cs_row_idx = row_idx // n_heads
+    col_offsets = tl.arange(0, BLOCK_SIZE)
+    mask = col_offsets < n_cols
+    half_dim = n_cols // 2
+
+    # Load input row
+    X_row = tl.load(X_ptr + row_idx * X_row_stride + col_offsets, mask=mask, other=0)
+    X_dtype = X_row.dtype
+    X_fp32 = X_row.to(tl.float32)
+
+    # RMSNorm: compute 1/rms
+    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)
+
+    # Normalize
+    X_norm = X_fp32 * rstd
+
+    # Apply weight if present (with_scale=True)
+    if HAS_WEIGHT:
+        W_row = tl.load(W_ptr + col_offsets, mask=mask, other=0).to(tl.float32)
+        X_norm = X_norm * W_row
+
+    # RoPE: load cos/sin (broadcast across heads)
+    cos_row = tl.load(
+        COS_ptr + cs_row_idx * COS_row_stride + col_offsets, mask=mask, other=0
+    ).to(tl.float32)
+    sin_row = tl.load(
+        SIN_ptr + cs_row_idx * SIN_row_stride + col_offsets, mask=mask, other=0
+    ).to(tl.float32)
+
+    # rotate_half: for col < half_dim, take -X_norm[col + half_dim]
+    #              for col >= half_dim, take  X_norm[col - half_dim]
+    rot_offsets = tl.where(
+        col_offsets < half_dim, col_offsets + half_dim, col_offsets - half_dim
+    )
+    rot_mask = rot_offsets < n_cols
+    X_rot = tl.load(
+        X_ptr + row_idx * X_row_stride + rot_offsets, mask=rot_mask & mask, other=0
+    ).to(tl.float32)
+    # Re-normalize the rotated values
+    X_rot_norm = X_rot * rstd
+    if HAS_WEIGHT:
+        W_rot = tl.load(W_ptr + rot_offsets, mask=rot_mask & mask, other=0).to(
+            tl.float32
+        )
+        X_rot_norm = X_rot_norm * W_rot
+
+    # Negate the first half (rotate_half negates x2, which becomes the first half)
+    sign = tl.where(col_offsets < half_dim, -1.0, 1.0)
+    X_rot_norm = X_rot_norm * sign
+
+    # Final RoPE: y = x_norm * cos + rotate_half(x_norm) * sin
+    Y_row = X_norm * cos_row + X_rot_norm * sin_row
+
+    tl.store(
+        Y_ptr + row_idx * Y_row_stride + col_offsets,
+        Y_row.to(X_dtype),
+        mask=mask,
+    )
+
+
+@triton.jit
+def _rms_norm_rope_backward_kernel(
+    dY_ptr,
+    dY_row_stride,
+    dX_ptr,
+    dX_row_stride,
+    X_ptr,
+    X_row_stride,
+    X_dtype: tl.constexpr,
+    W_ptr,
+    COS_ptr,
+    COS_row_stride,
+    SIN_ptr,
+    SIN_row_stride,
+    RSTD_ptr,
+    RSTD_row_stride,
+    dW_ptr,
+    dW_row_stride,
+    n_rows,
+    n_cols,
+    n_heads,
+    rows_per_program,
+    HAS_WEIGHT: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    """
+    Backward for Y = RoPE(RMSNorm(X, W))
+    cos/sin indexed by row_idx // n_heads for per-head broadcast.
+    """
+    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
+    half_dim = n_cols // 2
+
+    dW_acc = tl.zeros((BLOCK_SIZE,), dtype=tl.float32)
+
+    if HAS_WEIGHT:
+        W_row = tl.load(W_ptr + col_offsets, mask=mask, other=0).to(tl.float32)
+
+    for row_idx in range(row_start, row_end):
+        cs_row_idx = row_idx // n_heads
+
+        dY_row = tl.load(
+            dY_ptr + row_idx * dY_row_stride + col_offsets, mask=mask, other=0
+        ).to(tl.float32)
+        X_row = tl.load(
+            X_ptr + row_idx * X_row_stride + col_offsets, mask=mask, other=0
+        ).to(tl.float32)
+        rstd = tl.load(RSTD_ptr + row_idx * RSTD_row_stride)
+
+        cos_row = tl.load(
+            COS_ptr + cs_row_idx * COS_row_stride + col_offsets, mask=mask, other=0
+        ).to(tl.float32)
+
+        # dN = dY * cos + rotate_half^T(dY * sin)
+        # rotate_half^T([a, b]) = [b, -a]  (adjoint of rotate_half)
+        #
+        # Compute rotate_half_transpose(dY * sin) by loading dY and sin at
+        # rotated offsets directly:  dY[rot] * sin[rot] * adj_sign
+        # This is equivalent to rotating (dY * sin) because the rotation
+        # just permutes which elements are multiplied.
+        rot_offsets = tl.where(
+            col_offsets < half_dim, col_offsets + half_dim, col_offsets - half_dim
+        )
+        rot_mask = rot_offsets < n_cols
+        dY_rot = tl.load(
+            dY_ptr + row_idx * dY_row_stride + rot_offsets,
+            mask=rot_mask & mask,
+            other=0,
+        ).to(tl.float32)
+        sin_rot = tl.load(
+            SIN_ptr + cs_row_idx * SIN_row_stride + rot_offsets,
+            mask=rot_mask & mask,
+            other=0,
+        ).to(tl.float32)
+
+        adj_sign = tl.where(col_offsets < half_dim, 1.0, -1.0)
+        dN = dY_row * cos_row + dY_rot * sin_rot * adj_sign
+
+        # Pre-weight normalized: n = rstd * x
+        n = X_row * rstd
+
+        if HAS_WEIGHT:
+            dW_acc += dN * n
+            dm = dN * W_row
+        else:
+            dm = dN
+
+        # RMSNorm backward: dX = rstd * (dm - (1/n_cols) * rstd^2 * dot(dm, X) * X)
+        dot_dm_x = tl.sum(dm * X_row, axis=0)
+        dX_row = rstd * (dm - (1.0 / n_cols) * rstd * rstd * dot_dm_x * X_row)
+
+        tl.store(
+            dX_ptr + row_idx * dX_row_stride + col_offsets,
+            dX_row.to(X_dtype),
+            mask=mask,
+        )
+
+    if HAS_WEIGHT:
+        tl.store(
+            dW_ptr + row_block_id * dW_row_stride + col_offsets,
+            dW_acc,
+            mask=mask,
+        )
+
+
+def rms_norm_rope_forward(X, W, cos, sin, eps, n_heads):
+    """
+    Args:
+        X:   (B*S*H, head_dim) — contiguous, flattened from (B, S, H, D)
+        W:   (head_dim,) or None — RMSNorm weight
+        cos: (B*S, head_dim) — position embeddings (broadcast across heads)
+        sin: (B*S, head_dim) — position embeddings (broadcast across heads)
+        eps: float
+        n_heads: int — number of attention heads (for cos/sin indexing)
+    Returns:
+        Y, X_saved, RSTD, BLOCK_SIZE, num_warps
+    """
+    n_rows, n_cols = X.shape
+    BLOCK_SIZE, num_warps = calculate_settings(n_cols)
+    has_weight = W is not None
+
+    Y = torch.empty_like(X)
+    RSTD = torch.empty(n_rows, dtype=torch.float32, device=X.device)
+
+    _rms_norm_rope_forward_kernel[(n_rows,)](
+        Y,
+        Y.stride(0),
+        X,
+        X.stride(0),
+        W if has_weight else X,  # dummy pointer when no weight
+        cos,
+        cos.stride(0),
+        sin,
+        sin.stride(0),
+        RSTD,
+        RSTD.stride(0),
+        n_cols,
+        n_heads,
+        eps,
+        HAS_WEIGHT=has_weight,
+        BLOCK_SIZE=BLOCK_SIZE,
+        num_warps=num_warps,
+    )
+    return Y, X, RSTD, BLOCK_SIZE, num_warps
+
+
+def rms_norm_rope_backward(dY, X, W, cos, sin, RSTD, n_heads, BLOCK_SIZE, num_warps):
+    n_rows, n_cols = dY.shape
+    has_weight = W is not None
+
+    sm_count = torch.cuda.get_device_properties(X.device).multi_processor_count
+    rows_per_program = math.ceil(n_rows / sm_count)
+
+    dX = torch.empty_like(X)
+
+    if has_weight:
+        _dW = torch.empty((sm_count, n_cols), dtype=torch.float32, device=X.device)
+    else:
+        _dW = torch.empty((1, n_cols), dtype=torch.float32, device=X.device)
+
+    _rms_norm_rope_backward_kernel[(sm_count,)](
+        dY,
+        dY.stride(0),
+        dX,
+        dX.stride(0),
+        X,
+        X.stride(0),
+        torch_to_triton_dtype[X.dtype],
+        W if has_weight else X,  # dummy
+        cos,
+        cos.stride(0),
+        sin,
+        sin.stride(0),
+        RSTD,
+        RSTD.stride(0),
+        _dW,
+        _dW.stride(0),
+        n_rows,
+        n_cols,
+        n_heads,
+        rows_per_program,
+        HAS_WEIGHT=has_weight,
+        BLOCK_SIZE=BLOCK_SIZE,
+        num_warps=num_warps,
+    )
+
+    dW = _dW.sum(dim=0).to(W.dtype) if has_weight else None
+    return dX, dW
+
+
+class FusedRMSNormRoPEFunction(torch.autograd.Function):
+    @staticmethod
+    @ensure_contiguous
+    def forward(ctx, X, W, cos, sin, eps, n_heads):
+        """
+        X:   (B*S*H, head_dim)
+        W:   (head_dim,) or None
+        cos: (B*S, head_dim) — broadcast across heads
+        sin: (B*S, head_dim) — broadcast across heads
+        n_heads: int
+        """
+        Y, X_saved, RSTD, BLOCK_SIZE, num_warps = rms_norm_rope_forward(
+            X,
+            W,
+            cos,
+            sin,
+            eps,
+            n_heads,
+        )
+        ctx.eps = eps
+        ctx.BLOCK_SIZE = BLOCK_SIZE
+        ctx.num_warps = num_warps
+        ctx.n_heads = n_heads
+        ctx.has_weight = W is not None
+        ctx.save_for_backward(X_saved, W, cos, sin, RSTD)
+        return Y
+
+    @staticmethod
+    @ensure_contiguous
+    def backward(ctx, dY):
+        X, W, cos, sin, RSTD = ctx.saved_tensors
+        dX, dW = rms_norm_rope_backward(
+            dY,
+            X,
+            W,
+            cos,
+            sin,
+            RSTD,
+            ctx.n_heads,
+            ctx.BLOCK_SIZE,
+            ctx.num_warps,
+        )
+        return dX, dW, None, None, None, None
+
+
+def fused_rms_norm_rope(x, weight, cos, sin, eps=1e-6):
+    """
+    Apply fused RMSNorm + RoPE.
+
+    Args:
+        x:      (batch, seq_len, num_heads, head_dim) — after projection + view
+        weight: (head_dim,) — RMSNorm weight, or None for no-scale norm
+        cos:    (batch, seq_len, head_dim) — from RotaryEmbedding
+        sin:    (batch, seq_len, head_dim) — from RotaryEmbedding
+        eps:    float — RMSNorm epsilon
+
+    Returns:
+        y: (batch, seq_len, num_heads, head_dim) — normalized + rotated
+    """
+    shape = x.shape  # (B, S, H, D)
+    B, S, H, D = shape
+    # Flatten to 2D: (B*S*H, D)
+    x_flat = x.reshape(-1, D).contiguous()
+    # Flatten cos/sin to (B*S, D) — the kernel will handle per-head broadcast
+    # by dividing the row_idx by H to get the cos/sin row
+    cos_flat = cos.reshape(B * S, D).contiguous()
+    sin_flat = sin.reshape(B * S, D).contiguous()
+
+    y_flat = FusedRMSNormRoPEFunction.apply(x_flat, weight, cos_flat, sin_flat, eps, H)
+    return y_flat.view(shape)
+
+
+@triton.jit
+def _rms_norm_forward_kernel(
+    Y_ptr,
+    Y_row_stride,
+    X_ptr,
+    X_row_stride,
+    RSTD_ptr,
+    RSTD_row_stride,
+    n_cols,
+    eps,
+    BLOCK_SIZE: tl.constexpr,
+):
+    """RMSNorm without scale weight: y = x / rms(x)"""
+    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)
+    X_dtype = X_row.dtype
+    X_fp32 = X_row.to(tl.float32)
+
+    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)
+
+    Y_row = X_fp32 * rstd
+    tl.store(Y_ptr + row_idx * Y_row_stride + col_offsets, Y_row.to(X_dtype), mask=mask)
+
+
+@triton.jit
+def _rms_norm_noscale_backward_kernel(
+    dY_ptr,
+    dY_row_stride,
+    dX_ptr,
+    dX_row_stride,
+    X_ptr,
+    X_row_stride,
+    X_dtype: tl.constexpr,
+    RSTD_ptr,
+    RSTD_row_stride,
+    n_cols,
+    BLOCK_SIZE: tl.constexpr,
+):
+    """Backward for y = x * rstd (no weight)."""
+    row_idx = tl.program_id(0).to(tl.int64)
+    col_offsets = tl.arange(0, BLOCK_SIZE)
+    mask = col_offsets < n_cols
+
+    dY_row = tl.load(
+        dY_ptr + row_idx * dY_row_stride + col_offsets, mask=mask, other=0
+    ).to(tl.float32)
+    X_row = tl.load(
+        X_ptr + row_idx * X_row_stride + col_offsets, mask=mask, other=0
+    ).to(tl.float32)
+    rstd = tl.load(RSTD_ptr + row_idx * RSTD_row_stride)
+
+    dot_dy_x = tl.sum(dY_row * X_row, axis=0)
+    dX_row = rstd * (dY_row - (1.0 / n_cols) * rstd * rstd * dot_dy_x * X_row)
+
+    tl.store(
+        dX_ptr + row_idx * dX_row_stride + col_offsets, dX_row.to(X_dtype), mask=mask
+    )
+
+
+class FusedRMSNormNoScaleFunction(torch.autograd.Function):
+    """RMSNorm without learnable scale — used for Gemma4's v_norm."""
+
+    @staticmethod
+    @ensure_contiguous
+    def forward(ctx, X, eps):
+        n_rows, n_cols = X.shape
+        BLOCK_SIZE, num_warps = calculate_settings(n_cols)
+        Y = torch.empty_like(X)
+        RSTD = torch.empty(n_rows, dtype=torch.float32, device=X.device)
+
+        _rms_norm_forward_kernel[(n_rows,)](
+            Y,
+            Y.stride(0),
+            X,
+            X.stride(0),
+            RSTD,
+            RSTD.stride(0),
+            n_cols,
+            eps,
+            BLOCK_SIZE=BLOCK_SIZE,
+            num_warps=num_warps,
+        )
+        ctx.BLOCK_SIZE = BLOCK_SIZE
+        ctx.num_warps = num_warps
+        ctx.save_for_backward(X, RSTD)
+        ctx.n_cols = n_cols
+        return Y
+
+    @staticmethod
+    @ensure_contiguous
+    def backward(ctx, dY):
+        X, RSTD = ctx.saved_tensors
+        n_rows = X.shape[0]
+        dX = torch.empty_like(X)
+        _rms_norm_noscale_backward_kernel[(n_rows,)](
+            dY,
+            dY.stride(0),
+            dX,
+            dX.stride(0),
+            X,
+            X.stride(0),
+            torch_to_triton_dtype[X.dtype],
+            RSTD,
+            RSTD.stride(0),
+            ctx.n_cols,
+            BLOCK_SIZE=ctx.BLOCK_SIZE,
+            num_warps=ctx.num_warps,
+        )
+        return dX, None
+
+
+def fused_rms_norm_noscale(x, eps=1e-6):
+    """
+    RMSNorm without scale for v_norm.
+
+    Args:
+        x: (batch, seq_len, num_heads, head_dim)
+    Returns:
+        y: same shape, normalized
+    """
+    shape = x.shape
+    x_flat = x.reshape(-1, shape[-1])
+    y_flat = FusedRMSNormNoScaleFunction.apply(x_flat, eps)
+    return y_flat.view(shape)

src/axolotl/kernels/lora.py

@@ -1297,6 +1297,339 @@ def apply_lora_qkv(
     return Q, K, V
 
 
+class LoRA_QK(torch.autograd.Function):
+    """Optimized LoRA QK implementation for models where v_proj is None.
+
+    Used by models like Gemma4 with attention_k_eq_v=True, where key states are
+    reused as value states.  Only Q and K projections are fused; the caller
+    returns K a second time as V so that autograd accumulates key+value gradients
+    into a single dK.
+
+    Supports bias, dropout, and DoRA (Weight-Decomposed Low-Rank Adaptation).
+    """
+
+    @staticmethod
+    @torch_amp_custom_fwd
+    def forward(
+        ctx: torch.autograd.function.FunctionCtx,
+        X: torch.Tensor,
+        X_drop: torch.Tensor | None,
+        # Q params
+        q_weight: torch.Tensor,
+        q_bias: torch.Tensor | None,
+        q_quant: QuantState | None,
+        q_A: torch.Tensor | None,
+        q_B: torch.Tensor | None,
+        q_scale: float,
+        q_lora_bias: torch.Tensor | None,
+        q_magnitude: torch.Tensor | None,
+        # K params
+        k_weight: torch.Tensor,
+        k_bias: torch.Tensor | None,
+        k_quant: QuantState | None,
+        k_A: torch.Tensor | None,
+        k_B: torch.Tensor | None,
+        k_scale: float,
+        k_lora_bias: torch.Tensor | None,
+        k_magnitude: torch.Tensor | None,
+        # Flags
+        inplace: bool = True,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        has_dropout = X_drop is not None
+        has_dora = q_magnitude is not None
+
+        if has_dora:
+            dtype = X.dtype
+            X_lora = X_drop if has_dropout else X
+
+            # Compute Q with DoRA
+            Q_base = matmul_lora(X, q_weight, None, q_quant, None, None, None)
+            Q_lora = _lora_only(X_lora, q_A, q_B, q_scale, q_lora_bias, dtype)
+            q_mag_scale = _compute_dora_scale(
+                q_weight, q_quant, q_A, q_B, q_scale, q_magnitude, dtype
+            )
+            Q = q_mag_scale.unsqueeze(0) * (Q_base + Q_lora)
+            if q_bias is not None:
+                Q = Q + q_bias
+
+            # Compute K with DoRA
+            K_base = matmul_lora(X, k_weight, None, k_quant, None, None, None)
+            K_lora = _lora_only(X_lora, k_A, k_B, k_scale, k_lora_bias, dtype)
+            k_mag_scale = _compute_dora_scale(
+                k_weight, k_quant, k_A, k_B, k_scale, k_magnitude, dtype
+            )
+            K = k_mag_scale.unsqueeze(0) * (K_base + K_lora)
+            if k_bias is not None:
+                K = K + k_bias
+
+            Q_combined = Q_base + Q_lora
+            K_combined = K_base + K_lora
+
+            ctx.save_for_backward(
+                X,
+                X_drop if has_dropout else X,
+                q_A.to(dtype) if q_A is not None else q_A,
+                q_B.to(dtype) if q_B is not None else q_B,
+                k_A.to(dtype) if k_A is not None else k_A,
+                k_B.to(dtype) if k_B is not None else k_B,
+                q_magnitude,
+                k_magnitude,
+                q_mag_scale,
+                k_mag_scale,
+                Q_combined,
+                K_combined,
+                q_lora_bias,
+                k_lora_bias,
+            )
+        else:
+            # Standard LoRA (with optional dropout and bias)
+            Q = matmul_lora(
+                X,
+                q_weight,
+                q_bias,
+                q_quant,
+                q_A,
+                q_B,
+                q_scale,
+                X_drop=X_drop,
+                lora_bias=q_lora_bias,
+            )
+            K = matmul_lora(
+                X,
+                k_weight,
+                k_bias,
+                k_quant,
+                k_A,
+                k_B,
+                k_scale,
+                X_drop=X_drop,
+                lora_bias=k_lora_bias,
+            )
+
+            dtype = X.dtype
+            ctx.save_for_backward(
+                X,
+                X_drop if has_dropout else X,
+                q_A.to(dtype) if q_A is not None else q_A,
+                q_B.to(dtype) if q_B is not None else q_B,
+                k_A.to(dtype) if k_A is not None else k_A,
+                k_B.to(dtype) if k_B is not None else k_B,
+                q_lora_bias,
+                k_lora_bias,
+            )
+
+        ctx.scales = (q_scale, k_scale)
+        ctx.quants = (q_quant, k_quant)
+        ctx.weights = (q_weight, k_weight)
+        ctx.inplace = inplace
+        ctx.has_dropout = has_dropout
+        ctx.has_dora = has_dora
+
+        return Q, K
+
+    @staticmethod
+    @torch_amp_custom_bwd
+    def backward(
+        ctx: torch.autograd.function.FunctionCtx,
+        q_grad: torch.Tensor,
+        k_grad: torch.Tensor,
+    ):
+        q_weight, k_weight = ctx.weights
+        q_quant, k_quant = ctx.quants
+        q_scale, k_scale = ctx.scales
+        has_dropout = ctx.has_dropout
+        has_dora = ctx.has_dora
+
+        if has_dora:
+            (
+                X,
+                X_lora,
+                A_q,
+                B_q,
+                A_k,
+                B_k,
+                q_magnitude,
+                k_magnitude,
+                q_mag_scale,
+                k_mag_scale,
+                Q_combined,
+                K_combined,
+                q_lora_bias,
+                k_lora_bias,
+            ) = ctx.saved_tensors
+        else:
+            (
+                X,
+                X_lora,
+                A_q,
+                B_q,
+                A_k,
+                B_k,
+                q_lora_bias,
+                k_lora_bias,
+            ) = ctx.saved_tensors
+            q_magnitude = k_magnitude = None
+            q_mag_scale = k_mag_scale = None
+            Q_combined = K_combined = None
+
+        batch, seq_len = X.shape[:2]
+        q_grad = q_grad.view(-1, q_grad.shape[-1])
+        k_grad = k_grad.reshape(-1, k_grad.shape[-1])
+        X = X.view(-1, X.shape[-1])
+        X_lora = X_lora.view(-1, X_lora.shape[-1])
+
+        d_q_mag = d_k_mag = None
+        d_q_lora_bias = d_k_lora_bias = None
+
+        if has_dora:
+            Q_combined = Q_combined.view(-1, Q_combined.shape[-1])
+            K_combined = K_combined.view(-1, K_combined.shape[-1])
+
+            d_q_mag = (q_grad * Q_combined).sum(dim=0) * q_mag_scale / q_magnitude
+            d_k_mag = (k_grad * K_combined).sum(dim=0) * k_mag_scale / k_magnitude
+
+            q_grad = q_grad * q_mag_scale.unsqueeze(0)
+            k_grad = k_grad * k_mag_scale.unsqueeze(0)
+
+        # LoRA bias gradients
+        if q_lora_bias is not None:
+            d_q_lora_bias = q_scale * q_grad.sum(dim=0)
+        if k_lora_bias is not None:
+            d_k_lora_bias = k_scale * k_grad.sum(dim=0)
+
+        X_lora_t = X_lora.t()
+
+        d_A_q = d_B_q = d_A_k = d_B_k = None
+        grad_B_q = grad_B_k = None
+
+        if A_q is not None and B_q is not None:
+            grad_B_q = q_grad @ B_q
+            d_A_q = torch.empty_like(A_q.t())
+            d_B_q = torch.empty_like(B_q.t())
+            d_A_q.addmm_(X_lora_t, grad_B_q, alpha=q_scale, beta=0)
+            d_B_q.addmm_(A_q @ X_lora_t, q_grad, alpha=q_scale, beta=0)
+
+        if A_k is not None and B_k is not None:
+            grad_B_k = k_grad @ B_k
+            d_A_k = torch.empty_like(A_k.t())
+            d_B_k = torch.empty_like(B_k.t())
+            d_A_k.addmm_(X_lora_t, grad_B_k, alpha=k_scale, beta=0)
+            d_B_k.addmm_(A_k @ X_lora_t, k_grad, alpha=k_scale, beta=0)
+
+        # Base path input gradient
+        out_buffer = X if ctx.inplace else None
+
+        q_weight_t = dequantize(q_weight, q_quant)
+        grad_X = torch.mm(q_grad, q_weight_t, out=out_buffer)
+        del q_weight_t
+
+        k_weight_t = dequantize(k_weight, k_quant)
+        grad_X.addmm_(k_grad, k_weight_t)
+        del k_weight_t
+
+        # LoRA path input gradient
+        if has_dropout:
+            grad_X_drop = torch.zeros_like(X_lora)
+            if grad_B_q is not None:
+                grad_X_drop.addmm_(grad_B_q, A_q, alpha=q_scale)
+            if grad_B_k is not None:
+                grad_X_drop.addmm_(grad_B_k, A_k, alpha=k_scale)
+        else:
+            grad_X_drop = None
+            if grad_B_q is not None:
+                grad_X.addmm_(grad_B_q, A_q, alpha=q_scale)
+            if grad_B_k is not None:
+                grad_X.addmm_(grad_B_k, A_k, alpha=k_scale)
+
+        if d_A_q is not None:
+            d_A_q = d_A_q.t()
+            d_B_q = d_B_q.t()  # type: ignore[union-attr]
+        if d_A_k is not None:
+            d_A_k = d_A_k.t()
+            d_B_k = d_B_k.t()  # type: ignore[union-attr]
+
+        grad_X = grad_X.view(batch, seq_len, -1)
+        if grad_X_drop is not None:
+            grad_X_drop = grad_X_drop.view(batch, seq_len, -1)
+
+        # Return gradients for all forward inputs:
+        # X, X_drop,
+        # q: weight, bias, quant, A, B, scale, lora_bias, magnitude
+        # k: weight, bias, quant, A, B, scale, lora_bias, magnitude
+        # inplace
+        return (
+            grad_X,
+            grad_X_drop,
+            # Q
+            None,
+            None,
+            None,
+            d_A_q,
+            d_B_q,
+            None,
+            d_q_lora_bias,
+            d_q_mag,
+            # K
+            None,
+            None,
+            None,
+            d_A_k,
+            d_B_k,
+            None,
+            d_k_lora_bias,
+            d_k_mag,
+            # inplace
+            None,
+        )
+
+
+def apply_lora_qk(
+    self, X: torch.Tensor, inplace: bool = True
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    Applies LoRA to compute Query and Key projections for models where v_proj is None.
+
+    When v_proj is None (e.g. Gemma4 attention_k_eq_v), key states are reused as
+    value states.  Returns (Q, K, K) — the caller's patched forward will use K as V.
+    Because K is returned twice, autograd accumulates gradients from both the key and
+    value paths into dK before calling LoRA_QK.backward.
+
+    Supports bias, dropout, and DoRA.
+    """
+    QW, Qb, QW_quant, QA, QB, QS, Qlb, Qdrop, Qmag = get_lora_parameters(self.q_proj)
+    KW, Kb, KW_quant, KA, KB, KS, Klb, Kdrop, Kmag = get_lora_parameters(self.k_proj)
+
+    # Apply dropout outside autograd.Function (shared mask for Q, K)
+    X_drop = _apply_dropout(Qdrop, X, self.training)
+
+    Q, K = LoRA_QK.apply(
+        X,
+        X_drop,
+        # Q
+        QW,
+        Qb,
+        QW_quant,
+        QA,
+        QB,
+        QS,
+        Qlb,
+        Qmag,
+        # K
+        KW,
+        Kb,
+        KW_quant,
+        KA,
+        KB,
+        KS,
+        Klb,
+        Kmag,
+        # Flags
+        inplace,
+    )
+
+    return Q, K, K
+
+
 class LoRA_O(torch.autograd.Function):
     """Optimized LoRA implementation for output projection.
 

src/axolotl/loaders/adapter.py

@@ -67,12 +67,165 @@ def find_all_linear_names(model):
     return list(lora_module_names)
 
 
+def _patch_peft_clippable_linear():
+    """Patch PEFT to handle Gemma4ClippableLinear which wraps nn.Linear.
+
+    Gemma4's vision tower uses ClippableLinear (a thin wrapper around nn.Linear
+    that clips activations). PEFT doesn't recognise it as a supported layer type,
+    so we redirect LoRA injection to the inner ``.linear`` child instead.
+    """
+    try:
+        from transformers.models.gemma4.modeling_gemma4 import (
+            Gemma4ClippableLinear as _cls,
+        )
+    except ImportError:
+        return
+
+    from peft.tuners.lora.model import LoraModel
+
+    if getattr(LoraModel, "_axolotl_clippable_patched", False):
+        return
+    _orig = LoraModel._create_and_replace
+
+    def _patched(
+        self,
+        peft_config,
+        adapter_name,
+        target,
+        target_name,
+        parent,
+        current_key=None,
+        **kw,
+    ):
+        if isinstance(target, _cls):
+            # Redirect to the inner nn.Linear so PEFT can wrap it normally.
+            return _orig(
+                self,
+                peft_config,
+                adapter_name,
+                target.linear,
+                "linear",
+                target,
+                current_key=current_key,
+                **kw,
+            )
+        return _orig(
+            self,
+            peft_config,
+            adapter_name,
+            target,
+            target_name,
+            parent,
+            current_key=current_key,
+            **kw,
+        )
+
+    LoraModel._create_and_replace = _patched
+    LoraModel._axolotl_clippable_patched = True
+
+
+def _peft_will_auto_convert_target_params(model, lora_config) -> bool:
+    """Check whether PEFT will auto-populate target_parameters for this model.
+
+    PEFT 0.19's ``convert_peft_config_for_transformers`` rewrites old MoE
+    ``target_modules`` (e.g. ``w1``/``w2``/``w3`` on Mixtral) into
+    ``target_parameters`` (``gate_up_proj``/``down_proj``) because
+    transformers v5 fused those expert linears into 3D ``nn.Parameter``
+    tensors. PEFT wraps the resulting 3D params with ``ParamWrapper``,
+    which rejects ``lora_dropout != 0``. This probe runs the conversion on
+    a copy of the config so we can detect the situation before
+    ``get_peft_model`` blows up.
+    """
+    if getattr(lora_config, "target_parameters", None):
+        return False
+
+    try:
+        from peft.utils.transformers_weight_conversion import (
+            convert_peft_config_for_transformers,
+            get_model_conversion_mapping,
+        )
+    except ImportError:
+        return False
+
+    import copy
+
+    probe_cfg = copy.deepcopy(lora_config)
+    try:
+        convert_peft_config_for_transformers(
+            probe_cfg,
+            model=model,
+            conversions=get_model_conversion_mapping(model),
+        )
+    except Exception:  # pylint: disable=broad-except
+        return False
+
+    return bool(getattr(probe_cfg, "target_parameters", None))
+
+
+def _patch_peft_param_wrapper_dropout():
+    """Let PEFT's ``ParamWrapper`` silently accept ``lora_dropout != 0``.
+
+    ``ParamWrapper`` wraps 3D expert ``nn.Parameter`` tensors and rejects
+    non-zero dropout because dropout can't be factored out of
+    ``lora_B(lora_A(dropout(x)))`` when the inner op is an expert-indexed
+    matmul. For mixed configs (attention + MoE experts) this is too
+    aggressive — the non-expert ``Linear`` LoRA layers *can* apply dropout
+    and that's usually what the user intended. We pass a copy of the
+    ``LoraConfig`` with ``lora_dropout=0`` only to ``ParamWrapper.__init__``
+    so it builds with ``nn.Identity`` for its internal dropout slot while
+    every other layer type still receives the real dropout value.
+    """
+    from peft.tuners.lora.layer import ParamWrapper
+
+    if getattr(ParamWrapper, "_axolotl_dropout_patched", False):
+        return
+
+    _orig_init = ParamWrapper.__init__
+
+    def _patched_init(
+        self,
+        base_layer,
+        adapter_name,
+        parameter_name,
+        config,
+        *args,
+        **kwargs,
+    ):
+        if getattr(config, "lora_dropout", 0):
+            import copy as _copy
+
+            patched_config = _copy.copy(config)
+            patched_config.lora_dropout = 0.0
+            return _orig_init(
+                self,
+                base_layer,
+                adapter_name,
+                parameter_name,
+                patched_config,
+                *args,
+                **kwargs,
+            )
+        return _orig_init(
+            self,
+            base_layer,
+            adapter_name,
+            parameter_name,
+            config,
+            *args,
+            **kwargs,
+        )
+
+    ParamWrapper.__init__ = _patched_init
+    ParamWrapper._axolotl_dropout_patched = True
+
+
 def load_lora(
     model: PreTrainedModel,
     cfg: DictDefault,
     inference: bool = False,
     config_only: bool = False,
 ) -> tuple[PreTrainedModel | PeftModel | PeftMixedModel | None, PeftConfig | None]:
+    _patch_peft_clippable_linear()
     lora_target_modules = cfg.lora_target_modules or []
     lora_target_parameters = cfg.lora_target_parameters or []
 
@@ -124,6 +277,7 @@ def load_lora(
         lora_dropout=cfg.lora_dropout,
         fan_in_fan_out=cfg.lora_fan_in_fan_out,
         modules_to_save=cfg.lora_modules_to_save if cfg.lora_modules_to_save else None,
+        exclude_modules=getattr(cfg, "lora_exclude_modules", None) or None,
         bias="none",
         task_type=task_type,
         **lora_config_kwargs,
@@ -132,6 +286,20 @@ def load_lora(
     if config_only:
         return None, lora_config
 
+    if getattr(
+        lora_config, "lora_dropout", 0
+    ) and _peft_will_auto_convert_target_params(model, lora_config):
+        LOG.warning(
+            "lora_dropout=%s requested but PEFT will wrap this model's fused "
+            "MoE expert parameters with ParamWrapper, which cannot apply "
+            "dropout (the 3D einsum can't factor dropout out of "
+            "lora_B(lora_A(dropout(x)))). Dropout will still be applied to "
+            "non-expert LoRA layers (e.g. attention), and expert LoRA layers "
+            "will use nn.Identity for the dropout slot.",
+            lora_config.lora_dropout,
+        )
+        _patch_peft_param_wrapper_dropout()
+
     rank = int(os.environ.get("LOCAL_RANK", 0))
 
     if (

src/axolotl/loaders/model.py

@@ -547,6 +547,16 @@ class ModelLoader:
                 mxfp4_kwargs = self.cfg.model_quantization_config_kwargs
             self.model_kwargs["quantization_config"] = Mxfp4Config(**mxfp4_kwargs)
 
+        if self.cfg.model_quantization_config == "FineGrainedFP8Config":
+            from transformers import FineGrainedFP8Config
+
+            fp8_kwargs = {}
+            if self.cfg.model_quantization_config_kwargs:
+                fp8_kwargs = self.cfg.model_quantization_config_kwargs
+            self.model_kwargs["quantization_config"] = FineGrainedFP8Config(
+                **fp8_kwargs
+            )
+
         if self.cfg.gptq:
             if not hasattr(self.model_config, "quantization_config"):
                 LOG.warning(
@@ -624,7 +634,14 @@ class ModelLoader:
 
     def _set_attention_config(self):
         """Sample packing uses custom FA2 patch"""
-        if self.cfg.attn_implementation:
+        if self.cfg.gemma4_hybrid_attn_impl:
+            # Load model with flash_attention_2 for sliding window layers;
+            # global layers will be patched to sdpa post-load.
+            self.model_kwargs["attn_implementation"] = "flash_attention_2"
+            self.model_config._attn_implementation = "flash_attention_2"
+            # Set flash_attention so multipack/sample_packing patches activate
+            self.cfg.flash_attention = True
+        elif self.cfg.attn_implementation:
             self.model_kwargs["attn_implementation"] = self.cfg.attn_implementation
         elif self.cfg.flex_attention:
             self.model_kwargs["attn_implementation"] = "flex_attention"

src/axolotl/loaders/patch_manager.py

@@ -156,6 +156,7 @@ class PatchManager:
             # which would clobber any earlier fix.
             self._fix_nemotron_h_conversion_mapping()
 
+        self._apply_gemma_hybrid_attention(model)
         self._finalize_moe_expert_quantization(model)
 
     def apply_post_model_load_patches(self, model: PreTrainedModel):
@@ -165,6 +166,72 @@ class PatchManager:
         self._apply_lora_kernel_patch(model)
         self._apply_scaling_softmax_patch(model)
 
+    def _apply_gemma_hybrid_attention(self, model: PreTrainedModel):
+        """Apply hybrid attention: FA2 for sliding window layers, SDPA for global layers.
+
+        Gemma 4 has global (full_attention) layers with head_dim=512
+        which exceeds flash attention's supported size. This patch loads the model
+        with flash_attention_2 for the sliding window layers (head_dim=256), then
+        gives each global layer a shallow-copied config with _attn_implementation="sdpa".
+        """
+        if not self.cfg.gemma4_hybrid_attn_impl:
+            return
+
+        import copy
+
+        # Navigate to the module that has 'layers' - varies by model structure:
+        # Gemma4ForConditionalGeneration -> .model (Gemma4Model) -> .language_model (Gemma4TextModel) -> .layers
+        # Gemma4ForCausalLM -> .model (Gemma4TextModel) -> .layers
+        layers = None
+        config_source = None
+        for candidate in [model, getattr(model, "model", None)]:
+            if candidate is None:
+                continue
+            # Check direct layers
+            if hasattr(candidate, "layers"):
+                layers = candidate.layers
+                config_source = candidate
+                break
+            # Check language_model.layers (multimodal wrapper)
+            lang_model = getattr(candidate, "language_model", None)
+            if lang_model is not None and hasattr(lang_model, "layers"):
+                layers = lang_model.layers
+                config_source = lang_model
+                break
+
+        if layers is None:
+            LOG.warning(
+                "gemma4_hybrid_attn_impl: could not find decoder layers in model, skipping"
+            )
+            return
+
+        config = getattr(config_source, "config", self.model_config)
+        layer_types = getattr(config, "layer_types", None)
+        if layer_types is None:
+            LOG.warning(
+                "gemma4_hybrid_attn_impl: model config has no 'layer_types', skipping. "
+                "This feature requires a model with mixed sliding/global attention layers."
+            )
+            return
+
+        patched_count = 0
+        for layer_idx, layer in enumerate(layers):
+            if layer_types[layer_idx] != "sliding_attention":
+                # Global / full_attention layer - use SDPA instead of FA2
+                attn_module = getattr(layer, "self_attn", None)
+                if attn_module is not None and hasattr(attn_module, "config"):
+                    sdpa_config = copy.copy(attn_module.config)
+                    sdpa_config._attn_implementation = "sdpa"
+                    attn_module.config = sdpa_config
+                    patched_count += 1
+
+        LOG.info(
+            "gemma4_hybrid_attn_impl: patched %d global layers to use SDPA "
+            "(remaining %d sliding layers use flash_attention_2)",
+            patched_count,
+            len(layers) - patched_count,
+        )
+
     def _apply_flash_attention_patches(self):
         """Apply patches related to Flash Attention."""
         if self.cfg.xformers_attention and self.cfg.sample_packing:
@@ -324,6 +391,13 @@ class PatchManager:
 
                 patch_qwen3_5_vlm_flash_attention()
 
+            if self.cfg.model_config_type in ("gemma4", "gemma4_text"):
+                from axolotl.monkeypatch.models.gemma4.fused_attn import (
+                    patch_gemma4_fused_attn,
+                )
+
+                patch_gemma4_fused_attn()
+
     @staticmethod
     def _fix_nemotron_h_conversion_mapping():
         """Remove the spurious embedding→embeddings WeightRenaming from the

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()
@@ -303,6 +295,14 @@ def load_tokenizer(cfg: DictDefault) -> PreTrainedTokenizer:
             {"additional_special_tokens": additional_special_tokens}
         )
 
+    # 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,
+        )
+
     if is_main_process():
         LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
         LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")

src/axolotl/monkeypatch/accelerate/fsdp2.py

@@ -60,6 +60,13 @@ def fsdp2_load_full_state_dict(
                 sharded_meta_param.placements,
                 src_data_rank=0,
             )
+            # Clone the local shard to allow full_tensor to be freed.
+            if (
+                sharded_param._local_tensor.untyped_storage().size()
+                > sharded_param._local_tensor.nelement()
+                * sharded_param._local_tensor.element_size()
+            ):
+                sharded_param = sharded_param.clone()
         else:
             # Non-sharded parameters
             if _accelerator.is_main_process:

src/axolotl/monkeypatch/attention/flash_attn_4.py

@@ -86,12 +86,19 @@ def patch_flash_attn_4(model_config=None):
     if getattr(fa_utils._lazy_imports, "_axolotl_patched", False):
         return
 
+    try:
+        # flash-attn-4>=4.0.0b7
+        from flash_attn.cute import flash_attn_with_kvcache
+    except ImportError:
+        flash_attn_with_kvcache = None
+
     def _patched_lazy_imports(
         implementation, attention_wrapper=None, allow_all_kernels=False
     ):
         return (
             flash_attn_func,
             flash_attn_varlen_func,
+            flash_attn_with_kvcache,
             fa_utils._pad_input,
             fa_utils._unpad_input,
         )

src/axolotl/monkeypatch/lora_kernels.py

@@ -16,6 +16,7 @@ from axolotl.kernels.lora import (
     apply_lora_mlp_geglu,
     apply_lora_mlp_swiglu,
     apply_lora_o,
+    apply_lora_qk,
     apply_lora_qkv,
 )
 from axolotl.monkeypatch.utils import detab_code
@@ -111,6 +112,47 @@ QKV_PATCHES = [
     else:
         key_states = key_states.view(hidden_shape)
         value_states = value_states.view(hidden_shape) if self.v_proj is not None else key_states
+""".lstrip("\n"),
+    ),
+    # Gemma4 (transformers >= 5.6): shared_kv_states parameter replaces
+    # past_key_values.shared_layers, and v_norm added after k_norm.
+    (
+        """
+    query_states = self.q_proj(hidden_states).view(hidden_shape)
+    query_states = self.q_norm(query_states)
+    query_states = apply_rotary_pos_emb(query_states, cos, sin, unsqueeze_dim=2)
+    query_states = query_states.transpose(1, 2)
+
+    # For layers with shared KV (from kv sharing point onwards), we reuse the same keys/values states as the last non-sharing layer.
+    # We cannot simply reuse the cached state if we have a Cache, as sliding layers will not remember the full states in their Cache
+    # once we are past the sliding window - so we always use `shared_kv_states` instead, even when past_key_values is not None
+    if self.is_kv_shared_layer:
+        key_states, value_states = shared_kv_states[self.kv_shared_layer_index]
+        # Device of past layer may be different from current one
+        key_states = key_states.to(query_states.device)
+        value_states = value_states.to(query_states.device)
+    else:
+        key_states = self.k_proj(hidden_states).view(hidden_shape)
+        value_states = self.v_proj(hidden_states).view(hidden_shape) if self.v_proj is not None else key_states
+""".lstrip("\n"),
+        """
+    query_states, key_states, value_states = self.apply_qkv(hidden_states)
+    query_states = query_states.view(hidden_shape)
+    query_states = self.q_norm(query_states)
+    query_states = apply_rotary_pos_emb(query_states, cos, sin, unsqueeze_dim=2)
+    query_states = query_states.transpose(1, 2)
+
+    # For layers with shared KV (from kv sharing point onwards), we reuse the same keys/values states as the last non-sharing layer.
+    # We cannot simply reuse the cached state if we have a Cache, as sliding layers will not remember the full states in their Cache
+    # once we are past the sliding window - so we always use `shared_kv_states` instead, even when past_key_values is not None
+    if self.is_kv_shared_layer:
+        key_states, value_states = shared_kv_states[self.kv_shared_layer_index]
+        # Device of past layer may be different from current one
+        key_states = key_states.to(query_states.device)
+        value_states = value_states.to(query_states.device)
+    else:
+        key_states = key_states.view(hidden_shape)
+        value_states = value_states.view(hidden_shape) if self.v_proj is not None else key_states
 """.lstrip("\n"),
     ),
 ]
@@ -483,18 +525,24 @@ def apply_lora_kernel_patches(
             if cfg.lora_qkv_kernel:
                 # Query, key, value patching
                 # Filter out None projections (e.g. Gemma4 v_proj when attention_k_eq_v=True)
-                proj_names = ["q_proj", "k_proj", "v_proj"]
-                layer_modules = [
-                    getattr(self_attn, name)
-                    for name in proj_names
-                    if getattr(self_attn, name, None) is not None
-                ]
+                has_v_proj = getattr(self_attn, "v_proj", None) is not None
+                proj_names = (
+                    ["q_proj", "k_proj", "v_proj"]
+                    if has_v_proj
+                    else ["q_proj", "k_proj"]
+                )
+                layer_modules = [getattr(self_attn, name) for name in proj_names]
                 can_patch_qkv = all(
                     hasattr(module, "lora_A") for module in layer_modules
                 )
 
                 if can_patch_qkv:
-                    self_attn.apply_qkv = types.MethodType(apply_lora_qkv, self_attn)
+                    if has_v_proj:
+                        self_attn.apply_qkv = types.MethodType(
+                            apply_lora_qkv, self_attn
+                        )
+                    else:
+                        self_attn.apply_qkv = types.MethodType(apply_lora_qk, self_attn)
                 else:
                     LOG.warning_once(
                         "Cannot patch some attention QKV projections - requires LoRA adapters"

src/axolotl/monkeypatch/models/gemma4/fused_attn.py

@@ -0,0 +1,147 @@
+"""
+Gemma 4 fused attention monkeypatch.
+
+Replaces the per-layer RMSNorm + RoPE + transpose sequence with fused Triton
+kernels, eliminating intermediate tensor allocations from rotate_half / apply_rotary_pos_emb
+
+Usage:
+    from axolotl.monkeypatch.models.gemma4.fused_attn import patch_gemma4_fused_attn
+    patch_gemma4_fused_attn()
+"""
+
+import logging
+from typing import Callable
+
+import torch
+
+logger = logging.getLogger(__name__)
+
+
+def _make_fused_forward(original_forward):
+    """Create a patched forward that uses fused RMSNorm+RoPE kernels."""
+
+    from axolotl.kernels.gemma4_fused_rope import (
+        fused_rms_norm_noscale,
+        fused_rms_norm_rope,
+    )
+
+    def fused_forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: torch.Tensor,
+        attention_mask: torch.Tensor | None,
+        shared_kv_states: dict[int, tuple[torch.Tensor, torch.Tensor]],
+        past_key_values=None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
+        from transformers.models.gemma4.modeling_gemma4 import (
+            eager_attention_forward,
+        )
+
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+        eps = self.config.rms_norm_eps
+
+        cos, sin = position_embeddings
+
+        # ---- Projections ----
+        # Use apply_qkv if present (LoRA kernel patch), otherwise direct proj
+        has_lora_qkv = hasattr(self, "apply_qkv")
+
+        if has_lora_qkv:
+            query_states, key_states, value_states = self.apply_qkv(hidden_states)
+            query_states = query_states.view(hidden_shape)
+        else:
+            query_states = self.q_proj(hidden_states).view(hidden_shape)
+
+        # ---- Q path: fused q_norm + RoPE ----
+        query_states = fused_rms_norm_rope(
+            query_states,
+            self.q_norm.weight,
+            cos,
+            sin,
+            eps=eps,
+        )
+        query_states = query_states.transpose(1, 2)
+
+        # ---- K/V path ----
+        if self.is_kv_shared_layer:
+            key_states, value_states = shared_kv_states[self.kv_shared_layer_index]
+            key_states = key_states.to(query_states.device)
+            value_states = value_states.to(query_states.device)
+        else:
+            if has_lora_qkv:
+                # apply_qkv already computed k/v projections
+                key_states = key_states.view(hidden_shape)
+                value_states = (
+                    value_states.view(hidden_shape)
+                    if self.v_proj is not None
+                    else key_states
+                )
+            else:
+                key_states = self.k_proj(hidden_states).view(hidden_shape)
+                value_states = (
+                    self.v_proj(hidden_states).view(hidden_shape)
+                    if self.v_proj is not None
+                    else key_states
+                )
+
+            # Fused k_norm + RoPE
+            key_states = fused_rms_norm_rope(
+                key_states,
+                self.k_norm.weight,
+                cos,
+                sin,
+                eps=eps,
+            )
+            key_states = key_states.transpose(1, 2)
+
+            # Fused v_norm (no scale, no RoPE)
+            value_states = fused_rms_norm_noscale(value_states, eps=eps)
+            value_states = value_states.transpose(1, 2)
+
+        if past_key_values is not None and not self.is_kv_shared_layer:
+            key_states, value_states = past_key_values.update(
+                key_states, value_states, self.layer_idx
+            )
+        if self.store_full_length_kv:
+            shared_kv_states[self.layer_idx] = key_states, value_states
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[
+                self.config._attn_implementation
+            ]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=self.attention_dropout if self.training else 0.0,
+            scaling=self.scaling,
+            sliding_window=self.sliding_window,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+    return fused_forward
+
+
+def patch_gemma4_fused_attn():
+    """
+    Monkeypatch Gemma4TextAttention.forward to use fused RMSNorm+RoPE kernels.
+    """
+    from transformers.models.gemma4.modeling_gemma4 import Gemma4TextAttention
+
+    original_forward = Gemma4TextAttention.forward
+    Gemma4TextAttention.forward = _make_fused_forward(original_forward)
+
+    logger.info(
+        "Patched Gemma4TextAttention.forward with fused RMSNorm+RoPE Triton kernels"
+    )

src/axolotl/prompt_strategies/chat_template.py

@@ -315,6 +315,13 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
         self._validate_eot_and_eos_tokens()
 
+        # Pre-cache EOT token IDs to avoid re-encoding on every call
+        self._eot_token_ids = set()
+        for token in self.eot_tokens:
+            token_ids = self.tokenizer.encode(token, add_special_tokens=False)
+            if len(token_ids) == 1:
+                self._eot_token_ids.add(token_ids[0])
+
     def _validate_eot_and_eos_tokens(self):
         """
         - Validates that EOT tokens (or eos_token) are in the chat_template
@@ -471,6 +478,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             content = turn.get("content")
             train_turn = turn.get("training")
             train_detail = turn.get("training_detail")
+            reasoning_train_detail = turn.get("reasoning_training_detail")
 
             LOG.debug(
                 f"Processing turn {index}: role={role}, content={content}, train_turn={train_turn}, train_detail={train_detail}"
@@ -479,8 +487,8 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             should_train = None
             if train_turn is not None:
                 should_train = train_turn
-            elif train_detail is not None:
-                should_train = bool(train_detail)
+            elif train_detail is not None or reasoning_train_detail is not None:
+                should_train = bool(train_detail) or bool(reasoning_train_detail)
             else:
                 should_train = self.train_on_inputs or role in self.roles_to_train
 
@@ -500,15 +508,26 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
                 continue
 
+            thinking_key = self.prompter.template_thinking_key
+            has_reasoning = thinking_key and turn.get(thinking_key) is not None
+            has_any_detail = train_detail or reasoning_train_detail
+
+            # When train_detail is present and the turn has reasoning_content,
+            # use content_only=True so find_turn returns content-only boundaries
+            # (excluding reasoning_content + template separator tokens).
+            use_content_only = bool(has_any_detail and has_reasoning)
+
             turn_start_idx, turn_end_idx = self.find_turn(
-                turns=turns, turn_idx=index, tools=tools
+                turns=turns,
+                turn_idx=index,
+                tools=tools,
+                content_only=use_content_only,
             )
 
             LOG.debug(f"Turn indices: start={turn_start_idx}, end={turn_end_idx}")
 
             if should_train and turn_start_idx != -1 and turn_end_idx != -1:
                 if train_detail:
-                    # Block multi-content for now
                     if not isinstance(content, str):
                         raise ValueError(
                             "`train_detail` is not supported when `content` is not a string."
@@ -526,7 +545,8 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                             LOG.debug(
                                 f"Label set at index {turn_start_idx + i}: {input_ids[turn_start_idx + i]}"
                             )
-                else:
+                elif not reasoning_train_detail:
+                    # No per-part detail on either field — train the whole span
                     labels[turn_start_idx:turn_end_idx] = input_ids[
                         turn_start_idx:turn_end_idx
                     ]
@@ -534,6 +554,32 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                         f"Set labels for training from {turn_start_idx} to {turn_end_idx}"
                     )
 
+            # Handle reasoning_content training_detail separately
+            if should_train and reasoning_train_detail and has_reasoning:
+                reasoning_text = turn[thinking_key]
+                if not isinstance(reasoning_text, str):
+                    raise ValueError(
+                        "`reasoning_training_detail` is not supported when reasoning_content is not a string."
+                    )
+
+                reasoning_start, reasoning_end = self.find_turn(
+                    turns=turns,
+                    turn_idx=index,
+                    tools=tools,
+                    reasoning_only=True,
+                )
+
+                if reasoning_start != -1 and reasoning_end != -1:
+                    token_offsets = self.prompter.get_offsets_for_train_detail(  # type: ignore
+                        reasoning_text, reasoning_train_detail
+                    )
+                    LOG.debug(f"Reasoning token offsets: {token_offsets}")
+                    for i, offset in enumerate(token_offsets):
+                        if offset != IGNORE_TOKEN_ID and reasoning_start + i < len(
+                            input_ids
+                        ):
+                            labels[reasoning_start + i] = input_ids[reasoning_start + i]
+
                 LOG.debug(f"Labels after processing turn {index}: {labels}")
 
             # Handle special tokens (EOT and EOS)
@@ -593,28 +639,31 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
     def find_first_eot_token(self, input_ids, start_idx):
         """Find the first EOT token in the input_ids starting from start_idx."""
-        # Get token IDs for all EOT tokens
-        eot_token_ids = []
-        for token in self.eot_tokens:
-            token_ids = self.tokenizer.encode(token, add_special_tokens=False)
-            if len(token_ids) != 1:
-                raise ValueError(
-                    f"EOT token '{token}' is encoded as multiple tokens: {token_ids}. Please add it under `tokens: ` in the config."
-                )
-
-            eot_token_ids.append(token_ids[0])  # Use the last token ID if multiple
-
-        # Search for any of the EOT token IDs
+        # Use pre-cached EOT token IDs (computed once in __init__)
         for i in range(start_idx, len(input_ids)):
-            if input_ids[i] in eot_token_ids:
+            if input_ids[i] in self._eot_token_ids:
                 return i
         return -1
 
     def find_turn(
-        self, turns: list[dict], turn_idx: int, tools: list[dict] | None = None
+        self,
+        turns: list[dict],
+        turn_idx: int,
+        tools: list[dict] | None = None,
+        content_only: bool = False,
+        reasoning_only: bool = False,
     ):
         """
         Locate the starting and ending indices of the specified turn in a conversation.
+
+        Args:
+            content_only: If True and the turn has reasoning_content (template_thinking_key),
+                preserve reasoning_content in the dummy turn so the diff only captures the
+                content field boundaries. This is needed for correct training_detail alignment
+                when reasoning_content is present.
+            reasoning_only: If True, preserve content in the dummy turn and replace
+                reasoning_content with a dummy, so the diff only captures the
+                reasoning_content field boundaries.
         """
 
         if turn_idx >= len(turns):
@@ -628,10 +677,26 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         ):
             return -1, -1
 
-        empty_turn = {
-            "role": turns[turn_idx].get("role"),
-            "content": "[[dummy_message]]",
-        }
+        thinking_key = self.prompter.template_thinking_key
+
+        if reasoning_only:
+            # Keep content as-is, replace reasoning with dummy
+            empty_turn = {
+                "role": turns[turn_idx].get("role"),
+                "content": turns[turn_idx].get("content", ""),
+            }
+            if thinking_key and thinking_key in turns[turn_idx]:
+                empty_turn[thinking_key] = "[[dummy_reasoning]]"
+        else:
+            empty_turn = {
+                "role": turns[turn_idx].get("role"),
+                "content": "[[dummy_message]]",
+            }
+
+            # When content_only is True, copy reasoning_content to the dummy turn so
+            # the diff only captures the content field (not reasoning + separator).
+            if content_only and thinking_key and thinking_key in turns[turn_idx]:
+                empty_turn[thinking_key] = turns[turn_idx][thinking_key]
 
         # Create conversation versions
         turns_with_empty = turns[:turn_idx] + [empty_turn]
@@ -697,6 +762,94 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
 
         return start_idx, end_idx
 
+    @staticmethod
+    def _convert_content_parts(
+        content,
+    ) -> tuple[str, list[dict] | None] | None:
+        """Convert list content to concatenated string + optional training_detail.
+
+        When content is a list of dicts (content parts), each part can specify:
+        - ``text``, ``content``, or ``value``: the text string
+        - ``train`` (bool) or ``weight`` (0/1): per-part training flag
+
+        Returns ``(concatenated_text, training_details_or_None)`` if content was
+        a list, or ``None`` if content was not a list (no conversion needed).
+
+        .. note::
+            **Whitespace at part boundaries matters.** BPE tokenizers prepend
+            spaces to word tokens (e.g. ``" answer"`` is one token). Always
+            split BEFORE spaces::
+
+                GOOD: ["Let me think...", " The answer is 4."]
+                BAD:  ["Let me think... ", "The answer is 4."]
+
+            Tokens that straddle a boundary are conservatively masked.
+            Newlines typically merge with preceding punctuation (``":\\n"`` is
+            one token), so keep newlines with the preceding part.
+        """
+        if not isinstance(content, list):
+            return None
+
+        text_parts: list[str] = []
+        training_details: list[dict] = []
+        has_explicit_training = False
+        offset = 0
+
+        for part in content:
+            if isinstance(part, dict):
+                # Extract text (HF uses "text", also support "content"/"value")
+                text = (
+                    part.get("text") or part.get("content") or part.get("value") or ""
+                )
+                text_parts.append(text)
+
+                # Check for per-part training flags
+                part_train = part.get("train")
+                part_weight = part.get("weight")
+                if part_train is not None or part_weight is not None:
+                    has_explicit_training = True
+                    train = (
+                        part_train
+                        if part_train is not None
+                        else (part_weight not in (0, 0.0))
+                    )
+                else:
+                    train = True  # default trainable, gated by turn-level should_train
+
+                if text:
+                    training_details.append(
+                        {
+                            "begin_offset": offset,
+                            "end_offset": offset + len(text) - 1,
+                            "train": train,
+                        }
+                    )
+                    offset += len(text)
+
+        # Warn about trailing whitespace at boundaries between parts with
+        # different training flags — this almost always causes token straddling
+        if has_explicit_training and len(training_details) > 1:
+            for i in range(len(training_details) - 1):
+                cur = training_details[i]
+                nxt = training_details[i + 1]
+                if cur["train"] != nxt["train"]:
+                    boundary_text = text_parts[i]
+                    if boundary_text and boundary_text[-1] in (" ", "\t"):
+                        LOG.warning(
+                            "Content part %d ends with whitespace at a train/mask boundary. "
+                            "BPE tokenizers typically prepend spaces to word tokens, so "
+                            "the space will merge with the next part's first word and the "
+                            "resulting token will be MASKED (not trained). Move the "
+                            "whitespace to the start of the next content part instead. "
+                            "Part text: %r",
+                            i,
+                            boundary_text[-20:],
+                        )
+
+        concatenated = "".join(text_parts)
+        details = training_details if has_explicit_training else None
+        return concatenated, details
+
     def get_conversation_thread(self, prompt):
         turns = []
 
@@ -723,6 +876,23 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             if training_detail is not None:
                 turn["training_detail"] = training_detail
 
+            # Convert list content/reasoning_content to string + auto-generated
+            # training_detail. See _convert_content_parts for whitespace guidance.
+            content_result = self._convert_content_parts(turn.get("content"))
+            if content_result is not None:
+                turn["content"] = content_result[0]
+                if content_result[1] is not None:
+                    turn["training_detail"] = content_result[1]
+
+            # Also convert reasoning_content (template_thinking_key) if it's a list
+            thinking_key = self.prompter.template_thinking_key
+            if thinking_key and thinking_key in turn:
+                reasoning_result = self._convert_content_parts(turn[thinking_key])
+                if reasoning_result is not None:
+                    turn[thinking_key] = reasoning_result[0]
+                    if reasoning_result[1] is not None:
+                        turn["reasoning_training_detail"] = reasoning_result[1]
+
             turns.append(turn)
 
         if self.prompter.drop_system_message and turns[0]["role"] == "system":

src/axolotl/train.py

@@ -36,7 +36,7 @@ from axolotl.telemetry.manager import TelemetryManager
 from axolotl.utils.ctx_managers.sequence_parallel import SequenceParallelContextManager
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import cleanup_distributed
-from axolotl.utils.freeze import freeze_layers_except
+from axolotl.utils.freeze import freeze_layers_except, freeze_mm_modules
 from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.enums import RLType
 from axolotl.utils.train import determine_last_checkpoint
@@ -114,6 +114,10 @@ def setup_model_and_tokenizer(
         ):
             model.enable_input_require_grads()
 
+    # Freeze multimodal modules for text-only training of multimodal models
+    if cfg.freeze_mm_modules:
+        freeze_mm_modules(model)
+
     return model, tokenizer, peft_config, processor
 
 
@@ -225,6 +229,28 @@ def execute_training(
         PLUGIN_MANAGER.post_train(cfg, trainer.model)
 
 
+def _rename_fsdp_merged_to_adapter(merged_dir: Path):
+    """Rename model*.safetensors files to adapter_model* in place.
+
+    Also rewrites the index JSON weight_map if sharded output was produced.
+    """
+    for file in sorted(merged_dir.iterdir()):
+        if file.name.startswith("model") and ".safetensors" in file.name:
+            file.rename(merged_dir / file.name.replace("model", "adapter_model", 1))
+
+    index = merged_dir / "adapter_model.safetensors.index.json"
+    if index.exists():
+        data = json.loads(index.read_text(encoding="utf-8"))
+        if "weight_map" in data:
+            data["weight_map"] = {
+                k: v.replace("model", "adapter_model", 1)
+                for k, v in data["weight_map"].items()
+            }
+        index.write_text(
+            json.dumps(data, indent=2, sort_keys=True) + "\n", encoding="utf-8"
+        )
+
+
 def save_trained_model(
     cfg: DictDefault,
     trainer: Any,
@@ -294,12 +320,17 @@ def save_trained_model(
                 )
                 trainer.accelerator.wait_for_everyone()
                 if trainer.accelerator.is_main_process:
-                    # move all files in merged_path to cfg.output_dir
+                    # FSDP checkpoints for PEFT only contain adapter weights;
+                    # rename model* → adapter_model* so it loads correctly.
+                    is_peft = cfg.adapter and not cfg.relora
+                    if is_peft:
+                        _rename_fsdp_merged_to_adapter(Path(merged_path))
                     for merged_file in Path(merged_path).iterdir():
-                        if (Path(cfg.output_dir) / merged_file.name).exists():
-                            (Path(cfg.output_dir) / merged_file.name).unlink()
-                        shutil.move(str(merged_file), cfg.output_dir)
-                    shutil.rmtree(merged_path)  # remove what should be an empty dir
+                        dest = Path(cfg.output_dir) / merged_file.name
+                        if dest.exists():
+                            dest.unlink()
+                        shutil.move(str(merged_file), dest)
+                    shutil.rmtree(merged_path)
         # TODO(wing):see https://github.com/huggingface/transformers/pull/40207
         # cleanup the FSDP prefix in the model config.json
         if trainer.accelerator.is_main_process:

src/axolotl/utils/callbacks/__init__.py

@@ -98,6 +98,56 @@ class SaveModelOnFirstStepCallback(TrainerCallback):
         return control
 
 
+class SkipEvalOnResumeCallback(TrainerCallback):
+    """Skip the redundant evaluation that fires when resuming from a checkpoint
+    whose step aligns with ``eval_steps``.
+
+    When HuggingFace Trainer resumes, it restores ``global_step`` from the
+    checkpoint and immediately triggers ``_maybe_log_save_evaluate`` for that
+    step.  Because the evaluation was already performed during the original
+    run, repeating it wastes time and pollutes metric logs.
+
+    This callback records the ``global_step`` at the start of training (i.e.
+    the checkpoint step when resuming, or 0 for a fresh run) and suppresses
+    any evaluation request on that exact step.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._resume_step: int | None = None
+
+    def on_train_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **_kwargs,
+    ):
+        # ``global_step`` is already restored from the checkpoint at this
+        # point.  For a fresh run it will be 0, so the guard below becomes a
+        # no-op.
+        self._resume_step = state.global_step
+
+    def on_step_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **_kwargs,
+    ) -> TrainerControl:
+        if (
+            self._resume_step
+            and state.global_step <= self._resume_step
+            and control.should_evaluate
+        ):
+            LOG.info(
+                "Skipping evaluation at step %d (already completed before resume)",
+                state.global_step,
+            )
+            control.should_evaluate = False
+        return control
+
+
 def bench_eval_callback_factory(trainer, tokenizer):
     accuracy = evaluate.load("accuracy")
     abcd_idx = [

src/axolotl/utils/chat_templates/templates/qwen3_5.jinja

@@ -1,7 +1,19 @@
 {%- if tools %}
     {{- '<|im_start|>system\n' }}
     {%- if messages[0].role == 'system' %}
-        {{- messages[0].content + '\n\n' }}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content + '\n\n' }}
+        {%- else %}
+            {%- for part in messages[0].content %}
+                {%- if part is mapping %}
+                    {%- set system_text = part.get('text') or part.get('content') or part.get('value') %}
+                    {%- if system_text %}{{- system_text }}{%- endif %}
+                {%- elif part is string %}
+                    {{- part }}
+                {%- endif %}
+            {%- endfor %}
+            {{- '\n\n' }}
+        {%- endif %}
     {%- endif %}
     {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
     {%- for tool in tools %}
@@ -11,7 +23,20 @@
     {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
 {%- else %}
     {%- if messages[0].role == 'system' %}
-        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+        {%- if messages[0].content is string %}
+            {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+        {%- else %}
+            {{- '<|im_start|>system\n' }}
+            {%- for part in messages[0].content %}
+                {%- if part is mapping %}
+                    {%- set system_text = part.get('text') or part.get('content') or part.get('value') %}
+                    {%- if system_text %}{{- system_text }}{%- endif %}
+                {%- elif part is string %}
+                    {{- part }}
+                {%- endif %}
+            {%- endfor %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
     {%- endif %}
 {%- endif %}
 {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}

src/axolotl/utils/config/__init__.py

@@ -268,6 +268,37 @@ def normalize_config(cfg):
     ):
         cfg.gradient_checkpointing_kwargs = {"use_reentrant": True}
 
+    # Gemma4 requires use_reentrant=False for DDP (shared per-layer norms cause
+    # "marked ready twice" errors with reentrant checkpointing) and
+    # ddp_find_unused_parameters=True (per_layer_projection LoRA params may not
+    # receive gradients on every step).
+    if cfg.model_config_type == "gemma4":
+        if cfg.gradient_checkpointing:
+            if cfg.gradient_checkpointing_kwargs is None:
+                cfg.gradient_checkpointing_kwargs = {}
+            if cfg.gradient_checkpointing_kwargs.get("use_reentrant") is not False:
+                LOG.warning(
+                    "Gemma4 requires use_reentrant=False for gradient checkpointing "
+                    "in distributed training. Setting use_reentrant=False."
+                )
+                cfg.gradient_checkpointing_kwargs["use_reentrant"] = False
+        if cfg.ddp and cfg.ddp_find_unused_parameters is None:
+            if cfg.activation_offloading is True:
+                # activation_offloading uses checkpoint wrappers that conflict
+                # with find_unused_parameters (causes "marked ready twice").
+                # Use freeze_mm_modules instead to eliminate unused params.
+                LOG.info(
+                    "Gemma4 + DDP + activation_offloading: skipping "
+                    "ddp_find_unused_parameters (use freeze_mm_modules to "
+                    "handle unused vision/audio params)."
+                )
+            else:
+                LOG.warning(
+                    "Gemma4 requires ddp_find_unused_parameters=True for DDP. "
+                    "Auto-enabling."
+                )
+                cfg.ddp_find_unused_parameters = True
+
     log_gpu_memory_usage(LOG, "baseline", cfg.device)
 
 

src/axolotl/utils/data/rl.py

@@ -180,6 +180,119 @@ def _drop_long_sequences(
     raise ValueError("Unknown RL type")
 
 
+def _raise_on_long_sequences(
+    sample: dict[str, Any], rl: RLType, tokenizer: Any, sequence_len: int
+) -> bool:
+    """Check sequence length and raise ValueError if exceeded.
+
+    Used as a filter function for ``excess_length_strategy: raise``.
+
+    Args:
+        sample: Dataset sample to check.
+        rl: Reinforcement learning type.
+        tokenizer: Tokenizer for length calculation.
+        sequence_len: Maximum allowed sequence length.
+
+    Returns:
+        Always True (raises before returning False).
+
+    Raises:
+        ValueError: If any sample exceeds the configured sequence length.
+    """
+    is_valid = _drop_long_sequences(sample, rl, tokenizer, sequence_len)
+    if not is_valid:
+        raise ValueError(
+            f"Sample exceeds configured sequence_len ({sequence_len}). "
+            "Set `excess_length_strategy: drop` or `excess_length_strategy: truncate` "
+            "to handle long sequences automatically."
+        )
+    return True
+
+
+def _truncate_long_sequences_rl(
+    sample: dict[str, Any], rl: RLType, tokenizer: Any, sequence_len: int
+) -> dict[str, Any]:
+    """Truncate RL samples that exceed maximum sequence length.
+
+    For preference datasets (DPO/IPO/ORPO/SIMPO), truncates chosen and rejected
+    responses to fit within ``sequence_len`` when combined with the prompt.
+    For KTO, truncates the completion similarly.
+    GRPO/GDPO/EBFT samples are returned unchanged.
+
+    Samples where the prompt alone exceeds ``sequence_len`` cannot be
+    meaningfully truncated and are returned unchanged.  The caller should
+    follow up with a drop filter to remove them.
+
+    Args:
+        sample: Dataset sample to potentially truncate.
+        rl: Reinforcement learning type.
+        tokenizer: Tokenizer for encoding/decoding.
+        sequence_len: Maximum allowed sequence length.
+
+    Returns:
+        The sample with text fields truncated to fit within sequence_len.
+    """
+    # Fast path: if sample already fits, return unchanged (avoids decode overhead)
+    if _drop_long_sequences(sample, rl, tokenizer, sequence_len):
+        return sample
+
+    if rl in {RLType.DPO, RLType.IPO, RLType.ORPO, RLType.SIMPO}:
+        if not (
+            sample.get("prompt") and sample.get("chosen") and sample.get("rejected")
+        ):
+            raise ValueError(
+                "Prompt, chosen and rejected keys are required for DPO/ORPO datasets"
+            )
+
+        prompt_ids = tokenizer(sample["prompt"], add_special_tokens=False)["input_ids"]
+        chosen_ids = tokenizer(sample["chosen"], add_special_tokens=False)["input_ids"]
+        rejected_ids = tokenizer(sample["rejected"], add_special_tokens=False)[
+            "input_ids"
+        ]
+
+        max_response_len = sequence_len - len(prompt_ids)
+        if max_response_len <= 0:
+            # Prompt alone exceeds limit; cannot meaningfully truncate.
+            # Returned unchanged — the follow-up drop filter will remove it.
+            return sample
+
+        updates: dict[str, Any] = {}
+        if len(chosen_ids) > max_response_len:
+            updates["chosen"] = tokenizer.decode(
+                chosen_ids[:max_response_len], skip_special_tokens=False
+            )
+        if len(rejected_ids) > max_response_len:
+            updates["rejected"] = tokenizer.decode(
+                rejected_ids[:max_response_len], skip_special_tokens=False
+            )
+        if updates:
+            sample = {**sample, **updates}
+
+    elif rl is RLType.KTO:
+        if not (sample.get("prompt") and sample.get("completion")):
+            raise ValueError("Prompt and completion keys are required for KTO datasets")
+
+        prompt_ids = tokenizer(sample["prompt"], add_special_tokens=False)["input_ids"]
+        completion_ids = tokenizer(sample["completion"], add_special_tokens=False)[
+            "input_ids"
+        ]
+
+        max_completion_len = sequence_len - len(prompt_ids)
+        if max_completion_len <= 0:
+            return sample
+
+        if len(completion_ids) > max_completion_len:
+            sample = {
+                **sample,
+                "completion": tokenizer.decode(
+                    completion_ids[:max_completion_len], skip_special_tokens=False
+                ),
+            }
+
+    # GRPO/GDPO/EBFT: no truncation needed (responses generated at runtime)
+    return sample
+
+
 def _load_split(cfg: DictDefault, split: Literal["train", "test"]) -> Dataset:
     """Load and process dataset split for RL training.
 
@@ -243,23 +356,77 @@ def _load_split(cfg: DictDefault, split: Literal["train", "test"]) -> Dataset:
             split_datasets[i] = dataset
 
         if not cfg.skip_prepare_dataset:
-            drop_long = partial(
-                _drop_long_sequences,
-                rl=cfg.rl,
-                tokenizer=tokenizer,
-                sequence_len=cfg.sequence_len,
-            )
+            excess_length_strategy = (cfg.excess_length_strategy or "drop").lower()
 
-            prior_len = len(split_datasets[i])
-            split_datasets[i] = split_datasets[i].filter(
-                drop_long,
-                num_proc=cfg.dataset_num_proc,
-                load_from_cache_file=not cfg.is_preprocess,
-                desc="Dropping Long Sequences",
-            )
-            dropped = prior_len - len(split_datasets[i])
-            if dropped:
-                LOG.warning(f"Dropped {dropped} long samples from dataset index {i}")
+            if excess_length_strategy == "truncate":
+                truncate_fn = partial(
+                    _truncate_long_sequences_rl,
+                    rl=cfg.rl,
+                    tokenizer=tokenizer,
+                    sequence_len=cfg.sequence_len,
+                )
+                prior_len = len(split_datasets[i])
+                split_datasets[i] = split_datasets[i].map(
+                    truncate_fn,
+                    num_proc=cfg.dataset_num_proc,
+                    load_from_cache_file=not cfg.is_preprocess,
+                    desc="Truncating Long Sequences",
+                )
+
+                # Drop samples that could not be truncated (e.g. prompt
+                # alone exceeds sequence_len)
+                drop_long = partial(
+                    _drop_long_sequences,
+                    rl=cfg.rl,
+                    tokenizer=tokenizer,
+                    sequence_len=cfg.sequence_len,
+                )
+                split_datasets[i] = split_datasets[i].filter(
+                    drop_long,
+                    num_proc=cfg.dataset_num_proc,
+                    load_from_cache_file=not cfg.is_preprocess,
+                    desc="Dropping Un-truncatable Sequences",
+                )
+                dropped = prior_len - len(split_datasets[i])
+                if dropped:
+                    LOG.warning(
+                        f"Dropped {dropped} samples from dataset index {i} "
+                        f"that could not be truncated to fit sequence_len "
+                        f"(prompt alone exceeds limit)"
+                    )
+            elif excess_length_strategy == "raise":
+                raise_fn = partial(
+                    _raise_on_long_sequences,
+                    rl=cfg.rl,
+                    tokenizer=tokenizer,
+                    sequence_len=cfg.sequence_len,
+                )
+                split_datasets[i] = split_datasets[i].filter(
+                    raise_fn,
+                    num_proc=cfg.dataset_num_proc,
+                    load_from_cache_file=not cfg.is_preprocess,
+                    desc="Checking Sequence Lengths",
+                )
+            else:  # "drop" (default)
+                drop_long = partial(
+                    _drop_long_sequences,
+                    rl=cfg.rl,
+                    tokenizer=tokenizer,
+                    sequence_len=cfg.sequence_len,
+                )
+
+                prior_len = len(split_datasets[i])
+                split_datasets[i] = split_datasets[i].filter(
+                    drop_long,
+                    num_proc=cfg.dataset_num_proc,
+                    load_from_cache_file=not cfg.is_preprocess,
+                    desc="Dropping Long Sequences",
+                )
+                dropped = prior_len - len(split_datasets[i])
+                if dropped:
+                    LOG.warning(
+                        f"Dropped {dropped} long samples from dataset index {i}"
+                    )
 
     # Merge datasets
     dataset = merge_datasets(split_datasets, cfg)

src/axolotl/utils/freeze.py

@@ -10,6 +10,44 @@ from axolotl.utils.logging import get_logger
 
 LOG = get_logger(__name__)
 
+# Top-level module name prefixes that belong to vision/audio/multimodal encoders
+# rather than the language backbone.  These are matched against the first component
+# of each ``named_parameter`` path (e.g. "model.vision_tower." -> "vision_tower").
+_MM_MODULE_PREFIXES = (
+    "vision_tower",
+    "vision_model",
+    "vision_encoder",
+    "embed_vision",
+    "multi_modal_projector",
+    "visual",
+    "audio_tower",
+    "audio_model",
+    "embed_audio",
+)
+
+
+def freeze_mm_modules(model):
+    """Freeze all vision/audio/multimodal-projector parameters.
+
+    Iterates over ``model.named_parameters()`` and sets ``requires_grad = False``
+    for any parameter whose name contains a known vision/audio module prefix.
+    This is useful when fine-tuning only the language backbone of a multimodal
+    model and avoids the need for ``ddp_find_unused_parameters=True``.
+    """
+    frozen_count = 0
+    for name, param in model.named_parameters():
+        # Check if any path component matches a vision/audio prefix
+        parts = name.split(".")
+        if any(part in _MM_MODULE_PREFIXES for part in parts):
+            if param.requires_grad:
+                param.requires_grad = False
+                frozen_count += 1
+                if is_main_process():
+                    LOG.debug(f"freeze_mm_modules: froze {name}")
+
+    if is_main_process():
+        LOG.info(f"freeze_mm_modules: froze {frozen_count} vision/audio parameters")
+
 
 def freeze_layers_except(model, regex_patterns):
     """

src/axolotl/utils/schemas/config.py

@@ -578,6 +578,17 @@ class AxolotlInputConfig(
         },
     )
 
+    freeze_mm_modules: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Freeze multimodal encoder parameters (vision, audio, etc.) for "
+            "text-only training of multimodal models. When True, parameters belonging to "
+            "vision towers, audio towers, multimodal projectors, and similar non-language "
+            "modules are frozen (requires_grad=False). This allows DDP training without "
+            "ddp_find_unused_parameters=True."
+        },
+    )
+
     unfrozen_parameters: list[str] | None = Field(
         default=None,
         json_schema_extra={
@@ -766,6 +777,15 @@ class AxolotlInputConfig(
         },
     )
 
+    gemma4_hybrid_attn_impl: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Use hybrid attention for Gemma 4: flash_attention_2 for sliding window layers "
+            "and sdpa for global (full_attention) layers. Global layers have head_dim=512 which "
+            "exceeds flash attention's supported size."
+        },
+    )
+
     experts_implementation: str | None = Field(
         default=None,
         json_schema_extra={

src/axolotl/utils/schemas/model.py

@@ -87,9 +87,11 @@ class ModelInputConfig(BaseModel):
         json_schema_extra={"description": "Use custom kernels, e.g. MegaBlocks."},
     )
 
-    model_quantization_config: Literal["Mxfp4Config"] | None = Field(
-        default=None,
-        json_schema_extra={"description": "Model loading quantization config"},
+    model_quantization_config: Literal["Mxfp4Config", "FineGrainedFP8Config"] | None = (
+        Field(
+            default=None,
+            json_schema_extra={"description": "Model loading quantization config"},
+        )
     )
     model_quantization_config_kwargs: dict[str, Any] | None = Field(
         default=None,

tests/e2e/integrations/test_scattermoe_lora_olmoe.py

@@ -422,22 +422,22 @@ class TestPeftLoRAWeightExtraction:
         )
 
         # gate_up_proj [E, 2*inter, hidden]
-        # peft: in_features=2*inter (dim 1), out_features=hidden (dim 2)
+        # peft: in_features=hidden (last dim), out_features=2*inter (middle dim)
         assert trainable[
             "base_model.model.moe.experts.base_layer.lora_A.default.weight"
-        ].shape == (E * r, 2 * config.intermediate_size)
-        assert trainable[
-            "base_model.model.moe.experts.base_layer.lora_B.default.weight"
-        ].shape == (config.hidden_size, E * r)
-
-        # down_proj [E, hidden, inter]
-        # peft: in_features=hidden (dim 1), out_features=inter (dim 2)
-        assert trainable[
-            "base_model.model.moe.experts.lora_A.default.weight"
         ].shape == (E * r, config.hidden_size)
+        assert trainable[
+            "base_model.model.moe.experts.base_layer.lora_B.default.weight"
+        ].shape == (2 * config.intermediate_size, E * r)
+
+        # down_proj [E, hidden, inter]
+        # peft: in_features=inter (last dim), out_features=hidden (middle dim)
+        assert trainable[
+            "base_model.model.moe.experts.lora_A.default.weight"
+        ].shape == (E * r, config.intermediate_size)
         assert trainable[
             "base_model.model.moe.experts.lora_B.default.weight"
-        ].shape == (config.intermediate_size, E * r)
+        ].shape == (config.hidden_size, E * r)
 
     @requires_cuda
     def test_peft_forward_runs(self):
@@ -489,26 +489,28 @@ class TestPeftLoRAWeightExtraction:
         assert down_lora is not None, "down_proj LoRA not detected"
 
         # Check shapes (after peft->scattermoe conversion with A<->B swap)
-        # gate_up_proj W = param.T = [E, hidden, 2*inter], K=hidden, N=2*inter
+        # gate_up_proj: peft A [E*r, hidden] / B [2*inter, E*r]
+        # After swap: smoe_A [E*r, 2*inter], smoe_B [hidden, E*r]
         E, r = config.num_experts, 4
         gup_A, gup_B, gup_s = gup_lora
-        assert gup_A.shape == (E * r, config.hidden_size), (
-            f"gate_up_proj smoe_A: expected [r*E, K=hidden]={(E * r, config.hidden_size)}, "
+        assert gup_A.shape == (E * r, 2 * config.intermediate_size), (
+            f"gate_up_proj smoe_A: expected [r*E, 2*inter]={(E * r, 2 * config.intermediate_size)}, "
             f"got {gup_A.shape}"
         )
-        assert gup_B.shape == (2 * config.intermediate_size, E * r), (
-            f"gate_up_proj smoe_B: expected [N=2*inter, r*E]="
-            f"{(2 * config.intermediate_size, E * r)}, got {gup_B.shape}"
+        assert gup_B.shape == (config.hidden_size, E * r), (
+            f"gate_up_proj smoe_B: expected [hidden, r*E]="
+            f"{(config.hidden_size, E * r)}, got {gup_B.shape}"
         )
 
-        # down_proj W = param.T = [E, inter, hidden], K=inter, N=hidden
+        # down_proj: peft A [E*r, inter] / B [hidden, E*r]
+        # After swap: smoe_A [E*r, hidden], smoe_B [inter, E*r]
         down_A, down_B, down_s = down_lora
-        assert down_A.shape == (E * r, config.intermediate_size), (
-            f"down_proj smoe_A: expected [r*E, K=inter]={(E * r, config.intermediate_size)}, "
+        assert down_A.shape == (E * r, config.hidden_size), (
+            f"down_proj smoe_A: expected [r*E, hidden]={(E * r, config.hidden_size)}, "
             f"got {down_A.shape}"
         )
-        assert down_B.shape == (config.hidden_size, E * r), (
-            f"down_proj smoe_B: expected [N=hidden, r*E]={(config.hidden_size, E * r)}, "
+        assert down_B.shape == (config.intermediate_size, E * r), (
+            f"down_proj smoe_B: expected [inter, r*E]={(config.intermediate_size, E * r)}, "
             f"got {down_B.shape}"
         )
 

tests/kernels/test_gemma4_fused_rope.py

@@ -0,0 +1,226 @@
+"""
+Correctness tests for the fused RMSNorm+RoPE Triton kernel.
+
+Tests forward and backward against the reference Gemma4 implementation
+(Gemma4RMSNorm + apply_rotary_pos_emb) across both sliding window
+(head_dim=256) and global attention (head_dim=512) layer configurations.
+"""
+
+import pytest
+import torch
+
+torch.manual_seed(42)
+
+# Skip entire module if no CUDA
+pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
+
+
+def _reference_norm_rope(x, weight, cos, sin, eps):
+    """Reference: separate Gemma4RMSNorm + apply_rotary_pos_emb."""
+    from transformers.models.gemma4.modeling_gemma4 import (
+        Gemma4RMSNorm,
+        apply_rotary_pos_emb,
+    )
+
+    D = x.shape[-1]
+    norm = Gemma4RMSNorm(D, eps=eps).to(x.device, x.dtype)
+    norm.weight.data.copy_(weight)
+    normed = norm(x)
+    return apply_rotary_pos_emb(normed, cos, sin, unsqueeze_dim=2)
+
+
+def _reference_norm_noscale(x, eps):
+    """Reference: Gemma4RMSNorm with_scale=False."""
+    from transformers.models.gemma4.modeling_gemma4 import Gemma4RMSNorm
+
+    D = x.shape[-1]
+    norm = Gemma4RMSNorm(D, eps=eps, with_scale=False).to(x.device, x.dtype)
+    return norm(x)
+
+
+@pytest.fixture(
+    params=[
+        (2, 64, 32, 256),  # sliding window layer shape
+        (2, 64, 4, 512),  # global attention layer shape
+        (1, 128, 16, 256),  # different batch/seq
+        (1, 1, 1, 8),  # minimal size
+    ],
+    ids=["sliding_256", "global_512", "varied", "minimal"],
+)
+def shapes(request):
+    return request.param
+
+
+@pytest.fixture(params=[torch.bfloat16, torch.float16], ids=["bf16", "fp16"])
+def dtype(request):
+    return request.param
+
+
+class TestFusedRMSNormRoPEForward:
+    """Forward pass correctness."""
+
+    def test_matches_reference(self, shapes, dtype):
+        from axolotl.kernels.gemma4_fused_rope import fused_rms_norm_rope
+
+        B, S, H, D = shapes
+        eps = 1e-6
+        x = torch.randn(B, S, H, D, device="cuda", dtype=dtype)
+        weight = torch.randn(D, device="cuda", dtype=dtype)
+        cos = torch.randn(B, S, D, device="cuda", dtype=dtype)
+        sin = torch.randn(B, S, D, device="cuda", dtype=dtype)
+
+        y_ref = _reference_norm_rope(x.clone(), weight, cos, sin, eps)
+        y_fused = fused_rms_norm_rope(x.clone(), weight, cos, sin, eps=eps)
+
+        cos_sim = torch.nn.functional.cosine_similarity(
+            y_ref.flatten().float(), y_fused.flatten().float(), dim=0
+        )
+        assert cos_sim > 0.999, f"Forward cosine_sim={cos_sim:.6f}, expected > 0.999"
+
+    def test_output_shape(self, shapes):
+        from axolotl.kernels.gemma4_fused_rope import fused_rms_norm_rope
+
+        B, S, H, D = shapes
+        x = torch.randn(B, S, H, D, device="cuda", dtype=torch.bfloat16)
+        weight = torch.randn(D, device="cuda", dtype=torch.bfloat16)
+        cos = torch.randn(B, S, D, device="cuda", dtype=torch.bfloat16)
+        sin = torch.randn(B, S, D, device="cuda", dtype=torch.bfloat16)
+
+        y = fused_rms_norm_rope(x, weight, cos, sin, eps=1e-6)
+        assert y.shape == x.shape
+        assert y.dtype == x.dtype
+
+
+class TestFusedRMSNormRoPEBackward:
+    """Backward pass correctness via gradient comparison."""
+
+    @pytest.mark.parametrize(
+        "B,S,H,D",
+        [(2, 64, 32, 256), (2, 64, 4, 512)],
+        ids=["sliding_256", "global_512"],
+    )
+    def test_x_grad_matches_reference(self, B, S, H, D):
+        from transformers.models.gemma4.modeling_gemma4 import (
+            Gemma4RMSNorm,
+            apply_rotary_pos_emb,
+        )
+
+        from axolotl.kernels.gemma4_fused_rope import fused_rms_norm_rope
+
+        eps = 1e-6
+        cos = torch.randn(B, S, D, device="cuda", dtype=torch.bfloat16)
+        sin = torch.randn(B, S, D, device="cuda", dtype=torch.bfloat16)
+        weight_init = torch.randn(D, device="cuda", dtype=torch.bfloat16)
+
+        # Reference backward
+        x_ref = torch.randn(
+            B, S, H, D, device="cuda", dtype=torch.bfloat16, requires_grad=True
+        )
+        norm_ref = Gemma4RMSNorm(D, eps=eps).cuda().to(torch.bfloat16)
+        norm_ref.weight.data.copy_(weight_init)
+        y_ref = apply_rotary_pos_emb(norm_ref(x_ref), cos, sin, unsqueeze_dim=2)
+        y_ref.sum().backward()
+
+        # Fused backward
+        x_fused = x_ref.data.clone().requires_grad_(True)
+        w_fused = weight_init.clone().requires_grad_(True)
+        y_fused = fused_rms_norm_rope(x_fused, w_fused, cos, sin, eps=eps)
+        y_fused.sum().backward()
+
+        cos_sim_x = torch.nn.functional.cosine_similarity(
+            x_fused.grad.flatten().float(), x_ref.grad.flatten().float(), dim=0
+        )
+        assert cos_sim_x > 0.999, f"x grad cosine_sim={cos_sim_x:.6f}, expected > 0.999"
+
+    @pytest.mark.parametrize(
+        "B,S,H,D",
+        [(2, 64, 32, 256), (2, 64, 4, 512)],
+        ids=["sliding_256", "global_512"],
+    )
+    def test_weight_grad_matches_reference(self, B, S, H, D):
+        from transformers.models.gemma4.modeling_gemma4 import (
+            Gemma4RMSNorm,
+            apply_rotary_pos_emb,
+        )
+
+        from axolotl.kernels.gemma4_fused_rope import fused_rms_norm_rope
+
+        eps = 1e-6
+        cos = torch.randn(B, S, D, device="cuda", dtype=torch.bfloat16)
+        sin = torch.randn(B, S, D, device="cuda", dtype=torch.bfloat16)
+        weight_init = torch.randn(D, device="cuda", dtype=torch.bfloat16)
+
+        # Reference
+        x_ref = torch.randn(B, S, H, D, device="cuda", dtype=torch.bfloat16)
+        norm_ref = Gemma4RMSNorm(D, eps=eps).cuda().to(torch.bfloat16)
+        norm_ref.weight = torch.nn.Parameter(weight_init.clone())
+        apply_rotary_pos_emb(
+            norm_ref(x_ref), cos, sin, unsqueeze_dim=2
+        ).sum().backward()
+
+        # Fused
+        w_fused = weight_init.clone().requires_grad_(True)
+        fused_rms_norm_rope(x_ref.clone(), w_fused, cos, sin, eps=eps).sum().backward()
+
+        cos_sim_w = torch.nn.functional.cosine_similarity(
+            w_fused.grad.flatten().float(),
+            norm_ref.weight.grad.flatten().float(),
+            dim=0,
+        )
+        assert cos_sim_w > 0.995, (
+            f"weight grad cosine_sim={cos_sim_w:.6f}, expected > 0.995"
+        )
+
+    def test_grad_flows(self):
+        """Verify gradients are non-zero and finite."""
+        from axolotl.kernels.gemma4_fused_rope import fused_rms_norm_rope
+
+        B, S, H, D = 1, 16, 4, 64
+        x = torch.randn(
+            B, S, H, D, device="cuda", dtype=torch.bfloat16, requires_grad=True
+        )
+        w = torch.randn(D, device="cuda", dtype=torch.bfloat16, requires_grad=True)
+        cos = torch.randn(B, S, D, device="cuda", dtype=torch.bfloat16)
+        sin = torch.randn(B, S, D, device="cuda", dtype=torch.bfloat16)
+
+        y = fused_rms_norm_rope(x, w, cos, sin, eps=1e-6)
+        y.sum().backward()
+
+        assert x.grad is not None, "x.grad is None"
+        assert w.grad is not None, "w.grad is None"
+        assert x.grad.isfinite().all(), "x.grad has non-finite values"
+        assert w.grad.isfinite().all(), "w.grad has non-finite values"
+        assert x.grad.abs().sum() > 0, "x.grad is all zeros"
+        assert w.grad.abs().sum() > 0, "w.grad is all zeros"
+
+
+class TestFusedRMSNormNoScale:
+    """Tests for v_norm (RMSNorm without learnable scale)."""
+
+    def test_forward_matches_reference(self, shapes, dtype):
+        from axolotl.kernels.gemma4_fused_rope import fused_rms_norm_noscale
+
+        B, S, H, D = shapes
+        eps = 1e-6
+        x = torch.randn(B, S, H, D, device="cuda", dtype=dtype)
+
+        y_ref = _reference_norm_noscale(x.clone(), eps)
+        y_fused = fused_rms_norm_noscale(x.clone(), eps=eps)
+
+        cos_sim = torch.nn.functional.cosine_similarity(
+            y_ref.flatten().float(), y_fused.flatten().float(), dim=0
+        )
+        assert cos_sim > 0.999, f"v_norm cosine_sim={cos_sim:.6f}, expected > 0.999"
+
+    def test_backward_flows(self):
+        from axolotl.kernels.gemma4_fused_rope import fused_rms_norm_noscale
+
+        x = torch.randn(
+            1, 16, 4, 64, device="cuda", dtype=torch.bfloat16, requires_grad=True
+        )
+        y = fused_rms_norm_noscale(x, eps=1e-6)
+        y.sum().backward()
+
+        assert x.grad is not None
+        assert x.grad.isfinite().all()
+        assert x.grad.abs().sum() > 0

tests/prompt_strategies/test_chat_templates_advanced.py

@@ -916,6 +916,235 @@ class TestChatTemplateConfigurations:
         LOG.debug(f"Final labels: {labels}")
         LOG.debug(f"Final input_ids: {input_ids}")
 
+    @enable_hf_offline
+    def test_content_parts_training(
+        self,
+        tokenizer,
+        chat_template,
+        chat_template_jinja,
+        eos_token,
+        request,
+    ):
+        LOG.info("Testing with content as list of parts with per-part training")
+
+        tokenizer, chat_template_jinja = self.setup_tokenizer(
+            tokenizer, chat_template, chat_template_jinja, eos_token, request
+        )
+
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(
+                tokenizer,
+                chat_template=get_chat_template(
+                    chat_template, jinja_template=chat_template_jinja
+                ),
+                message_property_mappings={"role": "role", "content": "content"},
+            ),
+            tokenizer=tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+        )
+
+        # Dataset where assistant content is a list of parts with per-part training
+        conversation = [
+            {
+                "role": "system",
+                "content": [
+                    {"type": "text", "text": "You are an AI assistant."},
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "What is 2+2?"},
+                ],
+            },
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "Let me think...", "train": False},
+                    {"type": "text", "text": "The answer is 4.", "train": True},
+                ],
+            },
+        ]
+
+        dataset = Dataset.from_dict({"messages": [conversation]})
+        res = strategy.tokenize_prompt(dataset[0])
+        turns = strategy.get_conversation_thread(dataset[0])
+        labels = res["labels"]
+        input_ids = res["input_ids"]
+
+        # Find the assistant turn (last turn)
+        assistant_turn_idx = len(turns) - 1
+        start_idx, end_idx = strategy.find_turn(
+            turns=turns, turn_idx=assistant_turn_idx
+        )
+
+        assert start_idx != -1 and end_idx != -1, (
+            "Could not find assistant turn boundaries"
+        )
+
+        decoded = tokenizer.decode(input_ids[start_idx:end_idx])
+        LOG.debug(f"Assistant turn decoded: {decoded}")
+
+        # Tokenize each part separately to find their boundaries
+        part1_text = "Let me think..."
+        part2_text = "The answer is 4."
+
+        # Verify the concatenated content is in the decoded output
+        assert part1_text in decoded, (
+            f"Part 1 '{part1_text}' not found in decoded: {decoded}"
+        )
+        assert part2_text in decoded, (
+            f"Part 2 '{part2_text}' not found in decoded: {decoded}"
+        )
+
+        # Verify that part1 tokens (train=False) are masked
+        # and part2 tokens (train=True) are labeled
+        turn_labels = labels[start_idx:end_idx]
+
+        # Find where part2 starts in the token sequence
+        part1_tokens = tokenizer(part1_text, add_special_tokens=False)["input_ids"]
+        part2_tokens = tokenizer(part2_text, add_special_tokens=False)["input_ids"]
+
+        # The first part should be masked (all IGNORE_TOKEN_ID)
+        # Due to token boundary alignment, check that at least the interior tokens
+        # of part1 are masked
+        assert any(label == IGNORE_TOKEN_ID for label in turn_labels), (
+            f"Expected some masked labels for train=False part, but got {turn_labels}"
+        )
+
+        # The second part should be trained (not IGNORE_TOKEN_ID)
+        assert any(label != IGNORE_TOKEN_ID for label in turn_labels), (
+            f"Expected some trained labels for train=True part, but got {turn_labels}"
+        )
+
+        # More precise check: first N tokens should be masked, last M tokens should be trained
+        # where N ~ len(part1_tokens) and M ~ len(part2_tokens)
+        # Allow for token boundary effects at the boundary
+        num_masked = sum(1 for label in turn_labels if label == IGNORE_TOKEN_ID)
+        num_trained = sum(1 for label in turn_labels if label != IGNORE_TOKEN_ID)
+
+        LOG.debug(f"Turn labels: {turn_labels}")
+        LOG.debug(f"Masked tokens: {num_masked}, Trained tokens: {num_trained}")
+        LOG.debug(
+            f"Part1 tokens: {len(part1_tokens)}, Part2 tokens: {len(part2_tokens)}"
+        )
+
+        # The number of masked tokens should be roughly the size of part1
+        # and the number of trained tokens should be roughly the size of part2
+        assert num_masked > 0, "Expected masked tokens for the train=False part"
+        assert num_trained > 0, "Expected trained tokens for the train=True part"
+
+    @enable_hf_offline
+    def test_content_parts_with_weight(
+        self,
+        tokenizer,
+        chat_template,
+        chat_template_jinja,
+        eos_token,
+        request,
+    ):
+        LOG.info("Testing with content parts using weight field")
+
+        tokenizer, chat_template_jinja = self.setup_tokenizer(
+            tokenizer, chat_template, chat_template_jinja, eos_token, request
+        )
+
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(
+                tokenizer,
+                chat_template=get_chat_template(
+                    chat_template, jinja_template=chat_template_jinja
+                ),
+                message_property_mappings={"role": "role", "content": "content"},
+            ),
+            tokenizer=tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+        )
+
+        # Dataset using weight instead of train
+        conversation = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Hello"},
+                ],
+            },
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "Thinking step by step: ", "weight": 0},
+                    {"type": "text", "text": "Hello! How can I help?", "weight": 1},
+                ],
+            },
+        ]
+
+        dataset = Dataset.from_dict({"messages": [conversation]})
+        res = strategy.tokenize_prompt(dataset[0])
+        labels = res["labels"]
+
+        # There should be both masked and trained labels
+        has_masked = any(label == IGNORE_TOKEN_ID for label in labels)
+        has_trained = any(label != IGNORE_TOKEN_ID for label in labels)
+        assert has_masked, "Expected masked tokens (weight=0 part + user turn)"
+        assert has_trained, "Expected trained tokens (weight=1 part)"
+
+    @enable_hf_offline
+    def test_content_parts_string_passthrough(
+        self,
+        tokenizer,
+        chat_template,
+        chat_template_jinja,
+        eos_token,
+        request,
+    ):
+        LOG.info("Testing that string content still works alongside list content")
+
+        tokenizer, chat_template_jinja = self.setup_tokenizer(
+            tokenizer, chat_template, chat_template_jinja, eos_token, request
+        )
+
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(
+                tokenizer,
+                chat_template=get_chat_template(
+                    chat_template, jinja_template=chat_template_jinja
+                ),
+                message_property_mappings={"role": "role", "content": "content"},
+            ),
+            tokenizer=tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+        )
+
+        # All list content in the conversation
+        conversation = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "What is 2+2?"},
+                ],
+            },
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "The answer is 4.", "train": True},
+                ],
+            },
+        ]
+
+        dataset = Dataset.from_dict({"messages": [conversation]})
+        res = strategy.tokenize_prompt(dataset[0])
+
+        # Should tokenize without errors
+        assert "input_ids" in res
+        assert "labels" in res
+        assert len(res["input_ids"]) > 0
+
     def test_get_chat_template_variables(
         self, tokenizer, chat_template, chat_template_jinja, eos_token, request
     ):
@@ -1428,3 +1657,250 @@ class TestChatTemplateToolCalling:
                 assert all(label != IGNORE_TOKEN_ID for label in turn_labels), (
                     f"Assistant turn {i} should be unmasked"
                 )
+
+
+class TestChatTemplateReasoningContent:
+    """
+    Test class for reasoning_content with content parts.
+    """
+
+    @enable_hf_offline
+    def test_reasoning_content_with_content_parts(self, qwen3_tokenizer):
+        """Test that reasoning_content as string + content as list parts works correctly.
+        Content training_detail offsets should align with content-only boundaries."""
+        LOG.info("Testing reasoning_content with content parts on qwen3")
+
+        tokenizer = deepcopy(qwen3_tokenizer)
+
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(
+                tokenizer,
+                chat_template=get_chat_template("qwen3"),
+                message_property_mappings={
+                    "role": "role",
+                    "content": "content",
+                    "reasoning_content": "reasoning_content",
+                },
+            ),
+            tokenizer=tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+        )
+
+        # reasoning_content is a plain string, content is list with per-part training
+        conversation = [
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": "What is 2+2?"}],
+            },
+            {
+                "role": "assistant",
+                "reasoning_content": "Step 1: 2+2=4",
+                "content": [
+                    {"type": "text", "text": "The answer is 4.", "train": True},
+                ],
+            },
+        ]
+
+        dataset = Dataset.from_dict({"messages": [conversation]})
+        res = strategy.tokenize_prompt(dataset[0])
+        turns = strategy.get_conversation_thread(dataset[0])
+        labels = res["labels"]
+        input_ids = res["input_ids"]
+
+        # Find the assistant turn
+        assistant_idx = 1
+        start_idx, end_idx = strategy.find_turn(
+            turns=turns, turn_idx=assistant_idx, content_only=True
+        )
+
+        assert start_idx != -1 and end_idx != -1, (
+            "Could not find assistant content boundaries"
+        )
+
+        # The content-only span should contain "The answer is 4." but NOT "Step 1: 2+2=4"
+        decoded_span = tokenizer.decode(input_ids[start_idx:end_idx])
+        assert "The answer is 4." in decoded_span, (
+            f"Content not found in span: {decoded_span}"
+        )
+        assert "Step 1" not in decoded_span, (
+            f"Reasoning should not be in content-only span: {decoded_span}"
+        )
+
+        # Verify that content tokens are trained
+        content_labels = labels[start_idx:end_idx]
+        assert any(label != IGNORE_TOKEN_ID for label in content_labels), (
+            f"Expected trained labels in content span, got {content_labels}"
+        )
+
+    @enable_hf_offline
+    def test_reasoning_content_per_part_masking(self, qwen3_tokenizer):
+        """Test masking incorrect reasoning while training on self-correction.
+        This is the core use case: mask out wrong thoughts, train on corrections."""
+        LOG.info("Testing reasoning_content per-part masking on qwen3")
+
+        tokenizer = deepcopy(qwen3_tokenizer)
+
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(
+                tokenizer,
+                chat_template=get_chat_template("qwen3"),
+                message_property_mappings={
+                    "role": "role",
+                    "content": "content",
+                    "reasoning_content": "reasoning_content",
+                },
+            ),
+            tokenizer=tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+        )
+
+        # Reasoning has wrong step (masked) then self-correction (trained)
+        conversation = [
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": "What is 2+2?"}],
+            },
+            {
+                "role": "assistant",
+                "reasoning_content": [
+                    {"type": "text", "text": "Hmm maybe 2+2=5.", "train": False},
+                    {"type": "text", "text": " Wait no, 2+2=4.", "train": True},
+                ],
+                "content": [
+                    {"type": "text", "text": "The answer is 4.", "train": True},
+                ],
+            },
+        ]
+
+        dataset = Dataset.from_dict({"messages": [conversation]})
+        res = strategy.tokenize_prompt(dataset[0])
+        turns = strategy.get_conversation_thread(dataset[0])
+        labels = res["labels"]
+        input_ids = res["input_ids"]
+
+        # Find reasoning boundaries
+        reasoning_start, reasoning_end = strategy.find_turn(
+            turns=turns, turn_idx=1, reasoning_only=True
+        )
+        assert reasoning_start != -1 and reasoning_end != -1, (
+            "Could not find reasoning boundaries"
+        )
+
+        decoded_reasoning = tokenizer.decode(input_ids[reasoning_start:reasoning_end])
+        LOG.debug(f"Reasoning span: {decoded_reasoning!r}")
+        assert "2+2=5" in decoded_reasoning, (
+            f"Wrong step not in reasoning span: {decoded_reasoning}"
+        )
+        assert "2+2=4" in decoded_reasoning, (
+            f"Correction not in reasoning span: {decoded_reasoning}"
+        )
+
+        # Verify reasoning labels have both masked and trained tokens
+        reasoning_labels = labels[reasoning_start:reasoning_end]
+        reasoning_ids = input_ids[reasoning_start:reasoning_end]
+
+        # Decode only the trained tokens — should be exactly the self-correction
+        trained_ids = [
+            tid
+            for tid, lab in zip(reasoning_ids, reasoning_labels, strict=True)
+            if lab != IGNORE_TOKEN_ID
+        ]
+        trained_text = tokenizer.decode(trained_ids)
+        assert trained_text.strip() == "Wait no, 2+2=4.", (
+            f"Expected trained reasoning to be 'Wait no, 2+2=4.', got: {trained_text!r}"
+        )
+
+        # Decode only the masked tokens — should be exactly the incorrect step
+        masked_ids = [
+            tid
+            for tid, lab in zip(reasoning_ids, reasoning_labels, strict=True)
+            if lab == IGNORE_TOKEN_ID
+        ]
+        masked_text = tokenizer.decode(masked_ids)
+        assert masked_text.strip() == "Hmm maybe 2+2=5.", (
+            f"Expected masked reasoning to be 'Hmm maybe 2+2=5.', got: {masked_text!r}"
+        )
+
+        # Find content boundaries
+        content_start, content_end = strategy.find_turn(
+            turns=turns, turn_idx=1, content_only=True
+        )
+        assert content_start != -1 and content_end != -1, (
+            "Could not find content boundaries"
+        )
+
+        # Content should be fully trained — decode trained tokens to verify
+        content_labels = labels[content_start:content_end]
+        content_ids = input_ids[content_start:content_end]
+        content_trained_ids = [
+            tid
+            for tid, lab in zip(content_ids, content_labels, strict=True)
+            if lab != IGNORE_TOKEN_ID
+        ]
+        content_trained_text = tokenizer.decode(content_trained_ids)
+        assert "The answer is 4." in content_trained_text, (
+            f"Expected 'The answer is 4.' in trained content tokens, "
+            f"got: {content_trained_text!r}"
+        )
+        assert all(label != IGNORE_TOKEN_ID for label in content_labels), (
+            f"Expected all content labels trained, got {content_labels}"
+        )
+
+    @enable_hf_offline
+    def test_reasoning_content_as_list_no_training_flags(self, qwen3_tokenizer):
+        """Test that reasoning_content as list without training flags still works."""
+        LOG.info("Testing reasoning_content as list without training flags on qwen3")
+
+        tokenizer = deepcopy(qwen3_tokenizer)
+
+        strategy = ChatTemplateStrategy(
+            ChatTemplatePrompter(
+                tokenizer,
+                chat_template=get_chat_template("qwen3"),
+                message_property_mappings={
+                    "role": "role",
+                    "content": "content",
+                    "reasoning_content": "reasoning_content",
+                },
+            ),
+            tokenizer=tokenizer,
+            train_on_inputs=False,
+            sequence_len=512,
+            roles_to_train=["assistant"],
+        )
+
+        # Both as lists, no per-part training flags
+        conversation = [
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": "What is 2+2?"}],
+            },
+            {
+                "role": "assistant",
+                "reasoning_content": [
+                    {"type": "text", "text": "Step 1: addition."},
+                    {"type": "text", "text": " Step 2: 2+2=4."},
+                ],
+                "content": [
+                    {"type": "text", "text": "The answer is 4."},
+                ],
+            },
+        ]
+
+        dataset = Dataset.from_dict({"messages": [conversation]})
+        res = strategy.tokenize_prompt(dataset[0])
+
+        # Should tokenize without errors
+        assert "input_ids" in res
+        assert "labels" in res
+        assert len(res["input_ids"]) > 0
+
+        # Verify the full output contains both reasoning and content
+        full_text = tokenizer.decode(res["input_ids"])
+        assert "Step 1: addition." in full_text
+        assert "Step 2: 2+2=4." in full_text
+        assert "The answer is 4." in full_text

tests/utils/callbacks/test_skip_eval_on_resume.py

@@ -0,0 +1,63 @@
+"""Tests for SkipEvalOnResumeCallback."""
+
+from unittest.mock import MagicMock
+
+from transformers import TrainerControl, TrainerState, TrainingArguments
+
+from axolotl.utils.callbacks import SkipEvalOnResumeCallback
+
+
+class TestSkipEvalOnResumeCallback:
+    """Tests for skipping redundant evaluation on checkpoint resume."""
+
+    @staticmethod
+    def _make_state(global_step: int) -> TrainerState:
+        state = MagicMock(spec=TrainerState)
+        state.global_step = global_step
+        return state
+
+    def test_suppresses_eval_at_resume_step(self):
+        cb = SkipEvalOnResumeCallback()
+        args = MagicMock(spec=TrainingArguments)
+        state = self._make_state(20)
+        control = TrainerControl(should_evaluate=False)
+
+        # Simulate on_train_begin at checkpoint-20
+        cb.on_train_begin(args, state, control)
+
+        # Trainer sets should_evaluate = True for step 20
+        control.should_evaluate = True
+        result = cb.on_step_end(args, state, control)
+
+        assert result.should_evaluate is False
+
+    def test_allows_eval_after_resume_step(self):
+        cb = SkipEvalOnResumeCallback()
+        args = MagicMock(spec=TrainingArguments)
+        state = self._make_state(20)
+        control = TrainerControl(should_evaluate=False)
+
+        cb.on_train_begin(args, state, control)
+
+        # Advance past the resume point
+        state.global_step = 30
+        control.should_evaluate = True
+        result = cb.on_step_end(args, state, control)
+
+        assert result.should_evaluate is True
+
+    def test_noop_on_fresh_run(self):
+        cb = SkipEvalOnResumeCallback()
+        args = MagicMock(spec=TrainingArguments)
+        state = self._make_state(0)
+        control = TrainerControl(should_evaluate=False)
+
+        # Fresh run: global_step starts at 0
+        cb.on_train_begin(args, state, control)
+
+        # Even if eval triggers at step 0 (unlikely but defensive)
+        state.global_step = 10
+        control.should_evaluate = True
+        result = cb.on_step_end(args, state, control)
+
+        assert result.should_evaluate is True

tests/utils/data/test_rl.py

@@ -0,0 +1,292 @@
+"""
+Unit tests for RL data utility functions (excess_length_strategy support).
+"""
+
+import unittest
+
+from axolotl.utils.data.rl import (
+    _drop_long_sequences,
+    _raise_on_long_sequences,
+    _truncate_long_sequences_rl,
+)
+from axolotl.utils.schemas.enums import RLType
+
+
+class _FakeTokenizer:
+    """Simple whitespace tokenizer for testing length calculations."""
+
+    def __call__(self, text, add_special_tokens=True):  # noqa: ARG002
+        tokens = text.split()
+        return {"input_ids": list(range(len(tokens)))}
+
+    def decode(self, token_ids, skip_special_tokens=True):  # noqa: ARG002
+        # Each token id maps to a placeholder word; length is what matters.
+        return " ".join(f"w{i}" for i in range(len(token_ids)))
+
+
+def _make_dpo_sample(prompt_len: int, chosen_len: int, rejected_len: int):
+    """Create a DPO sample with specified word counts."""
+    return {
+        "prompt": " ".join(f"p{i}" for i in range(prompt_len)),
+        "chosen": " ".join(f"c{i}" for i in range(chosen_len)),
+        "rejected": " ".join(f"r{i}" for i in range(rejected_len)),
+    }
+
+
+def _make_kto_sample(prompt_len: int, completion_len: int):
+    """Create a KTO sample with specified word counts."""
+    return {
+        "prompt": " ".join(f"p{i}" for i in range(prompt_len)),
+        "completion": " ".join(f"c{i}" for i in range(completion_len)),
+    }
+
+
+class TestDropLongSequences(unittest.TestCase):
+    """Tests for the existing _drop_long_sequences filter function."""
+
+    def setUp(self):
+        self.tokenizer = _FakeTokenizer()
+
+    def test_dpo_keeps_short_samples(self):
+        sample = _make_dpo_sample(prompt_len=3, chosen_len=2, rejected_len=2)
+        result = _drop_long_sequences(
+            sample, RLType.DPO, self.tokenizer, sequence_len=10
+        )
+        self.assertTrue(result)
+
+    def test_dpo_drops_long_chosen(self):
+        sample = _make_dpo_sample(prompt_len=5, chosen_len=10, rejected_len=2)
+        result = _drop_long_sequences(
+            sample, RLType.DPO, self.tokenizer, sequence_len=10
+        )
+        self.assertFalse(result)
+
+    def test_dpo_drops_long_rejected(self):
+        sample = _make_dpo_sample(prompt_len=5, chosen_len=2, rejected_len=10)
+        result = _drop_long_sequences(
+            sample, RLType.DPO, self.tokenizer, sequence_len=10
+        )
+        self.assertFalse(result)
+
+    def test_kto_keeps_short_samples(self):
+        sample = _make_kto_sample(prompt_len=3, completion_len=2)
+        result = _drop_long_sequences(
+            sample, RLType.KTO, self.tokenizer, sequence_len=10
+        )
+        self.assertTrue(result)
+
+    def test_kto_drops_long_completion(self):
+        sample = _make_kto_sample(prompt_len=5, completion_len=10)
+        result = _drop_long_sequences(
+            sample, RLType.KTO, self.tokenizer, sequence_len=10
+        )
+        self.assertFalse(result)
+
+    def test_grpo_always_keeps(self):
+        sample = {"prompt": "a " * 100}
+        result = _drop_long_sequences(
+            sample, RLType.GRPO, self.tokenizer, sequence_len=5
+        )
+        self.assertTrue(result)
+
+    def test_dpo_missing_keys_raises(self):
+        with self.assertRaises(ValueError):
+            _drop_long_sequences({"prompt": "hi"}, RLType.DPO, self.tokenizer, 10)
+
+    def test_kto_missing_keys_raises(self):
+        with self.assertRaises(ValueError):
+            _drop_long_sequences({"prompt": "hi"}, RLType.KTO, self.tokenizer, 10)
+
+    def test_ipo_uses_dpo_logic(self):
+        sample = _make_dpo_sample(prompt_len=5, chosen_len=10, rejected_len=2)
+        result = _drop_long_sequences(
+            sample, RLType.IPO, self.tokenizer, sequence_len=10
+        )
+        self.assertFalse(result)
+
+    def test_orpo_uses_dpo_logic(self):
+        sample = _make_dpo_sample(prompt_len=3, chosen_len=2, rejected_len=2)
+        result = _drop_long_sequences(
+            sample, RLType.ORPO, self.tokenizer, sequence_len=10
+        )
+        self.assertTrue(result)
+
+    def test_boundary_length_kept(self):
+        """Samples exactly at sequence_len should be kept."""
+        sample = _make_dpo_sample(prompt_len=5, chosen_len=5, rejected_len=5)
+        result = _drop_long_sequences(
+            sample, RLType.DPO, self.tokenizer, sequence_len=10
+        )
+        self.assertTrue(result)
+
+
+class TestRaiseOnLongSequences(unittest.TestCase):
+    """Tests for _raise_on_long_sequences (excess_length_strategy='raise')."""
+
+    def setUp(self):
+        self.tokenizer = _FakeTokenizer()
+
+    def test_short_sample_passes(self):
+        sample = _make_dpo_sample(prompt_len=3, chosen_len=2, rejected_len=2)
+        result = _raise_on_long_sequences(
+            sample, RLType.DPO, self.tokenizer, sequence_len=10
+        )
+        self.assertTrue(result)
+
+    def test_long_sample_raises_valueerror(self):
+        sample = _make_dpo_sample(prompt_len=5, chosen_len=10, rejected_len=2)
+        with self.assertRaises(ValueError, msg="excess_length_strategy"):
+            _raise_on_long_sequences(
+                sample, RLType.DPO, self.tokenizer, sequence_len=10
+            )
+
+    def test_kto_long_raises(self):
+        sample = _make_kto_sample(prompt_len=5, completion_len=10)
+        with self.assertRaises(ValueError):
+            _raise_on_long_sequences(
+                sample, RLType.KTO, self.tokenizer, sequence_len=10
+            )
+
+    def test_grpo_never_raises(self):
+        sample = {"prompt": "a " * 100}
+        result = _raise_on_long_sequences(
+            sample, RLType.GRPO, self.tokenizer, sequence_len=5
+        )
+        self.assertTrue(result)
+
+
+class TestTruncateLongSequencesRL(unittest.TestCase):
+    """Tests for _truncate_long_sequences_rl (excess_length_strategy='truncate')."""
+
+    def setUp(self):
+        self.tokenizer = _FakeTokenizer()
+
+    def test_dpo_short_sample_unchanged(self):
+        sample = _make_dpo_sample(prompt_len=3, chosen_len=2, rejected_len=2)
+        result = _truncate_long_sequences_rl(
+            sample, RLType.DPO, self.tokenizer, sequence_len=10
+        )
+        self.assertEqual(result["chosen"], sample["chosen"])
+        self.assertEqual(result["rejected"], sample["rejected"])
+
+    def test_dpo_truncates_chosen(self):
+        sample = _make_dpo_sample(prompt_len=5, chosen_len=10, rejected_len=3)
+        result = _truncate_long_sequences_rl(
+            sample, RLType.DPO, self.tokenizer, sequence_len=10
+        )
+        # max_response_len = 10 - 5 = 5, chosen had 10 words -> truncated to 5
+        chosen_tokens = self.tokenizer(result["chosen"], add_special_tokens=False)[
+            "input_ids"
+        ]
+        self.assertEqual(len(chosen_tokens), 5)
+
+    def test_dpo_truncates_rejected(self):
+        sample = _make_dpo_sample(prompt_len=5, chosen_len=3, rejected_len=10)
+        result = _truncate_long_sequences_rl(
+            sample, RLType.DPO, self.tokenizer, sequence_len=10
+        )
+        rejected_tokens = self.tokenizer(result["rejected"], add_special_tokens=False)[
+            "input_ids"
+        ]
+        self.assertEqual(len(rejected_tokens), 5)
+
+    def test_dpo_truncates_both(self):
+        sample = _make_dpo_sample(prompt_len=5, chosen_len=10, rejected_len=10)
+        result = _truncate_long_sequences_rl(
+            sample, RLType.DPO, self.tokenizer, sequence_len=10
+        )
+        chosen_len = len(
+            self.tokenizer(result["chosen"], add_special_tokens=False)["input_ids"]
+        )
+        rejected_len = len(
+            self.tokenizer(result["rejected"], add_special_tokens=False)["input_ids"]
+        )
+        self.assertEqual(chosen_len, 5)
+        self.assertEqual(rejected_len, 5)
+
+    def test_dpo_prompt_unchanged(self):
+        """Prompt text should never be modified."""
+        sample = _make_dpo_sample(prompt_len=5, chosen_len=10, rejected_len=10)
+        result = _truncate_long_sequences_rl(
+            sample, RLType.DPO, self.tokenizer, sequence_len=10
+        )
+        self.assertEqual(result["prompt"], sample["prompt"])
+
+    def test_dpo_prompt_exceeds_limit_returns_unchanged(self):
+        """When prompt alone exceeds sequence_len, sample is returned as-is."""
+        sample = _make_dpo_sample(prompt_len=15, chosen_len=3, rejected_len=3)
+        result = _truncate_long_sequences_rl(
+            sample, RLType.DPO, self.tokenizer, sequence_len=10
+        )
+        self.assertEqual(result, sample)
+
+    def test_kto_truncates_completion(self):
+        sample = _make_kto_sample(prompt_len=5, completion_len=10)
+        result = _truncate_long_sequences_rl(
+            sample, RLType.KTO, self.tokenizer, sequence_len=10
+        )
+        completion_len = len(
+            self.tokenizer(result["completion"], add_special_tokens=False)["input_ids"]
+        )
+        self.assertEqual(completion_len, 5)
+
+    def test_kto_short_sample_unchanged(self):
+        sample = _make_kto_sample(prompt_len=3, completion_len=2)
+        result = _truncate_long_sequences_rl(
+            sample, RLType.KTO, self.tokenizer, sequence_len=10
+        )
+        self.assertEqual(result["completion"], sample["completion"])
+
+    def test_kto_prompt_exceeds_limit_returns_unchanged(self):
+        sample = _make_kto_sample(prompt_len=15, completion_len=3)
+        result = _truncate_long_sequences_rl(
+            sample, RLType.KTO, self.tokenizer, sequence_len=10
+        )
+        self.assertEqual(result, sample)
+
+    def test_grpo_unchanged(self):
+        sample = {"prompt": "a " * 100}
+        result = _truncate_long_sequences_rl(
+            sample, RLType.GRPO, self.tokenizer, sequence_len=5
+        )
+        self.assertEqual(result, sample)
+
+    def test_ipo_uses_dpo_logic(self):
+        sample = _make_dpo_sample(prompt_len=5, chosen_len=10, rejected_len=3)
+        result = _truncate_long_sequences_rl(
+            sample, RLType.IPO, self.tokenizer, sequence_len=10
+        )
+        chosen_len = len(
+            self.tokenizer(result["chosen"], add_special_tokens=False)["input_ids"]
+        )
+        self.assertEqual(chosen_len, 5)
+
+    def test_does_not_mutate_original(self):
+        """Verify immutability — original sample dict is not modified."""
+        sample = _make_dpo_sample(prompt_len=5, chosen_len=10, rejected_len=10)
+        original_chosen = sample["chosen"]
+        original_rejected = sample["rejected"]
+        _truncate_long_sequences_rl(sample, RLType.DPO, self.tokenizer, sequence_len=10)
+        self.assertEqual(sample["chosen"], original_chosen)
+        self.assertEqual(sample["rejected"], original_rejected)
+
+    def test_dpo_missing_keys_raises(self):
+        with self.assertRaises(ValueError):
+            _truncate_long_sequences_rl(
+                {"prompt": "hi"}, RLType.DPO, self.tokenizer, 10
+            )
+
+    def test_kto_missing_keys_raises(self):
+        with self.assertRaises(ValueError):
+            _truncate_long_sequences_rl(
+                {"prompt": "hi"}, RLType.KTO, self.tokenizer, 10
+            )
+
+    def test_boundary_no_truncation_needed(self):
+        """Samples exactly at sequence_len should not be modified."""
+        sample = _make_dpo_sample(prompt_len=5, chosen_len=5, rejected_len=5)
+        result = _truncate_long_sequences_rl(
+            sample, RLType.DPO, self.tokenizer, sequence_len=10
+        )
+        self.assertEqual(result["chosen"], sample["chosen"])
+        self.assertEqual(result["rejected"], sample["rejected"])

tests/utils/lora/test_merge_lora.py

@@ -2,6 +2,7 @@ import json
 import math
 from unittest.mock import Mock, patch
 
+import pytest
 import safetensors.torch
 import torch
 
@@ -773,8 +774,8 @@ class TestEfficientMerge:
             "v_proj should be unchanged (no LoRA weights for it)"
         )
 
-    def test_dora_missing_magnitude_falls_back(self):
-        """DoRA without magnitude vector falls back to standard LoRA merge."""
+    def test_dora_missing_magnitude_raises(self):
+        """DoRA with missing magnitude vector raises an explicit error."""
         hidden = 16
         r = 4
         alpha = 8
@@ -791,11 +792,13 @@ class TestEfficientMerge:
         }
 
         config = {"r": r, "lora_alpha": alpha, "use_dora": True}
-        merged, was_merged = _merge_tensor_with_lora(
-            base, "layer.proj.weight", lora_state, scale, config, "cpu", use_dora=True
-        )
-        assert was_merged
-        # No magnitude vector → PEFT creates DoRA layer but with default magnitude,
-        # which produces a result different from plain W + scale * B @ A.
-        # Just verify it was merged (not unchanged).
-        assert not torch.equal(merged, base)
+        with pytest.raises(ValueError, match="DoRA merge requires a magnitude vector"):
+            _merge_tensor_with_lora(
+                base,
+                "layer.proj.weight",
+                lora_state,
+                scale,
+                config,
+                "cpu",
+                use_dora=True,
+            )