adding fp32 support

* feat: upgrade cce with patches for transformers 4.56

* feat: add missing models to cce readme

_quarto.yml

@@ -267,6 +267,7 @@ website:
             - docs/dataset_loading.qmd
             - docs/qat.qmd
             - docs/quantize.qmd
+            - docs/optimizations.qmd
 
         - section: "Core Concepts"
           contents:

docs/dataset-formats/conversation.qmd

@@ -212,6 +212,14 @@ Instead of passing `tools` via the system prompt, an alternative method would be
 Tools need to follow [JSON schema](https://json-schema.org/learn/getting-started-step-by-step).
 :::
 
+::: {.callout-warning}
+If you have tool arguments with same name but different dtypes (like `"time": string` and `"time": number`), please save `arguments: ` as JSON string to prevent `datasets` from having casting issues.
+
+```
+"arguments": "{\"...\": \"...\"}"
+```
+:::
+
 Example config for Llama4:
 ```yaml
 chat_template: llama4

docs/dataset-formats/index.qmd

@@ -61,7 +61,7 @@ While we recommend `.jsonl`, you can also use the other formats (`csv`, `parquet
 
 ### Pre-training without streaming
 
-On the rare case that the dataset is small and can be loaded entirely into memory, another approach to running pre-training is to use the `completion` format. This would mean that the entire dataset is pre-tokenized instead of on-demand in streaming.
+In the case that the dataset is small and can be loaded entirely into memory, another approach to running pre-training is to use the `completion` format. This would mean that the entire dataset is pre-tokenized instead of on-demand in streaming.
 
 One benefit of this is that the tokenization can be performed separately on a CPU-only machine, and then transferred to a GPU machine for training to save costs.
 

docs/faq.qmd

@@ -140,3 +140,7 @@ description: Frequently asked questions
 **Q: `ValueError("Backward pass should have cleared tracker of all tensors")`
 
 > A: This may happen due to edge cases in using the modern OffloadActivations context manager for CUDA streams. If you encounter this error, you may have success using the naive implementation with `offload_activations: legacy` in your YAML.
+
+**Q: `Error parsing tool_calls arguments as JSON.`
+
+> A: There is an error parsing string arguments to a dict. Please check your dataset and the error message for more details.

docs/multimodal.qmd

@@ -13,6 +13,7 @@ format:
 - [Pixtral](#sec-pixtral)
 - [Llava-1.5](#sec-llava-15)
 - [Mistral-Small-3.1](#sec-mistral-small-31)
+- [Magistral-Small-2509](#sec-magistral-small-2509)
 - [Voxtral](#sec-voxtral)
 - [Gemma-3](#sec-gemma-3)
 - [Gemma-3n](#sec-gemma-3n)
@@ -41,7 +42,6 @@ datasets:
   - path: HuggingFaceH4/llava-instruct-mix-vsft
     type: chat_template
     split: train[:1%]
-    field_messages: messages
 
 # (optional) if doing lora, only finetune the Language model,
 # leave the vision model and vision tower frozen
@@ -94,10 +94,22 @@ chat_template: llava
 
 ### Mistral-Small-3.1 {#sec-mistral-small-31}
 
+::: {.callout-tip}
+Please make sure to install vision lib via `pip install 'mistral-common[opencv]==1.8.5'`
+:::
+
 ```yaml
 base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
+```
 
-chat_template: mistral_v7_tekken
+### Magistral-Small-2509 {#sec-magistral-small-2509}
+
+::: {.callout-tip}
+Please make sure to install vision lib via `pip install 'mistral-common[opencv]==1.8.5'`
+:::
+
+```yaml
+base_model: mistralai/Magistral-Small-2509
 ```
 
 ### Voxtral {#sec-voxtral}

docs/optimizations.qmd

@@ -0,0 +1,133 @@
+---
+title: Optimizations Guide
+description: A guide to the performance and memory optimizations available in Axolotl.
+---
+
+Axolotl includes numerous optimizations to speed up training, reduce memory usage, and handle large models.
+
+This guide provides a high-level overview and directs you to the detailed documentation for each feature.
+
+## Speed Optimizations
+
+These optimizations focus on increasing training throughput and reducing total training time.
+
+### Sample Packing
+
+Improves GPU utilization by combining multiple short sequences into a single packed sequence for training. This requires enabling one of the [attention](#attention-implementations) implementations below.
+
+- **Config:** `sample_packing: true`
+- **Learn more:** [Sample Packing](multipack.qmd)
+
+### Attention Implementations
+
+Using an optimized attention implementation is critical for training speed.
+
+- **[Flash Attention 2](https://github.com/Dao-AILab/flash-attention)**: `flash_attention: true`. **(Recommended)** The industry standard for fast attention on modern GPUs. Requires Ampere or higher. For AMD, check [AMD Support](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#amd-rocm-support).
+- **[Flex Attention](https://pytorch.org/blog/flexattention/)**: `flex_attention: true`.
+- **[SDP Attention](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)**: `sdp_attention: true`. PyTorch's native implementation.
+- **[Xformers](https://github.com/facebookresearch/xformers)**: `xformers_attention: true`. Works with FP16.
+
+*Note: You should only enable one attention backend.*
+
+### LoRA Optimizations
+
+Leverages optimized kernels to accelerate LoRA training and reduce memory usage.
+
+- **Learn more:** [LoRA Optimizations Documentation](lora_optims.qmd)
+
+## Memory Optimizations
+
+These techniques help you fit larger models or use bigger batch sizes on your existing hardware.
+
+### Parameter Efficient Finetuning (LoRA & QLoRA)
+
+Drastically reduces memory by training a small set of "adapter" parameters instead of the full model. This is the most common and effective memory-saving technique.
+
+- Examples: Find configs with `lora` or `qlora` in the [examples directory](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/llama-3).
+- Config Reference: See `adapter`, `load_in_4bit`, and `load_in_8bit` in the [Configuration Reference](config-reference.qmd).
+
+### Gradient Checkpointing & Activation Offloading
+
+These techniques save VRAM by changing how activations are handled.
+
+- Gradient Checkpointing: re-computes activations during the backward pass, trading compute time for VRAM.
+- Activation Offloading: moves activations to CPU RAM or disk, trading I/O overhead for VRAM.
+- Learn more: [Gradient Checkpointing and Offloading Docs](gradient_checkpointing.qmd)
+
+### Cut Cross Entropy (CCE)
+
+Reduces VRAM usage by using an optimized cross-entropy loss calculation.
+
+- **Learn more:** [Custom Integrations - CCE](custom_integrations.qmd#cut-cross-entropy)
+
+### Liger Kernels
+
+Provides efficient Triton kernels to improve training speed and reduce memory usage.
+
+- **Learn more:** [Custom Integrations - Liger Kernels](custom_integrations.qmd#liger-kernels)
+
+## Long Context Models
+
+Techniques to train models on sequences longer than their original context window.
+
+### RoPE Scaling
+
+Extends a model's context window by interpolating its Rotary Position Embeddings.
+
+- **Config:** Pass the `rope_scaling` config under the `overrides_of_model_config: `. To learn how to set RoPE, check the respective model config.
+
+### Sequence Parallelism
+
+Splits long sequences across multiple GPUs, enabling training with sequence lengths that would not fit on a single device.
+
+- **Learn more:** [Sequence Parallelism Documentation](sequence_parallelism.qmd)
+
+### Artic Long Sequence Training (ALST)
+
+ALST is a recipe that combines several techniques to train long-context models efficiently. It typically involves:
+
+- TiledMLP to reduce memory usage in MLP layers.
+- Tiled Loss functions (like [CCE](#cut-cross-entropy-(cce) or [Liger](#liger-kernels)).
+- Activation Offloading to CPU.
+
+- Example: [ALST Example Configuration](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/alst)
+
+## Large Models (Distributed Training)
+
+To train models that don't fit on a single GPU, you'll need to use a distributed training strategy like FSDP or DeepSpeed. These frameworks shard the model weights, gradients, and optimizer states across multiple GPUs and nodes.
+
+- **Learn more:** [Multi-GPU Guide](multi-gpu.qmd)
+- **Learn more:** [Multi-Node Guide](multi-node.qmd)
+
+### N-D Parallelism (Beta)
+
+For advanced scaling, Axolotl allows you to compose different parallelism techniques (e.g., Data, Tensor, Sequence Parallelism). This is a powerful approach to train an extremely large model by overcoming multiple bottlenecks at once.
+
+- **Learn more:** [N-D Parallelism Guide](nd_parallelism.qmd)
+
+
+## Quantization
+
+Techniques to reduce the precision of model weights for memory savings.
+
+### 4-bit Training (QLoRA)
+
+The recommended approach for quantization-based training. It loads the base model in 4-bit using `bitsandbytes` and then trains QLoRA adapters. See [Adapter Finetuning](#adapter-finetuning-lora-qlora) for details.
+
+### FP8 Training
+
+Enables training with 8-bit floating point precision on supported hardware (e.g., NVIDIA Hopper series GPUs) for significant speed and memory gains.
+
+- **Example:** [Llama 3 FP8 FSDP Example](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/llama-3/3b-fp8-fsdp2.yaml)
+
+### Quantization Aware Training (QAT)
+
+Simulates quantization effects during training, helping the model adapt and potentially improving the final accuracy of the quantized model.
+
+- **Learn more:** [QAT Documentation](qat.qmd)
+
+### GPTQ
+
+Allows you to finetune LoRA adapters on top of a model that has already been quantized using the GPTQ method.
+
+- **Example:** [GPTQ LoRA Example](https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/llama-2/gptq-lora.yml)

docs/qat.qmd

@@ -30,6 +30,7 @@ qat:
 ```
 
 We support the following quantization schemas:
+
 - `Int4WeightOnly` (requires the `fbgemm-gpu` extra when installing Axolotl)
 - `Int8DynamicActivationInt4Weight`
 - `Float8DynamicActivationFloat8Weight`

examples/alst/README.md

@@ -7,3 +7,24 @@ techniques. It is a combination of:
 - Activation Offloading: Offload activations to CPU RAM to reduce memory usage
 
 For more information, you can check out the ALST paper [here](https://www.arxiv.org/abs/2506.13996).
+
+## Usage
+
+```yaml
+tiled_mlp: true
+
+# See Sequence Parallelism docs
+# https://docs.axolotl.ai/docs/sequence_parallelism.html
+context_parallel_size: int
+
+plugins:
+# See Cut Cross Entropy docs
+# https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+# or Liger Kernel docs
+# https://docs.axolotl.ai/docs/custom_integrations.html#liger-kernels
+  - axolotl.integrations.liger.LigerPlugin
+# ...
+
+```

examples/apertus/README.md

@@ -0,0 +1,110 @@
+# Finetune Swiss-AI's Apertus with Axolotl
+
+[Apertus](https://huggingface.co/collections/swiss-ai/apertus-llm-68b699e65415c231ace3b059) is a family of opensource models trained by Swiss-ai.
+
+This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
+
+## Getting started
+
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Apertus is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
+
+    Here is an example of how to install from main for pip:
+
+```bash
+# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
+git clone https://github.com/axolotl-ai-cloud/axolotl.git
+cd axolotl
+
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
+pip3 install --no-build-isolation -e '.[flash-attn]'
+
+# Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
+python scripts/cutcrossentropy_install.py | sh
+```
+
+2. (Optional, highly recommended) Install XIELU CUDA
+
+```bash
+## Recommended for reduced VRAM and faster speeds
+
+# Point to CUDA toolkit directory
+# For those using our Docker image, use the below path.
+export CUDA_HOME=/usr/local/cuda
+
+pip3 install git+https://github.com/nickjbrowning/XIELU@59d6031 --no-build-isolation --no-deps
+```
+
+For any installation errors, see [XIELU Installation Issues](#xielu-installation-issues)
+
+3. Run the finetuning example:
+
+```bash
+axolotl train examples/apertus/apertus-8b-qlora.yaml
+```
+
+This config uses about 8.7 GiB VRAM.
+
+Let us know how it goes. Happy finetuning! 🚀
+
+### Tips
+
+- For inference, the official Apertus team recommends `top_p=0.9` and `temperature=0.8`.
+- You can instead use full paremter fine-tuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
+- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
+
+### XIELU Installation Issues
+
+#### `ModuleNotFoundError: No module named 'torch'`
+
+Please check these one by one:
+- Running in correct environment
+- Env has PyTorch installed
+- CUDA toolkit is at `CUDA_HOME`
+
+If those didn't help, please try the below solutions:
+
+1. Pass env for CMAKE and try install again:
+
+    ```bash
+    Python_EXECUTABLE=$(which python) pip3 install git+https://github.com/nickjbrowning/XIELU@59d6031 --no-build-isolation --no-deps
+    ```
+
+2. Git clone the repo and manually hardcode python path:
+
+    ```bash
+    git clone https://github.com/nickjbrowning/XIELU
+    cd xielu
+    git checkout 59d6031
+
+    cd xielu
+    nano CMakeLists.txt  # or vi depending on your preference
+    ```
+
+    ```diff
+    execute_process(
+    -    COMMAND ${Python_EXECUTABLE} -c "import torch.utils; print(torch.utils.cmake_prefix_path)"
+    +    COMMAND /root/miniconda3/envs/py3.11/bin/python -c "import torch.utils; print(torch.utils.cmake_prefix_path)"
+        RESULT_VARIABLE TORCH_CMAKE_PATH_RESULT
+        OUTPUT_VARIABLE TORCH_CMAKE_PATH_OUTPUT
+        ERROR_VARIABLE TORCH_CMAKE_PATH_ERROR
+    )
+    ```
+
+    ```bash
+    pip3 install . --no-build-isolation --no-deps
+    ```
+
+## Optimization Guides
+
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+
+## Related Resources
+
+- [Apertus Tech Report](https://github.com/swiss-ai/apertus-tech-report/blob/main/Apertus_Tech_Report.pdf)
+- [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/apertus/apertus-8b-qlora.yaml

@@ -0,0 +1,64 @@
+base_model: swiss-ai/Apertus-8B-Instruct-2509
+
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_8bit: false
+load_in_4bit: true
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/lora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/arcee/README.md

@@ -19,6 +19,9 @@ cd axolotl
 
 pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
 pip3 install --no-build-isolation -e '.[flash-attn]'
+
+# Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
+python scripts/cutcrossentropy_install.py | sh
 ```
 
 2. Run the finetuning example:

examples/archived/deepcoder/deepcoder-14B-preview-lora.yml

@@ -9,10 +9,6 @@ strict: false
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
-    field_messages: messages
-    message_property_mappings:
-      role: role
-      content: content
 
 dataset_prepared_path:
 val_set_size: 0.05

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@c6a32c5\""
+    "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@147ea28\""
    ]
   },
   {

examples/deepcogito/cogito-v1-preview-llama-3B-lora.yml

@@ -9,10 +9,6 @@ strict: false
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
-    field_messages: messages
-    message_property_mappings:
-      role: role
-      content: content
 
 dataset_prepared_path:
 val_set_size: 0.05

examples/deepcogito/cogito-v1-preview-qwen-14B-lora.yml

@@ -9,10 +9,6 @@ strict: false
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
-    field_messages: messages
-    message_property_mappings:
-      role: role
-      content: content
 
 dataset_prepared_path:
 val_set_size: 0.05

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

@@ -18,7 +18,7 @@ datasets:
   - path: HuggingFaceH4/llava-instruct-mix-vsft
     type: chat_template
     split: train[:1%]
-    field_messages: messages
+
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./outputs/out

examples/gemma3n/README.md

@@ -23,7 +23,15 @@ pip3 install timm==1.0.17
 pip3 install librosa==0.11.0
 ```
 
-3. Run the finetuning example:
+3. Download sample dataset files
+
+```bash
+# for text + vision + audio only
+wget https://huggingface.co/datasets/Nanobit/text-vision-audio-2k-test/resolve/main/African_elephant.jpg
+wget https://huggingface.co/datasets/Nanobit/text-vision-audio-2k-test/resolve/main/En-us-African_elephant.oga
+```
+
+4. Run the finetuning example:
 
 ```bash
 # text only

examples/llama-3/instruct-lora-8b.yml

@@ -12,15 +12,6 @@ chat_template: llama3
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
-    field_messages: messages
-    message_property_mappings:
-      role: role
-      content: content
-    roles:
-      user:
-        - user
-      assistant:
-        - assistant
 
 dataset_prepared_path:
 val_set_size: 0.05

examples/llama-4/do-no-use-fa2/scout-vision-qlora-fsdp.yaml

@@ -46,7 +46,6 @@ datasets:
   - path: HuggingFaceH4/llava-instruct-mix-vsft
     type: chat_template
     split: train[:1%]
-    field_messages: messages
 
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0

examples/llama-4/scout-vision-qlora-fsdp2-flex.yaml

@@ -45,7 +45,6 @@ datasets:
   - path: HuggingFaceH4/llava-instruct-mix-vsft
     type: chat_template
     split: train[:1%]
-    field_messages: messages
 
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0

examples/magistral/README.md

@@ -1,10 +1,10 @@
 # Finetune Magistral Small with Axolotl
 
-Magistral Small is a 24B parameter opensource model from MistralAI found on HuggingFace at [2506](https://huggingface.co/mistralai/Magistral-Small-2506) and [2507](https://huggingface.co/mistralai/Magistral-Small-2507) (see [Thinking](#thinking)). This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
+Magistral Small is a 24B parameter opensource model from MistralAI found on HuggingFace at [2506](https://huggingface.co/mistralai/Magistral-Small-2506), [2507](https://huggingface.co/mistralai/Magistral-Small-2507) (see [Thinking](#thinking)), and [2509](https://huggingface.co/mistralai/Magistral-Small-2509) (see [Vision](#vision)). This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
 
 MistralAI has also released a proprietary medium-sized version called Magistral Medium.
 
-Thanks to the team at MistralAI for giving us early access to prepare for this release.
+Thanks to the team at MistralAI for giving us early access to prepare for these releases.
 
 ## Getting started
 
@@ -36,29 +36,17 @@ Let us know how it goes. Happy finetuning! 🚀
 
 ### Thinking
 
-MistralAI has released their [2507](https://huggingface.co/mistralai/Magistral-Small-2507) model with thinking capabilities. The model requires the multi-content dataset format with support for an extra `role: thinking` within system and assistant messages.
+MistralAI has released their [2507](https://huggingface.co/mistralai/Magistral-Small-2507) model with thinking capabilities, enabling Chain-of-Thought reasoning with explicit thinking steps.
 
-Example format:
+📚 **[See the Thinking fine-tuning guide →](./think/README.md)**
 
-```json
-{
-    "messages": [
-        {"role": "system", "content": [{ "type": "text", "text": "{SYSTEM_PROMPT}"}]},
-        {"role": "user", "content": [{ "type": "text", "text": "..."}]},
-        {"role": "assistant", "content": [{ "type": "thinking", "thinking": "..."}, { "type": "text", "text": "..." }]},
-    ],
-}
-```
+### Vision
 
-Example config: `./magistral-small-think-qlora.yaml`.
+MistralAI has released their [2509](https://huggingface.co/mistralai/Magistral-Small-2509) model with vision capabilities.
 
-The `thinking` section also supports an optional arg `closed: bool` (`True` default) which controls adding the closing `[/THINK]` tag.
+📚 **[See the Vision fine-tuning guide →](./vision/README.md)**
 
-Limitations:
-- You cannot mix `content: str` with `content: list[dict]` as the `dataset.load_dataset` may complain about different types for `content` key.
-- This mode does not work with custom `train_detail` and `training` at the moment.
-
-### TIPS
+### Tips
 
 - We recommend adding the same/similar SystemPrompt that the model is tuned for. You can find this within the repo's files titled `SYSTEM_PROMPT.txt`.
 - For inference, the official MistralAI team recommends `top_p: 0.95` and `temperature: 0.7` with `max_tokens: 40960`.
@@ -89,5 +77,5 @@ In addition, we do not support overriding tokens yet.
 
 ## Future Work
 
-- Add parity to Preference Tuning, RL, Multi-modal, etc.
+- Add parity to Preference Tuning, RL, etc.
 - Add parity to other tokenizer configs like overriding tokens.

examples/magistral/think/README.md

@@ -0,0 +1,73 @@
+# Magistral Small Thinking Fine-tuning
+
+This guide covers fine-tuning [Magistral Small 2507](https://huggingface.co/mistralai/Magistral-Small-2507) with thinking capabilities using Axolotl. The thinking model enables explicit Chain-of-Thought reasoning with separate thinking and response sections.
+
+## Prerequisites
+
+Before starting, ensure you have:
+- Installed Axolotl (see [main README](../README.md))
+
+## Getting Started
+
+Run the thinking model fine-tuning:
+
+```bash
+axolotl train magistral-small-think-qlora.yaml
+```
+
+This config uses about 19.1 GiB VRAM.
+
+### Tips
+
+- Dataset uses multi-content format with `type: thinking` support. See [Dataset Format](#dataset-format) below.
+- You cannot mix `content: str` and `content: list[dict]`, otherwise, dataset loading will fail. Keep it consistent.
+
+## Dataset Format
+
+The thinking model requires the multi-content dataset format with support for an extra `role: thinking` within system and assistant messages.
+
+Example format:
+
+```json
+{
+    "messages": [
+        {
+            "role": "system",
+            "content": [
+                { "type": "text", "text": "{SYSTEM_PROMPT}"}
+            ]
+        },
+        {
+            "role": "user",
+            "content": [
+                { "type": "text", "text": "Solve this step by step: What is 15% of 240?"}
+            ]
+        },
+        {
+            "role": "assistant",
+            "content": [
+                {
+                    "type": "thinking",
+                    "thinking": "I need to calculate 15% of 240. First, I'll convert 15% to decimal: 0.15. Then multiply: 0.15 × 240 = 36."
+                },
+                {
+                    "type": "text",
+                    "text": "To find 15% of 240, I'll multiply 240 by 0.15:\n\n240 × 0.15 = 36\n\nTherefore, 15% of 240 is 36."
+                }
+            ]
+        }
+    ]
+}
+```
+
+### Advanced Options
+
+The `thinking` section supports an optional `closed` parameter:
+
+```json
+{
+    "type": "thinking",
+    "thinking": "Internal reasoning here...",
+    "closed": true  // Default: true, controls adding the closing [/THINK] tag
+}
+```

examples/magistral/vision/README.md

@@ -0,0 +1,60 @@
+# Magistral Small Vision Fine-tuning
+
+This guide covers fine-tuning [Magistral Small 2509](https://huggingface.co/mistralai/Magistral-Small-2509) with vision capabilities using Axolotl.
+
+## Prerequisites
+
+Before starting, ensure you have:
+- Installed Axolotl from source (see [main README](../README.md#getting-started))
+
+## Getting started
+
+1. Install the required vision lib:
+    ```bash
+    pip install 'mistral-common[opencv]==1.8.5'
+    ```
+
+2. Download the example dataset image:
+   ```bash
+   wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
+   ```
+
+3. Run the fine-tuning:
+   ```bash
+   axolotl train magistral-small-vision-24B-qlora.yml
+   ```
+
+This config uses about 17GiB VRAM.
+
+WARNING: The loss and grad norm will be much higher than normal at first. We suspect this to be inherent to the model as of the moment. If anyone would like to submit a fix for this, we are happy to take a look.
+
+### Tips
+
+Key differences from text-only model:
+- `max_tokens: 131072` for inference
+- Multi-modal dataset format required
+- Sample packing not supported
+
+## Dataset Format
+
+The vision model requires multi-modal dataset format as documented [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
+
+One exception is that, passing `"image": PIL.Image` is not supported. MistralTokenizer only supports `path`, `url`, and `base64` for now.
+
+Example:
+```json
+{
+    "messages": [
+        {"role": "system", "content": [{ "type": "text", "text": "{SYSTEM_PROMPT}"}]},
+        {"role": "user", "content": [
+            { "type": "text", "text": "What's in this image?"},
+            {"type": "image", "path": "path/to/image.jpg" }
+        ]},
+        {"role": "assistant", "content": [{ "type": "text", "text": "..." }]},
+    ],
+}
+```
+
+## Limitations
+
+- Sample Packing is not supported for multi-modality training currently.

examples/magistral/vision/magistral-small-vision-24B-qlora.yml

@@ -0,0 +1,64 @@
+base_model: mistralai/Magistral-Small-2509
+processor_type: AutoProcessor
+
+# Enable to use mistral-common tokenizer
+tokenizer_use_mistral_common: true
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_4bit: true
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+# sample dataset below requires downloading image in advance
+# wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
+datasets:
+  - path: Nanobit/text-vision-2k-test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./outputs/out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/mistral/mistral-small/mistral-small-3.1-24B-lora.yml

@@ -1,6 +1,9 @@
 base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
 processor_type: AutoProcessor
 
+# Enable to use mistral-common tokenizer
+tokenizer_use_mistral_common: true
+
 load_in_8bit: true
 
 # these 3 lines are needed for now to handle vision chat templates w images
@@ -8,12 +11,12 @@ skip_prepare_dataset: true
 remove_unused_columns: false
 sample_packing: false
 
-chat_template: mistral_v7_tekken
+# sample dataset below requires downloading image in advance
+# wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
 datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
+  - path: Nanobit/text-vision-2k-test
     type: chat_template
-    split: train[:1%]
-    field_messages: messages
+
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./outputs/out
@@ -48,8 +51,7 @@ tf32: true
 
 gradient_checkpointing: true
 logging_steps: 1
-# flash_attention: false # PixtralVisionModel does not support Flash Attention 2.0 yet.
-sdp_attention: true
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/phi/lora-3.5.yaml

@@ -12,15 +12,6 @@ chat_template: phi_3
 datasets:
   - path: fozziethebeat/alpaca_messages_2k_test
     type: chat_template
-    field_messages: messages
-    message_property_mappings:
-      role: role
-      content: content
-    roles:
-      user:
-        - user
-      assistant:
-        - assistant
 
 dataset_prepared_path:
 val_set_size: 0.05

examples/pixtral/lora-12b.yml

@@ -45,8 +45,7 @@ tf32: true
 
 gradient_checkpointing: true
 logging_steps: 1
-# flash_attention:  # PixtralVisionModel does not support Flash Attention 2.0 yet
-sdp_attention: true
+flash_attention: true
 
 warmup_ratio: 0.1
 evals_per_epoch: 1

examples/qwen2-vl/lora-7b.yaml

@@ -11,7 +11,7 @@ datasets:
   - path: HuggingFaceH4/llava-instruct-mix-vsft
     type: chat_template
     split: train[:1%]
-    field_messages: messages
+
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
 output_dir: ./outputs/out

examples/qwen2_5-vl/lora-7b.yaml

@@ -11,7 +11,7 @@ datasets:
   - path: HuggingFaceH4/llava-instruct-mix-vsft
     type: chat_template
     split: train[:1%]
-    field_messages: messages
+
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.0
 output_dir: ./outputs/out

examples/qwen3-next/README.md

@@ -0,0 +1,64 @@
+# Finetune Qwen3-Next with Axolotl
+
+[Qwen3-Next](https://huggingface.co/collections/Qwen/qwen3-next-68c25fd6838e585db8eeea9d) represents the next-generation foundation models optimized for extreme context length and large-scale parameter efficiency. The series introduces architectural innovations including Hybrid Attention (Gated DeltaNet + Gated Attention), High-Sparsity MoE with 1:50 activation ratio, and Multi-Token Prediction for enhanced performance and inference acceleration.
+
+This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
+
+## Getting started
+
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Qwen3-Next is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
+
+    Here is an example of how to install from main for pip:
+
+```bash
+# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
+git clone https://github.com/axolotl-ai-cloud/axolotl.git
+cd axolotl
+
+pip3 install packaging==23.2 setuptools==75.8.0 wheel ninja
+pip3 install --no-build-isolation -e '.[flash-attn]'
+
+# Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
+python scripts/cutcrossentropy_install.py | sh
+```
+
+2. Install Qwen3-Next transformers commit
+```bash
+pip3 uninstall -y transformers && pip3 install "git+https://github.com/huggingface/transformers.git@b9282355bea846b54ed850a066901496b19da654"
+```
+
+3. Install FLA for improved performance
+```bash
+pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.3.2
+```
+
+4. Run the finetuning example:
+
+```bash
+axolotl train examples/qwen3-next/qwen3-next-80b-a3b-qlora.yaml
+```
+
+This config uses about 45.62 GiB VRAM.
+
+Let us know how it goes. Happy finetuning! 🚀
+
+### TIPS
+
+- For inference, you can experiment with `temperature: 0.7`, `top_p: 0.8`, `top_k: 20`, and `min_p: 0`.
+- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config. See [Multi-GPU](#optimization-guides) section below.
+- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
+
+## Optimization Guides
+
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+
+## Related Resources
+
+- [Qwen3-Next Blog](https://qwenlm.github.io/blog/qwen3_next/)
+- [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/qwen3-next/qwen3-next-80b-a3b-qlora.yaml

@@ -0,0 +1,68 @@
+base_model: Qwen/Qwen3-Next-80B-A3B-Instruct
+
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+plugins:
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+load_in_8bit: false
+load_in_4bit: true
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/lora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+
+lora_r: 16
+lora_alpha: 8
+lora_dropout: 0.05
+lora_target_modules:
+  - linear_attn.in_proj_ba
+  - linear_attn.in_proj_qkvz
+  - linear_attn.out_proj
+  - shared_expert.up_proj
+  - shared_expert.down_proj
+  - shared_expert.gate_proj
+  - shared_expert_gate
+  - mlp.gate
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: false
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/voxtral/README.md

@@ -27,7 +27,14 @@ pip3 install 'mistral_common[audio]==1.8.3'
 python scripts/cutcrossentropy_install.py | sh
 ```
 
-3. Run the finetuning example:
+3. Download sample dataset files
+
+```bash
+# for text + audio only
+wget https://huggingface.co/datasets/Nanobit/text-audio-2k-test/resolve/main/En-us-African_elephant.oga
+```
+
+4. Run the finetuning example:
 
 ```bash
 # text only

requirements.txt

@@ -70,4 +70,4 @@ schedulefree==1.4.1
 axolotl-contribs-lgpl==0.0.6
 axolotl-contribs-mit==0.0.5
 
-mistral-common==1.8.3
+mistral-common==1.8.5

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@c6a32c5"'
+    + f'{UV_PREFIX}pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@147ea28"'
 )

setup.py

@@ -124,7 +124,6 @@ extras_require = {
     "ring-flash-attn": [
         "flash-attn==2.8.3",
         "ring-flash-attn>=0.1.7",
-        "yunchang==0.6.0",
     ],
     "deepspeed": [
         "deepspeed==0.17.5",

src/axolotl/core/builders/rl.py

@@ -120,6 +120,11 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.use_wandb:
             training_args_kwargs["run_name"] = self.cfg.wandb_name
 
+        if self.cfg.max_prompt_len:
+            training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
+        else:
+            training_args_kwargs["max_prompt_length"] = self.cfg.sequence_len
+
         training_args_cls = None
         blocklist_args_kwargs = []
         if self.cfg.rl is RLType.SIMPO:
@@ -129,10 +134,16 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
+            # Handle when max_prompt_length == max_length from defaults
+            # CPOTrainer requires strictly less than
+            if (
+                training_args_kwargs["max_prompt_length"]
+                == training_args_kwargs["max_length"]
+            ):
+                training_args_kwargs["max_prompt_length"] -= 1
+
         elif self.cfg.rl is RLType.ORPO:
             training_args_cls = AxolotlORPOConfig
-            if self.cfg.max_prompt_len:
-                training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
         elif self.cfg.rl is RLType.KTO:
             training_args_cls = AxolotlKTOConfig
@@ -144,9 +155,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
                 self.cfg.kto_undesirable_weight or 1.0
             )
 
-            if self.cfg.max_prompt_len:
-                training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
-
         elif self.cfg.rl is RLType.GRPO:
             training_args_cls = GRPOStrategy.get_training_args_class()
             training_args_kwargs.update(GRPOStrategy.set_training_args_kwargs(self.cfg))

src/axolotl/core/datasets/transforms/chat_builder.py

@@ -8,7 +8,7 @@ from typing import Any, Mapping
 
 def chat_message_transform_builder(
     train_on_inputs=False,
-    conversations_field: str = "conversations",
+    conversations_field: str = "messages",
     message_field_role: str | list[str] | None = None,  # commonly "role"
     message_field_content: str | list[str] | None = None,  # commonly "content"
     message_field_training: str | list[str] | None = None,  # commonly "weight"
@@ -20,13 +20,13 @@ def chat_message_transform_builder(
             If True, the transform will train on the inputs. If False, the transform will train on the targets.
             Defaults to False.
         conversations_field (str, optional):
-            The field name of the conversations. Defaults to "conversations".
+            The field name of the conversations. Defaults to "messages".
         message_field_role (str | list[str], optional):
-            The field name of the role. Defaults to "role".
+            The field name of the role.
         message_field_content (str | list[str], optional):
-            The field name of the message content. Defaults to "content".
+            The field name of the message content.
         message_field_training (str | list[str], optional):
-            The field name of the train/weight. Defaults to "weight".
+            The field name of the train/weight.
 
     Returns:
         Callable:

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

@@ -27,7 +27,6 @@ class DPOStrategy:
             training_args_kwargs["label_smoothing"] = cfg.dpo_label_smoothing
         training_args_kwargs["max_completion_length"] = None
         training_args_kwargs["max_length"] = cfg.sequence_len
-        training_args_kwargs["max_prompt_length"] = cfg.sequence_len
         training_args_kwargs["generate_during_eval"] = cfg.dpo_generate_during_eval
         if cfg.dpo_use_weighting is not None:
             training_args_kwargs["use_weighting"] = cfg.dpo_use_weighting

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@c6a32c5"
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@147ea28"
 ```
 
 ## Usage
@@ -31,6 +31,7 @@ plugins:
 
 ## Supported Models
 
+- apertus
 - arcee
 - cohere
 - cohere2
@@ -44,9 +45,13 @@ plugins:
 - glm
 - glm4
 - glm4_moe
+- glm4v
+- glm4v_moe
 - gpt_oss
 - granite
 - granitemoe
+- granitemoeshared
+- granitemoehybrid
 - hunyuan_v1_dense
 - hunyuan_v1_moe
 - llama
@@ -65,6 +70,9 @@ plugins:
 - qwen2_5_vl
 - qwen3
 - qwen3_moe
+- qwen3_vl
+- qwen3_vl_moe
+- qwen3_next
 - smollm3
 - seed_oss
 - voxtral

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@c6a32c5"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@147ea28"`'
 )
 
 

src/axolotl/loaders/patch_manager.py

@@ -68,11 +68,12 @@ class PatchManager:
         self._apply_self_attention_lora_patch()
         self._apply_fsdp2_bnb_patches()
         self._apply_patch_deepspeed_zero3()
+        self._apply_voxtral_patches()
+        self._apply_apertus_patches()
 
     def apply_post_plugin_pre_model_load_patches(self):
         """Apply post plugin-pre_model_load load patches based on config."""
         self._apply_tiled_mlp(self.cfg.model_config_type)
-        self._apply_voxtral_patches()
 
     def _apply_transformers_patches(self):
         from axolotl.monkeypatch.transformers.trainer_loss_calc import (
@@ -83,6 +84,13 @@ class PatchManager:
         patch_evaluation_loop()
         patch_maybe_log_save_evaluate()
 
+        if self.cfg.context_parallel_size > 1:
+            from axolotl.monkeypatch.transformers.trainer_context_parallel import (
+                patch_prepare_context_parallel_inputs,
+            )
+
+            patch_prepare_context_parallel_inputs()
+
     def apply_post_model_load_patches(self, model: PreTrainedModel):
         """Apply patches that require the model instance."""
         self._apply_llama_flash_attn_patches(model)
@@ -168,6 +176,20 @@ class PatchManager:
 
             patch_llama4_linearized_modeling()
 
+        if self.cfg.model_config_type == "qwen3_next" and self.cfg.sample_packing:
+            from axolotl.monkeypatch.models.qwen3_next.modeling import (
+                patch_qwen3_next_modeling_packing,
+            )
+
+            patch_qwen3_next_modeling_packing()
+
+        if self.cfg.model_config_type == "mistral3" and self.cfg.processor_type:
+            from axolotl.monkeypatch.models.mistral3.mistral_common_tokenizer import (
+                apply_mistral_tokenizer_image_patch,
+            )
+
+            apply_mistral_tokenizer_image_patch()
+
     def _apply_fp8_patches(self):
         """Apply patches for FP8 support."""
         if self.cfg.fp8:
@@ -334,6 +356,13 @@ class PatchManager:
 
             replace_stablelm_attn_with_flash_attn(self.cfg.base_model)
 
+        if self.model_config.model_type in ("mistral3", "llava"):
+            from axolotl.monkeypatch.models.pixtral.modeling_flash_attention_utils import (
+                apply_patch_is_packed_sequence,
+            )
+
+            apply_patch_is_packed_sequence()
+
     def _patch_loss_llama(self):
         """Patch loss functions and other optimizations for LLaMA models."""
         if not self.cfg.is_llama_derived_model:
@@ -479,3 +508,12 @@ class PatchManager:
                 apply_deepspeed_patches()
         except ImportError as e:
             LOG.warning(f"DeepSpeed patches not applied: {e}")
+
+    def _apply_apertus_patches(self):
+        """Apply patches for Apertus model."""
+        if self.cfg.model_config_type == "apertus":
+            from axolotl.monkeypatch.models.apertus.activation import (
+                patch_apertus_xielu_activation,
+            )
+
+            patch_apertus_xielu_activation()

src/axolotl/loaders/processor.py

@@ -21,6 +21,13 @@ def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
     if cfg.processor_type:
         processor_cls = getattr(transformers, cfg.processor_type)
 
+    if cfg.tokenizer_use_mistral_common:
+        from axolotl.utils.mistral import Mistral3Processor
+
+        return Mistral3Processor(
+            tokenizer=tokenizer,
+        )
+
     processor = processor_cls.from_pretrained(
         cfg.processor_config,
         trust_remote_code=cfg.trust_remote_code or False,

src/axolotl/loaders/tokenizer.py

@@ -124,13 +124,8 @@ def load_tokenizer(cfg: DictDefault) -> PreTrainedTokenizer:
 
     def _load_mistral_common_tokenizer(cfg: DictDefault):
         """Load mistral-common tokenizer"""
-        from transformers import tokenization_mistral_common
-
         from axolotl.utils.mistral import HFMistralTokenizer
 
-        # patch
-        tokenization_mistral_common.MistralCommonTokenizer = HFMistralTokenizer
-
         # Load the HF-compatible wrapper around MistralTokenizer
         tokenizer = HFMistralTokenizer.from_pretrained(cfg.tokenizer_config)
 

src/axolotl/monkeypatch/accelerate/fsdp2.py

@@ -368,6 +368,7 @@ def fsdp2_prepare_model(accelerator, model: torch.nn.Module) -> torch.nn.Module:
         # removing the call above leads to extra memory usage as explained in the comment above
         if hasattr(model, "tie_weights"):
             model.tie_weights()
+    model = model.to(torch.float32)
     return model
 
 

src/axolotl/monkeypatch/models/apertus/activation.py

@@ -0,0 +1,52 @@
+"""Monkeypatch for Apertus to dtype mismatch in XIELU act"""
+
+from torch import Tensor
+
+
+def patch_apertus_xielu_activation():
+    try:
+        from transformers.activations import XIELUActivation
+    except ImportError as err:
+        raise ImportError(
+            "Cannot import XIELUActivation. "
+            "Please make sure to update your transformers version >= 4.56.1."
+        ) from err
+
+    from transformers.activations import logger
+
+    # Store the original method
+    old_fn = XIELUActivation._xielu_cuda
+
+    def _xielu_cuda_fixed(self, x: Tensor) -> Tensor:
+        """Firewall function to prevent torch.compile from seeing .item() calls"""
+        original_shape = x.shape
+        # CUDA kernel expects 3D tensors, reshape if needed
+        while x.dim() < 3:
+            x = x.unsqueeze(0)
+        if x.dim() > 3:
+            x = x.view(-1, 1, x.size(-1))
+        if original_shape != x.shape:
+            logger.warning_once(
+                "Warning: xIELU input tensor expects 3 dimensions but got (shape: %s). Reshaping to (shape: %s).",
+                original_shape,
+                x.shape,
+            )
+        result = self._xielu_cuda_obj.forward(
+            x,
+            self.alpha_p.to(x.dtype),
+            self.alpha_n.to(x.dtype),
+            # Temporary until xIELU CUDA fully implemented -> self.{beta,eps}.item()
+            self._beta_scalar,
+            self._eps_scalar,
+            self.with_vector_loads,
+        )
+        return result.view(original_shape)
+
+    # Apply the patch
+    XIELUActivation._xielu_cuda = _xielu_cuda_fixed
+
+    def unpatch():
+        """Restore the original method"""
+        XIELUActivation._xielu_cuda = old_fn
+
+    return unpatch

src/axolotl/monkeypatch/models/mistral3/mistral_common_tokenizer.py

@@ -0,0 +1,85 @@
+"""
+Monkeypatch to fix inefficient tensor conversion in MistralCommonTokenizer.apply_chat_template
+"""
+
+import importlib
+import inspect
+
+from axolotl.monkeypatch.utils import detab_code
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
+
+def apply_mistral_tokenizer_image_patch():
+    """Apply patch to MistralCommonTokenizer.apply_chat_template to fix image tensor conversion."""
+    from transformers.tokenization_mistral_common import MistralCommonTokenizer
+
+    # Get original source
+    original_source = inspect.getsource(MistralCommonTokenizer.apply_chat_template)
+    original_source, _ = detab_code(original_source)
+
+    # Define the replacement
+    original_tensor_conversion = (
+        "                    pixel_values = torch.tensor(images)"
+    )
+
+    patched_tensor_conversion = """                    if isinstance(images, list) and len(images) > 0 and isinstance(images[0], np.ndarray):
+                        pixel_values = torch.tensor(np.array(images))
+                    else:
+                        pixel_values = torch.tensor(images)"""
+
+    # Apply the replacement
+    if original_tensor_conversion in original_source:
+        patched_source = original_source.replace(
+            original_tensor_conversion, patched_tensor_conversion
+        )
+        patched_source = patched_source.replace(
+            "def apply_chat_template(",
+            "def patched_apply_chat_template(",
+            1,
+        )
+
+        # Load necessary imports from the module
+        module_name = MistralCommonTokenizer.__module__
+        module = importlib.import_module(module_name)
+
+        # Detect what needs to be imported
+        items_to_import = []
+        for item in dir(module):
+            if item in patched_source and not item.startswith("_"):
+                items_to_import.append(item)
+
+        # Execute imports in global scope
+        if items_to_import:
+            exec(  # nosec B102
+                f"from {module_name} import ({', '.join(items_to_import)})",
+                globals(),
+            )
+
+        # Also need standard imports that might be used
+        exec("import numpy as np", globals())  # nosec B102
+        exec("import torch", globals())  # nosec B102
+        exec("from typing import Union, Optional, List, Dict, Any, Callable", globals())  # nosec B102
+        exec("from pathlib import Path", globals())  # nosec B102
+
+        # Import other dependencies that might be needed
+        try:
+            exec("from transformers.utils import is_torch_available", globals())  # nosec B102
+            exec(
+                "from transformers.tokenization_utils_base import BatchEncoding, PaddingStrategy, TensorType",
+                globals(),
+            )  # nosec B102
+            exec("from transformers.utils import logging", globals())  # nosec B102
+            exec("logger = logging.get_logger(__name__)", globals())  # nosec B102
+        except ImportError as e:
+            LOG.warning(f"Could not import some dependencies: {e}")
+
+        # Execute the patched source
+        exec(patched_source, globals())  # nosec B102
+
+        # Replace the method
+        MistralCommonTokenizer.apply_chat_template = patched_apply_chat_template
+        LOG.info("Successfully applied MistralCommonTokenizer tensor conversion patch")
+    else:
+        LOG.warning("Could not find target code for MistralCommonTokenizer patching")

src/axolotl/monkeypatch/models/pixtral/modeling_flash_attention_utils.py

@@ -0,0 +1,42 @@
+"""Monkeypatch for FA utils to accept 1D position_ids from Pixtral's position_ids_in_meshgrid"""
+
+import torch
+
+
+def apply_patch_is_packed_sequence():
+    """Apply patch to FA utils to accept 1D position_ids from Pixtral's position_ids_in_meshgrid"""
+    from transformers import modeling_flash_attention_utils
+
+    def fixed_is_packed_sequence(position_ids, batch_size):
+        """
+        Check the position ids whether packed sequences are indicated or not
+            1. Position ids exist
+            2. Flattened sequences only are supported
+            3. Compile-friendly `not (torch.diff(position_ids, dim=-1) >= 0).all()`, i.e. we have multiple increasing sequences
+        """
+        if position_ids is None:
+            return False
+
+        if position_ids.ndim == 1:
+            position_ids = position_ids.unsqueeze(0)  # [N] -> [1, N]
+
+        increasing_position_sequences = (
+            torch.arange(position_ids.shape[1], device=position_ids.device)
+            + position_ids.min()
+        )
+        return (
+            batch_size == 1
+            and (increasing_position_sequences - position_ids).abs().sum().bool().item()
+        )
+
+    # Store original method
+    old_fn = modeling_flash_attention_utils._is_packed_sequence
+
+    # Apply the patch
+    modeling_flash_attention_utils._is_packed_sequence = fixed_is_packed_sequence
+
+    def unpatch():
+        """Restore the original method"""
+        modeling_flash_attention_utils._is_packed_sequence = old_fn
+
+    return unpatch

src/axolotl/monkeypatch/models/qwen3_next/__init__.py

@@ -0,0 +1 @@
+"""Qwen3_Next model monkeypatches."""

src/axolotl/monkeypatch/models/qwen3_next/modeling.py

@@ -0,0 +1,317 @@
+"""Monkeypatch for Qwen3_Next model to pass position_ids to linear attention."""
+
+from typing import Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
+
+def get_cu_seqlens(position_ids):
+    """
+    Adapted from transformers.modeling_flash_attention_utils.prepare_fa_kwargs_from_position_ids.
+
+    https://github.com/huggingface/transformers/blob/0f1b128d3359a26bd18be99c26d7f04fb3cba914/src/transformers/modeling_flash_attention_utils.py#L316
+    """
+    tensor_kwargs = {"dtype": torch.int32, "device": position_ids.device}
+
+    position_ids = position_ids.view(-1)
+    indices_q = (position_ids == 0).nonzero().view(-1)
+
+    cu_seq_lens_q = torch.cat(
+        (
+            indices_q.to(**tensor_kwargs),
+            torch.tensor(position_ids.size(), **tensor_kwargs),
+        )
+    )
+
+    return cu_seq_lens_q
+
+
+def patch_qwen3_next_decoder_layer():
+    """Patch Qwen3NextDecoderLayer to pass position_ids to linear attention."""
+    try:
+        from transformers.models.qwen3_next.modeling_qwen3_next import (
+            Qwen3NextDecoderLayer,
+        )
+    except ImportError:
+        LOG.warning("Qwen3Next model not found, skipping patch")
+        return
+
+    # Store original forward method
+    original_decoder_forward = Qwen3NextDecoderLayer.forward
+
+    def patched_decoder_forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[torch.Tensor]] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Token Mixer
+        if self.layer_type == "linear_attention":
+            hidden_states = self.linear_attn(
+                hidden_states=hidden_states,
+                cache_params=past_key_values,
+                cache_position=cache_position,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+            )
+        elif self.layer_type == "full_attention":
+            # Self Attention
+            hidden_states, _ = self.self_attn(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        # For the MoE layers, we need to unpack
+        if isinstance(hidden_states, Tuple):
+            hidden_states, _ = hidden_states
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+    # Apply the patches
+    Qwen3NextDecoderLayer.forward = patched_decoder_forward
+
+    def unpatch():
+        """Restore the original forward method"""
+        Qwen3NextDecoderLayer.forward = original_decoder_forward
+
+    return unpatch
+
+
+def patch_qwen3_next_gateddelta_layer():
+    """Patch Qwen3NextGatedDeltaNet to parse cu_seqlens and pass to chunk_gated_delta_rule"""
+    try:
+        from transformers.models.qwen3_next.modeling_qwen3_next import (
+            Qwen3NextDynamicCache,
+            Qwen3NextGatedDeltaNet,
+            apply_mask_to_padding_states,
+        )
+    except ImportError:
+        LOG.warning("Qwen3Next model not found, skipping patch")
+        return
+
+    # Store original forward method
+    original_gated_delta_net_forward = Qwen3NextGatedDeltaNet.forward
+
+    def patched_gated_delta_net_forward(
+        self,
+        hidden_states: torch.Tensor,
+        cache_params: Optional[Qwen3NextDynamicCache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+    ):
+        hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
+
+        # Set up dimensions for reshapes later
+        batch_size, seq_len, _ = hidden_states.shape
+
+        use_precomputed_states = (
+            cache_params is not None
+            and cache_params.has_previous_state
+            and seq_len == 1
+            and cache_position is not None
+        )
+
+        # getting projected states from cache if it exists
+        if cache_params is not None:
+            conv_state = cache_params.conv_states[self.layer_idx]
+            recurrent_state = cache_params.recurrent_states[self.layer_idx]
+
+        projected_states_qkvz = self.in_proj_qkvz(hidden_states)
+        projected_states_ba = self.in_proj_ba(hidden_states)
+        query, key, value, z, b, a = self.fix_query_key_value_ordering(
+            projected_states_qkvz, projected_states_ba
+        )
+        query, key, value = (
+            x.reshape(x.shape[0], x.shape[1], -1) for x in (query, key, value)
+        )
+
+        mixed_qkv = torch.cat((query, key, value), dim=-1)
+        mixed_qkv = mixed_qkv.transpose(1, 2)
+
+        if use_precomputed_states:
+            # 2. Convolution sequence transformation
+            # NOTE: the conv state is updated in `causal_conv1d_update`
+            mixed_qkv = self.causal_conv1d_update(
+                mixed_qkv,
+                conv_state,
+                self.conv1d.weight.squeeze(1),
+                self.conv1d.bias,
+                self.activation,
+            )
+        else:
+            if cache_params is not None:
+                conv_state = F.pad(
+                    mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0)
+                )
+                cache_params.conv_states[self.layer_idx] = conv_state
+            if self.causal_conv1d_fn is not None:
+                mixed_qkv = self.causal_conv1d_fn(
+                    x=mixed_qkv,
+                    weight=self.conv1d.weight.squeeze(1),
+                    bias=self.conv1d.bias,
+                    activation=self.activation,
+                    seq_idx=None,
+                )
+            else:
+                mixed_qkv = F.silu(self.conv1d(mixed_qkv)[:, :, :seq_len])
+
+        mixed_qkv = mixed_qkv.transpose(1, 2)
+        query, key, value = torch.split(
+            mixed_qkv,
+            [
+                self.key_dim,
+                self.key_dim,
+                self.value_dim,
+            ],
+            dim=-1,
+        )
+        query = query.reshape(query.shape[0], query.shape[1], -1, self.head_k_dim)
+        key = key.reshape(key.shape[0], key.shape[1], -1, self.head_k_dim)
+        value = value.reshape(value.shape[0], value.shape[1], -1, self.head_v_dim)
+
+        beta = b.sigmoid()
+        # If the model is loaded in fp16, without the .float() here, A might be -inf
+        g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias)
+        if self.num_v_heads // self.num_k_heads > 1:
+            query = query.repeat_interleave(self.num_v_heads // self.num_k_heads, dim=2)
+            key = key.repeat_interleave(self.num_v_heads // self.num_k_heads, dim=2)
+
+        if not use_precomputed_states:
+            cu_seqlens = get_cu_seqlens(position_ids=position_ids)
+            core_attn_out, last_recurrent_state = self.chunk_gated_delta_rule(
+                query,
+                key,
+                value,
+                g=g,
+                beta=beta,
+                initial_state=None,
+                output_final_state=cache_params is not None,
+                use_qk_l2norm_in_kernel=True,
+                cu_seqlens=cu_seqlens,
+            )
+
+        else:
+            core_attn_out, last_recurrent_state = self.recurrent_gated_delta_rule(
+                query,
+                key,
+                value,
+                g=g,
+                beta=beta,
+                initial_state=recurrent_state,
+                output_final_state=cache_params is not None,
+                use_qk_l2norm_in_kernel=True,
+            )
+
+        # Update cache
+        if cache_params is not None:
+            cache_params.recurrent_states[self.layer_idx] = last_recurrent_state
+
+        z_shape_og = z.shape
+        # reshape input data into 2D tensor
+        core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
+        z = z.reshape(-1, z.shape[-1])
+        core_attn_out = self.norm(core_attn_out, z)
+        core_attn_out = core_attn_out.reshape(z_shape_og)
+        core_attn_out = core_attn_out.reshape(
+            core_attn_out.shape[0], core_attn_out.shape[1], -1
+        )
+
+        output = self.out_proj(core_attn_out)
+        return output
+
+    # Apply the patches
+    Qwen3NextGatedDeltaNet.forward = patched_gated_delta_net_forward
+
+    def unpatch():
+        """Restore the original forward method"""
+        Qwen3NextGatedDeltaNet.forward = original_gated_delta_net_forward
+
+    return unpatch
+
+
+def patch_qwen3_next_imports():
+    """Patch Qwen3Next imports to use try/except instead of is_flash_linear_attention_available."""
+    try:
+        import transformers.models.qwen3_next.modeling_qwen3_next as qwen3_modeling
+    except ImportError:
+        LOG.warning("Qwen3Next model not found, skipping import patch")
+        return
+
+    # Save original values for unpatch
+    original_FusedRMSNormGated = getattr(qwen3_modeling, "FusedRMSNormGated", None)
+    original_chunk_gated_delta_rule = getattr(
+        qwen3_modeling, "chunk_gated_delta_rule", None
+    )
+    original_fused_recurrent_gated_delta_rule = getattr(
+        qwen3_modeling, "fused_recurrent_gated_delta_rule", None
+    )
+    original_is_fast_path_available = getattr(
+        qwen3_modeling, "is_fast_path_available", False
+    )
+
+    try:
+        from fla.modules import FusedRMSNormGated
+        from fla.ops.gated_delta_rule import (
+            chunk_gated_delta_rule,
+            fused_recurrent_gated_delta_rule,
+        )
+
+        qwen3_modeling.FusedRMSNormGated = FusedRMSNormGated
+        qwen3_modeling.chunk_gated_delta_rule = chunk_gated_delta_rule
+        qwen3_modeling.fused_recurrent_gated_delta_rule = (
+            fused_recurrent_gated_delta_rule
+        )
+
+        # Force is_fast_path_available to be True
+        # fla has triton kernels for causal_conv1d
+        qwen3_modeling.is_fast_path_available = True
+    except ImportError:
+        qwen3_modeling.chunk_gated_delta_rule = None
+        qwen3_modeling.fused_recurrent_gated_delta_rule = None
+        qwen3_modeling.FusedRMSNormGated = None
+
+    def unpatch():
+        """Restore the original import values"""
+        qwen3_modeling.FusedRMSNormGated = original_FusedRMSNormGated
+        qwen3_modeling.chunk_gated_delta_rule = original_chunk_gated_delta_rule
+        qwen3_modeling.fused_recurrent_gated_delta_rule = (
+            original_fused_recurrent_gated_delta_rule
+        )
+        qwen3_modeling.is_fast_path_available = original_is_fast_path_available
+
+    return unpatch
+
+
+def patch_qwen3_next_modeling_packing():
+    """Apply all Qwen3Next model patches."""
+    patch_qwen3_next_imports()
+    patch_qwen3_next_decoder_layer()
+    patch_qwen3_next_gateddelta_layer()
+
+    LOG.info("Applied Qwen3Next patch for packing")

src/axolotl/monkeypatch/multipack.py

@@ -11,6 +11,7 @@ from axolotl.monkeypatch.mixtral import patch_mixtral_moe_forward_zero3
 from axolotl.monkeypatch.utils import get_unpad_data
 
 SUPPORTED_MULTIPACK_MODEL_TYPES = [
+    "apertus",
     "mllama_text_model",
     "llama",
     "llama4",
@@ -20,6 +21,7 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "qwen2_moe",
     "qwen3",
     "qwen3_moe",
+    "qwen3_next",
     "falcon",
     "phi",
     "phi3",

src/axolotl/monkeypatch/transformers/trainer_context_parallel.py

@@ -0,0 +1,68 @@
+"""Monkey patch to allow context parallelism with FlashAttention in HF Trainer."""
+
+from __future__ import annotations
+
+import importlib
+import inspect
+
+from transformers import Trainer
+
+from axolotl.monkeypatch.utils import detab_code
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
+GUARD_PATTERN = 'if model.config._attn_implementation != "sdpa":'
+PATCHED_GUARD = (
+    'if model.config._attn_implementation not in ("sdpa", "flash_attention_2"):'
+)
+
+
+def patch_prepare_context_parallel_inputs() -> None:
+    """Relax the SDPA-only guard when running context parallelism with FlashAttention."""
+    if getattr(Trainer, "_axolotl_prepare_context_parallel_inputs_patched", False):
+        LOG.debug("Trainer._prepare_context_parallel_inputs already patched")
+        return
+
+    try:
+        original_source = inspect.getsource(Trainer._prepare_context_parallel_inputs)
+    except OSError as exc:  # pragma: no cover - occurs when source is unavailable
+        LOG.warning("Unable to patch Trainer._prepare_context_parallel_inputs: %s", exc)
+        return
+
+    if GUARD_PATTERN not in original_source:
+        LOG.warning(
+            "Expected guard not found in Trainer._prepare_context_parallel_inputs; \n"
+            "skipping FlashAttention context parallelism patch"
+        )
+        return
+
+    patched_source = original_source.replace(GUARD_PATTERN, PATCHED_GUARD)
+    patched_source, _ = detab_code(patched_source)
+    patched_source = patched_source.replace(
+        "def _prepare_context_parallel_inputs(",
+        "def axolotl_prepare_context_parallel_inputs(",
+        1,
+    )
+
+    module_name = Trainer.__module__
+    module = importlib.import_module(module_name)
+
+    # import symbols referenced in the method so exec can succeed
+    items_to_import = []
+    for item in dir(module):
+        if item in patched_source:
+            items_to_import.append(item)
+
+    exec(f"from {module_name} import ({', '.join(items_to_import)})", globals())
+    exec(patched_source, globals())
+
+    Trainer._original_prepare_context_parallel_inputs = (
+        Trainer._prepare_context_parallel_inputs
+    )
+    Trainer._prepare_context_parallel_inputs = axolotl_prepare_context_parallel_inputs
+    Trainer._axolotl_prepare_context_parallel_inputs_source = patched_source
+    Trainer._axolotl_prepare_context_parallel_inputs_patched = True
+    LOG.debug(
+        "Patched Trainer._prepare_context_parallel_inputs for FlashAttention + CP"
+    )

src/axolotl/processing_strategies.py

@@ -11,6 +11,7 @@ from transformers.image_utils import load_image
 
 from axolotl.utils.dict import remove_none_values
 from axolotl.utils.logging import get_logger
+from axolotl.utils.mistral.mistral3_processor import Mistral3Processor
 
 LOG = get_logger(__name__)
 
@@ -421,6 +422,36 @@ class SmolVLM2ProcessingStrategy(ProcessingStrategy):
         ]
 
 
+class Mistral3ProcessingStrategy(ProcessingStrategy):
+    """Processing Strategy class for Mistral3"""
+
+    def __init__(
+        self,
+        processor: Mistral3Processor,
+        chat_template: Optional[str] = None,
+        image_size: int | tuple[int, int] | None = None,
+        image_resize_algorithm: Resampling | None = None,
+    ):
+        super().__init__(processor, chat_template, image_size, image_resize_algorithm)
+        special_ids = (
+            processor.tokenizer.tokenizer.instruct_tokenizer.image_encoder.special_ids
+        )
+
+        self.image_token = special_ids.img
+        self.image_break_token = special_ids.img_break
+        self.image_end_token = special_ids.img_end
+
+    def process_labels(self, input_ids):
+        labels = input_ids.clone()
+
+        labels[labels == self.processor.tokenizer.pad_token_id] = -100
+        labels[labels == self.image_token] = -100
+        labels[labels == self.image_break_token] = -100
+        labels[labels == self.image_end_token] = -100
+
+        return labels
+
+
 def get_processing_strategy(
     processor: ProcessorMixin,
     chat_template,
@@ -463,6 +494,11 @@ def get_processing_strategy(
             **processing_kwargs,
         )
 
+    if isinstance(processor, Mistral3Processor):
+        return Mistral3ProcessingStrategy(
+            **processing_kwargs,
+        )
+
     # llama3_2_vision, llama4, llava
     # mistral_v7_tekken, pixtral, lfm2vl
     return ProcessingStrategy(

src/axolotl/prompt_strategies/chat_template.py

@@ -2,6 +2,7 @@
 HF Chat Templates prompt strategy
 """
 
+import json
 from collections import defaultdict
 from typing import TYPE_CHECKING, Any, Dict, List, Set, Union
 
@@ -794,6 +795,22 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
                 if val is not None:
                     transformed_message[key] = val
 
+        if "tool_calls" in transformed_message and transformed_message["tool_calls"]:
+            for tool_call in transformed_message["tool_calls"]:
+                if "function" in tool_call and "arguments" in tool_call["function"]:
+                    args = tool_call["function"]["arguments"]
+                    if isinstance(args, str):
+                        try:
+                            tool_call["function"]["arguments"] = json.loads(args)
+                        except json.JSONDecodeError as e:
+                            LOG.error(
+                                f"Error parsing tool_calls arguments as JSON. "
+                                f"Function: {tool_call.get('function', {}).get('name', 'unknown')}, "
+                                f"Arguments string: {args!r}, "
+                                f"Error: {e}"
+                            )
+                            raise
+
         return transformed_message
 
     def _get_images(self, prompt):

src/axolotl/utils/mistral/__init__.py

@@ -1,5 +1,6 @@
 """Init for `axolotl.utils.mistral` module."""
 
+from axolotl.utils.mistral.mistral3_processor import Mistral3Processor
 from axolotl.utils.mistral.mistral_tokenizer import HFMistralTokenizer
 
-__all__ = ["HFMistralTokenizer"]
+__all__ = ["HFMistralTokenizer", "Mistral3Processor"]

src/axolotl/utils/mistral/mistral3_processor.py

@@ -0,0 +1,169 @@
+"""Processor for Mistral3 multimodal models with image support"""
+
+from typing import Any, Dict, Optional, Union
+
+import torch
+from transformers import ProcessorMixin
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.processing_utils import ProcessingKwargs
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+
+from axolotl.utils.mistral.mistral_tokenizer import HFMistralTokenizer
+
+
+class Mistral3ProcessorKwargs(ProcessingKwargs):
+    _defaults: Dict[str, Dict[str, Any]] = {
+        "text_kwargs": {
+            "padding": True,
+        },
+        "common_kwargs": {
+            "return_tensors": "pt",
+            "return_dict": True,
+            "tokenize": True,
+        },
+    }
+
+
+class Mistral3Processor(ProcessorMixin):
+    """
+    Processor for Mistral3 multimodal models that handles text and images.
+    Wraps HFMistralTokenizer and adds image processing capabilities.
+    """
+
+    attributes = ["tokenizer"]
+    tokenizer_class = "HFMistralTokenizer"
+
+    def __init__(self, tokenizer: HFMistralTokenizer):
+        # Don't call super().__init__ to avoid the class validation issue
+        self.tokenizer = tokenizer
+
+    @property
+    def chat_template(self) -> None:
+        """Chat template is not supported. Dummy method to satisfy HuggingFace API."""
+        return None
+
+    @property
+    def audio_tokenizer(self) -> None:
+        """Audio tokenizer is not supported. Dummy method to satisfy HuggingFace API."""
+        return None
+
+    def _merge_kwargs(
+        self, processor_kwargs_class: Any, **kwargs: Any
+    ) -> Dict[str, Dict[str, Any]]:
+        """Merge kwargs with defaults similar to ProcessorMixin"""
+        defaults = processor_kwargs_class._defaults
+        output_kwargs: Dict[str, Dict[str, Any]] = {}
+
+        for kwarg_type, default_values in defaults.items():
+            output_kwargs[kwarg_type] = {**default_values}
+
+        # Update with provided kwargs
+        for key, value in kwargs.items():
+            # Try to match key to appropriate kwarg type
+            if key in ["padding", "truncation", "max_length"]:
+                output_kwargs.setdefault("text_kwargs", {}).update({key: value})
+            elif key in ["return_tensors", "return_dict", "tokenize"]:
+                output_kwargs.setdefault("common_kwargs", {}).update({key: value})
+            else:
+                # Add to text_kwargs by default
+                output_kwargs.setdefault("text_kwargs", {}).update({key: value})
+
+        return output_kwargs
+
+    def apply_chat_template(
+        self,
+        conversation: Union[list[dict[str, str]], list[list[dict[str, str]]]],
+        **kwargs: Any,
+    ) -> Union[BatchFeature, str, list[str]]:
+        """
+        Apply chat template with image support for Mistral3.
+
+        Similar to VoxtralProcessor, this method extracts images from the conversation,
+        calls the tokenizer's apply_chat_template, then adds pixel_values and image_sizes
+        to the result.
+        """
+        output_kwargs = self._merge_kwargs(Mistral3ProcessorKwargs, **kwargs)
+        text_kwargs = output_kwargs["text_kwargs"]
+        common_kwargs = output_kwargs["common_kwargs"]
+
+        return_tensors = common_kwargs.pop("return_tensors", "pt")
+        if return_tensors != "pt":
+            raise ValueError(
+                f"{self.__class__.__name__} only supports `return_tensors='pt'`."
+            )
+
+        return_dict = common_kwargs.pop("return_dict", False)
+        tokenize = common_kwargs.pop("tokenize", False)
+
+        # Determine if batched
+        if isinstance(conversation, (list, tuple)) and (
+            isinstance(conversation[0], (list, tuple))
+            or hasattr(conversation[0], "content")
+        ):
+            is_batched = True
+            conversations = conversation
+        else:
+            is_batched = False
+            conversations = [conversation]  # type: ignore
+
+        # Call tokenizer's apply_chat_template
+        tokenizer_kwargs = {**text_kwargs, **common_kwargs}
+        tokenizer_kwargs["return_tensors"] = return_tensors
+        tokenizer_kwargs["tokenize"] = tokenize
+        tokenizer_kwargs["return_dict"] = return_dict
+
+        encoded_instruct_inputs = self.tokenizer.apply_chat_template(
+            conversations,
+            **tokenizer_kwargs,
+        )
+
+        if tokenize:
+            if return_dict:
+                # The tokenizer already handles pixel_values, we just need to add image_sizes
+                if hasattr(encoded_instruct_inputs, "items"):
+                    data: Dict[str, Any] = dict(encoded_instruct_inputs)  # type: ignore
+                elif hasattr(encoded_instruct_inputs, "data"):
+                    data = encoded_instruct_inputs.data  # type: ignore
+                else:
+                    raise ValueError("Unknown data type")
+
+                if "pixel_values" in data:
+                    pixel_values = data["pixel_values"]
+
+                    # MistralTokenizer returns a Double, so we convert to fp32
+                    data["pixel_values"] = pixel_values.to(dtype=torch.float32)
+
+                    # Always batched: [B, C, H, W] -> image_sizes: [B, 2]
+                    # Since tensor is homogeneous, all images have same H, W
+                    batch_size = pixel_values.shape[0]
+                    image_sizes = torch.tensor([pixel_values.shape[-2:]] * batch_size)
+                    data["image_sizes"] = image_sizes
+
+                return BatchFeature(data=data, tensor_type=return_tensors)
+
+        if not is_batched:
+            return encoded_instruct_inputs[0]
+
+        return encoded_instruct_inputs
+
+    def __call__(
+        self,
+        text: Optional[
+            Union[
+                TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]
+            ]
+        ],
+        **kwargs: Any,
+    ) -> BatchFeature:
+        """
+        Forward text processing to the tokenizer.
+        This method does not support images - use apply_chat_template instead.
+        """
+        output_kwargs = self._merge_kwargs(Mistral3ProcessorKwargs, **kwargs)
+        text_kwargs = output_kwargs["text_kwargs"]
+        common_kwargs = output_kwargs["common_kwargs"]
+
+        out = self.tokenizer(text, **text_kwargs)
+        return BatchFeature(
+            data=out, tensor_type=common_kwargs.pop("return_tensors", None)
+        )

src/axolotl/utils/schemas/config.py

@@ -436,8 +436,8 @@ class AxolotlInputConfig(
         },
     )
     min_sample_len: int | None = None
-    max_prompt_len: int = Field(
-        default=512,
+    max_prompt_len: int | None = Field(
+        default=None,
         json_schema_extra={"description": "maximum prompt length for RL training"},
     )
     sample_packing: bool | None = Field(

tests/monkeypatch/test_mistral_tokenizer_patch.py

@@ -0,0 +1,35 @@
+"""Integration tests for MistralCommonTokenizer patches."""
+
+import pytest
+
+
+class TestMistralTokenizerPatchIntegration:
+    """Test MistralCommonTokenizer patch integration."""
+
+    @pytest.mark.integration
+    def test_mistral_tokenizer_image_patch(self):
+        """Test that MistralCommonTokenizer image patch can be applied."""
+        try:
+            from transformers.tokenization_mistral_common import MistralCommonTokenizer
+        except ImportError:
+            pytest.skip("MistralCommonTokenizer not available")
+
+        from axolotl.monkeypatch.models.mistral3.mistral_common_tokenizer import (
+            apply_mistral_tokenizer_image_patch,
+        )
+
+        # Store original method
+        original_apply_chat_template = MistralCommonTokenizer.apply_chat_template
+
+        # Apply patch
+        apply_mistral_tokenizer_image_patch()
+
+        # Verify patch was applied
+        assert (
+            MistralCommonTokenizer.apply_chat_template != original_apply_chat_template
+        ), "apply_chat_template was not patched"
+
+        # Verify the method is still callable
+        assert callable(MistralCommonTokenizer.apply_chat_template), (
+            "Patched method is not callable"
+        )

tests/monkeypatch/test_pixtral_flash_attention_patch.py

@@ -0,0 +1,77 @@
+"""Integration tests for Pixtral Flash Attention patches."""
+
+import pytest
+import torch
+
+
+class TestPixtralFlashAttentionPatchIntegration:
+    """Test Pixtral Flash Attention patch integration."""
+
+    @pytest.mark.integration
+    def test_pixtral_flash_attention_patch(self):
+        """Test that Pixtral Flash Attention patch can be applied and works correctly."""
+        try:
+            from transformers import modeling_flash_attention_utils
+        except ImportError:
+            pytest.skip("Flash Attention utils not available")
+
+        from axolotl.monkeypatch.models.pixtral.modeling_flash_attention_utils import (
+            apply_patch_is_packed_sequence,
+        )
+
+        # Store original method
+        original_is_packed_sequence = modeling_flash_attention_utils._is_packed_sequence
+
+        # Apply patch and get unpatch function
+        unpatch_fn = apply_patch_is_packed_sequence()
+
+        # Verify patch was applied
+        assert (
+            modeling_flash_attention_utils._is_packed_sequence
+            != original_is_packed_sequence
+        ), "_is_packed_sequence was not patched"
+
+        # Test the patched function with 1D position_ids
+        patched_fn = modeling_flash_attention_utils._is_packed_sequence
+
+        # Test 1D position_ids 1 sequence
+        position_ids_1d = torch.tensor([0, 1, 2, 3])
+        result = patched_fn(position_ids_1d, batch_size=1)
+        assert isinstance(result, bool), "Function should return a boolean"
+        assert result is False, "1D sequential position_ids should not be packed"
+
+        # Test 1D packed 2 sequences
+        position_ids_1d_packed = torch.tensor([0, 1, 2, 0, 1, 2])
+        result = patched_fn(position_ids_1d_packed, batch_size=1)
+        assert isinstance(result, bool), "Function should return a boolean"
+        assert result is True, "1D packed position_ids should be detected as packed"
+
+        # Test 2D packed 2 sequences
+        position_ids_2d_packed = torch.tensor([[0, 1, 2, 3, 0, 1]])
+        result = patched_fn(position_ids_2d_packed, batch_size=1)
+        assert isinstance(result, bool), "Function should return a boolean"
+        assert result is True, "2D packed position_ids should be detected as packed"
+
+        # Test 2D 1 sequence
+        position_ids_2d_normal = torch.tensor([[0, 1, 2, 3, 4, 5]])
+        result = patched_fn(position_ids_2d_normal, batch_size=1)
+        assert isinstance(result, bool), "Function should return a boolean"
+        assert result is False, "2D sequential position_ids should not be packed"
+
+        # Test 2D batch size 2
+        position_ids_2d_normal = torch.tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8]])
+        result = patched_fn(position_ids_2d_normal, batch_size=2)
+        assert isinstance(result, bool), "Function should return a boolean"
+        assert result is False, "2D position_ids batch 2 should not be packed"
+
+        # Test None case
+        result = patched_fn(None, batch_size=1)
+        assert isinstance(result, bool), "Function should return a boolean"
+        assert result is False, "None position_ids should return False"
+
+        # Test unpatch function
+        unpatch_fn()
+        assert (
+            modeling_flash_attention_utils._is_packed_sequence
+            == original_is_packed_sequence
+        ), "unpatch function did not restore original method"

tests/monkeypatch/test_qwen3_next_modeling_patch.py

@@ -0,0 +1,111 @@
+"""Integration tests for Qwen3 Next modeling patches."""
+
+import pytest
+import torch
+
+# Skip entire module if qwen3_next not available
+qwen3_next = pytest.importorskip("transformers.models.qwen3_next.modeling_qwen3_next")
+
+
+class TestQwen3NextModelingPatchIntegration:
+    """Test Qwen3 Next modeling patch integration."""
+
+    @pytest.mark.integration
+    def test_qwen3_next_decoder_layer_patch(self):
+        """Test that Qwen3Next decoder layer patch can be applied."""
+        from axolotl.monkeypatch.models.qwen3_next.modeling import (
+            patch_qwen3_next_decoder_layer,
+        )
+
+        # Store original method
+        original_forward = qwen3_next.Qwen3NextDecoderLayer.forward
+
+        # Apply patch and get unpatch function
+        unpatch_fn = patch_qwen3_next_decoder_layer()
+
+        # Verify patch was applied
+        assert qwen3_next.Qwen3NextDecoderLayer.forward != original_forward, (
+            "decoder layer forward method was not patched"
+        )
+
+        # Verify the method is still callable
+        assert callable(qwen3_next.Qwen3NextDecoderLayer.forward), (
+            "Patched method is not callable"
+        )
+
+        # Test unpatch function
+        if unpatch_fn:
+            unpatch_fn()
+            assert qwen3_next.Qwen3NextDecoderLayer.forward == original_forward, (
+                "unpatch function did not restore original method"
+            )
+
+    @pytest.mark.integration
+    def test_qwen3_next_gateddelta_layer_patch(self):
+        """Test that Qwen3Next GatedDeltaNet patch can be applied."""
+        from axolotl.monkeypatch.models.qwen3_next.modeling import (
+            patch_qwen3_next_gateddelta_layer,
+        )
+
+        # Store original method
+        original_forward = qwen3_next.Qwen3NextGatedDeltaNet.forward
+
+        # Apply patch and get unpatch function
+        unpatch_fn = patch_qwen3_next_gateddelta_layer()
+
+        # Verify patch was applied
+        assert qwen3_next.Qwen3NextGatedDeltaNet.forward != original_forward, (
+            "GatedDeltaNet forward method was not patched"
+        )
+
+        # Verify the method is still callable
+        assert callable(qwen3_next.Qwen3NextGatedDeltaNet.forward), (
+            "Patched method is not callable"
+        )
+
+        # Test unpatch function
+        if unpatch_fn:
+            unpatch_fn()
+            assert qwen3_next.Qwen3NextGatedDeltaNet.forward == original_forward, (
+                "unpatch function did not restore original method"
+            )
+
+    @pytest.mark.integration
+    def test_qwen3_next_imports_patch(self):
+        """Test that Qwen3Next imports patch can be applied without errors."""
+        from axolotl.monkeypatch.models.qwen3_next.modeling import (
+            patch_qwen3_next_imports,
+        )
+
+        # Apply patch - should not raise any exceptions even if modules unavailable
+        unpatch_fn = patch_qwen3_next_imports()
+
+        # Test that unpatch function is returned (or None if skipped)
+        assert unpatch_fn is None or callable(unpatch_fn), (
+            "patch_qwen3_next_imports should return None or callable unpatch function"
+        )
+
+    @pytest.mark.integration
+    def test_qwen3_next_modeling_packing_patch(self):
+        """Test that all Qwen3Next modeling patches can be applied together."""
+        from axolotl.monkeypatch.models.qwen3_next.modeling import (
+            patch_qwen3_next_modeling_packing,
+        )
+
+        # This should not raise any exceptions
+        patch_qwen3_next_modeling_packing()
+
+
+@pytest.mark.integration
+def test_get_cu_seqlens_utility():
+    """Test the get_cu_seqlens utility function."""
+    from axolotl.monkeypatch.models.qwen3_next.modeling import get_cu_seqlens
+
+    # Test with simple position_ids
+    position_ids = torch.tensor([[0, 1, 2, 0, 1]])
+    cu_seqlens = get_cu_seqlens(position_ids)
+    assert cu_seqlens.dtype == torch.int32, "Should be int32 dtype"
+
+    # Should return tensor with start positions and total length
+    expected = torch.tensor([0, 3, 5], dtype=torch.int32)
+    assert torch.equal(cu_seqlens, expected), f"Expected {expected}, got {cu_seqlens}"

tests/monkeypatch/test_trainer_context_parallel_patch.py

@@ -0,0 +1,66 @@
+"""Tests for the HF Trainer context parallel patch."""
+
+import pytest
+from transformers import Trainer
+
+from axolotl.monkeypatch.transformers.trainer_context_parallel import (
+    GUARD_PATTERN,
+    PATCHED_GUARD,
+    patch_prepare_context_parallel_inputs,
+)
+
+
+@pytest.fixture
+def restore_trainer_prepare_method():
+    """Ensure Trainer._prepare_context_parallel_inputs is restored after a test."""
+    original_method = getattr(
+        Trainer,
+        "_original_prepare_context_parallel_inputs",
+        Trainer._prepare_context_parallel_inputs,
+    )
+    patched_attr_present = hasattr(
+        Trainer, "_axolotl_prepare_context_parallel_inputs_patched"
+    )
+
+    yield
+
+    Trainer._prepare_context_parallel_inputs = original_method
+    if patched_attr_present:
+        delattr(Trainer, "_axolotl_prepare_context_parallel_inputs_patched")
+    if hasattr(Trainer, "_original_prepare_context_parallel_inputs"):
+        delattr(Trainer, "_original_prepare_context_parallel_inputs")
+    if hasattr(Trainer, "_axolotl_prepare_context_parallel_inputs_source"):
+        delattr(Trainer, "_axolotl_prepare_context_parallel_inputs_source")
+
+
+def test_patch_attention_guard(restore_trainer_prepare_method):
+    """Patch should swap the guard to allow sdpa or flash attention."""
+    # Ensure we start from the unpatched method
+    if hasattr(Trainer, "_original_prepare_context_parallel_inputs"):
+        Trainer._prepare_context_parallel_inputs = (
+            Trainer._original_prepare_context_parallel_inputs
+        )
+        delattr(Trainer, "_original_prepare_context_parallel_inputs")
+    if hasattr(Trainer, "_axolotl_prepare_context_parallel_inputs_patched"):
+        delattr(Trainer, "_axolotl_prepare_context_parallel_inputs_patched")
+
+    patch_prepare_context_parallel_inputs()
+
+    patched_method = Trainer._prepare_context_parallel_inputs
+    assert patched_method is not None
+    assert getattr(Trainer, "_axolotl_prepare_context_parallel_inputs_patched", False)
+
+    source = Trainer._axolotl_prepare_context_parallel_inputs_source
+    assert GUARD_PATTERN not in source
+    assert PATCHED_GUARD in source
+
+
+def test_patch_is_idempotent(restore_trainer_prepare_method):
+    """Calling the patch twice should leave the same patched function in place."""
+    patch_prepare_context_parallel_inputs()
+    first_patched = Trainer._prepare_context_parallel_inputs
+
+    patch_prepare_context_parallel_inputs()
+    second_patched = Trainer._prepare_context_parallel_inputs
+
+    assert first_patched is second_patched

tests/monkeypatch/test_voxtral_modeling_patch.py

@@ -0,0 +1,43 @@
+"""Integration tests for Voxtral modeling patches."""
+
+import pytest
+
+
+class TestVoxtralModelingPatchIntegration:
+    """Test Voxtral modeling patch integration."""
+
+    @pytest.mark.integration
+    def test_voxtral_conditional_generation_patch(self):
+        """Test that Voxtral conditional generation patch can be applied."""
+        try:
+            from transformers.models.voxtral.modeling_voxtral import (
+                VoxtralForConditionalGeneration,
+            )
+        except ImportError:
+            pytest.skip("VoxtralForConditionalGeneration not available")
+
+        from axolotl.monkeypatch.models.voxtral.modeling import (
+            patch_voxtral_conditional_generation_forward,
+        )
+
+        # Store original method
+        original_forward = VoxtralForConditionalGeneration.forward
+
+        # Apply patch and get unpatch function
+        unpatch_fn = patch_voxtral_conditional_generation_forward()
+
+        # Verify patch was applied
+        assert VoxtralForConditionalGeneration.forward != original_forward, (
+            "forward method was not patched"
+        )
+
+        # Verify the method is still callable
+        assert callable(VoxtralForConditionalGeneration.forward), (
+            "Patched method is not callable"
+        )
+
+        # Test unpatch function
+        unpatch_fn()
+        assert VoxtralForConditionalGeneration.forward == original_forward, (
+            "unpatch function did not restore original method"
+        )

tests/prompt_strategies/conftest.py

@@ -177,6 +177,15 @@ def fixture_devstral_1_1_tokenizer():
     return tokenizer
 
 
+@pytest.fixture(name="qwen3_tokenizer")
+def qwen3_tokenizer_fixture(
+    download_qwen3_half_billion_model,
+):  # pylint: disable=unused-argument,redefined-outer-name
+    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
+
+    return tokenizer
+
+
 @pytest.fixture(name="mistralv03_tokenizer_chat_template_jinja")
 def fixture_mistralv03_chat_template_jinja_w_system() -> str:
     return '{%- if messages[0]["role"] == "system" %}\n    {%- set system_message = messages[0]["content"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr("role", "equalto", "user") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n    {%- if not (message.role == "tool" or message.role == "tool_results" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n        {%- if (message["role"] == "user") != (ns.index % 2 == 0) %}\n            {{- raise_exception("After the optional system message, conversation roles must alternate user/assistant/user/assistant/...") }}\n        {%- endif %}\n        {%- set ns.index = ns.index + 1 %}\n    {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if message["role"] == "user" %}\n        {%- if tools is not none and (message == user_messages[-1]) %}\n            {{- "[AVAILABLE_TOOLS] [" }}\n            {%- for tool in tools %}\n                {%- set tool = tool.function %}\n                {{- \'{"type": "function", "function": {\' }}\n                {%- for key, val in tool.items() if key != "return" %}\n                    {%- if val is string %}\n                        {{- \'"\' + key + \'": "\' + val + \'"\' }}\n                    {%- else %}\n                        {{- \'"\' + key + \'": \' + val|tojson }}\n                    {%- endif %}\n                    {%- if not loop.last %}\n                        {{- ", " }}\n                    {%- endif %}\n                {%- endfor %}\n                {{- "}}" }}\n                {%- if not loop.last %}\n                    {{- ", " }}\n                {%- else %}\n                    {{- "]" }}\n                {%- endif %}\n            {%- endfor %}\n            {{- "[/AVAILABLE_TOOLS]" }}\n            {%- endif %}\n        {%- if loop.first and system_message is defined %}\n            {{- "[INST] " + system_message + "\\n\\n" + message["content"] + "[/INST]" }}\n        {%- else %}\n            {{- "[INST] " + message["content"] + "[/INST]" }}\n        {%- endif %}\n    {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n        {{- "[TOOL_CALLS] [" }}\n        {%- for tool_call in message.tool_calls %}\n            {%- set out = tool_call.function|tojson %}\n            {{- out[:-1] }}\n            {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n                {{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}\n            {%- endif %}\n            {{- \', "id": "\' + tool_call.id + \'"}\' }}\n            {%- if not loop.last %}\n                {{- ", " }}\n            {%- else %}\n                {{- "]" + eos_token }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif message["role"] == "assistant" %}\n        {{- " " + message["content"]|trim + eos_token}}\n    {%- elif message["role"] == "tool_results" or message["role"] == "tool" %}\n        {%- if message.content is defined and message.content.content is defined %}\n            {%- set content = message.content.content %}\n        {%- else %}\n            {%- set content = message.content %}\n        {%- endif %}\n        {{- \'[TOOL_RESULTS] {"content": \' + content|string + ", " }}\n        {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n            {{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}\n        {%- endif %}\n        {{- \'"call_id": "\' + message.tool_call_id + \'"}[/TOOL_RESULTS]\' }}\n    {%- else %}\n        {{- raise_exception("Only user and assistant roles are supported, with the exception of an initial optional system message!") }}\n    {%- endif %}\n{%- endfor %}\n'

tests/prompt_strategies/test_chat_template_ds_schema_unification.py

@@ -6,7 +6,6 @@ import json
 
 import pytest
 from datasets import Dataset
-from transformers import AutoTokenizer
 
 from axolotl.prompt_strategies.chat_template import StrategyLoader
 from axolotl.utils.dict import DictDefault
@@ -23,15 +22,6 @@ def fixture_messages_w_tools():
     return Dataset.from_list(rows)
 
 
-@pytest.fixture(name="qwen3_tokenizer")
-def qwen3_tokenizer_fixture(
-    download_qwen3_half_billion_model,
-):
-    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
-
-    return tokenizer
-
-
 @pytest.fixture(name="qwen3_prompt_strategy")
 def qwen3_chat_template_strategy(qwen3_tokenizer):
     cfg = DictDefault(

tests/prompt_strategies/test_chat_templates_thinking.py

@@ -4,7 +4,6 @@ Tests for splitting reasoning/thinking from content into separate field
 
 import pytest
 from datasets import Dataset
-from transformers import AutoTokenizer
 
 from axolotl.prompt_strategies.chat_template import (
     load,
@@ -56,15 +55,6 @@ def messages_w_reasoning_fixture():
     )
 
 
-@pytest.fixture(name="qwen3_tokenizer")
-def qwen3_tokenizer_fixture(
-    download_qwen3_half_billion_model,
-):
-    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
-
-    return tokenizer
-
-
 class TestSplitThinking:
     """
     test class to make sure datasets with reasoning content conforms to the chat_template strategy

tests/prompt_strategies/test_chat_templates_tool_call_string_arguments.py

@@ -0,0 +1,214 @@
+"""
+Tests for handling json tool content
+"""
+
+import json
+
+import pytest
+from datasets import Dataset
+
+from axolotl.prompt_strategies.chat_template import (
+    load,
+)
+from axolotl.utils.dict import DictDefault
+
+
+@pytest.fixture(name="qwen3_instruct_prompt_strategy")
+def qwen3_instruct_chat_template_strategy(qwen3_tokenizer):
+    strategy = load(
+        qwen3_tokenizer,
+        DictDefault(
+            {
+                "train_on_inputs": False,
+                "sequence_len": 512,
+            }
+        ),
+        DictDefault(
+            {
+                "chat_template": "qwen3",
+                "message_field_role": "role",
+                "message_field_content": "content",
+                "message_property_mappings": {
+                    "role": "role",
+                    "content": "content",
+                },
+                "roles": {
+                    "user": ["user"],
+                    "assistant": ["assistant"],
+                    "system": ["system"],
+                },
+                "field_messages": "messages",
+            }
+        ),
+    )
+    return strategy
+
+
+class TestQwen3IdenticalConversationArgs:
+    """
+    Test Qwen3 tools is identical between JSON and dict
+    """
+
+    @pytest.fixture(name="conversation_dict_args_dataset")
+    def fixture_conversation_dict_args_dataset(self):
+        """
+        Provides a dataset with conversation where arguments is a dict.
+        """
+        user_content = "What is the weather in Boston?"
+        function_name = "get_current_weather"
+        arguments_dict = {"location": "Boston, MA", "unit": "celsius"}
+
+        data = [
+            {
+                "messages": [
+                    {"role": "user", "content": user_content},
+                    {
+                        "role": "assistant",
+                        "content": "",
+                        "tool_calls": [
+                            {
+                                "function": {
+                                    "name": function_name,
+                                    "arguments": arguments_dict,  # dict格式
+                                }
+                            }
+                        ],
+                    },
+                ],
+            }
+        ]
+        return Dataset.from_list(data)
+
+    @pytest.fixture(name="conversation_str_args_dataset")
+    def fixture_conversation_str_args_dataset(self):
+        """
+        Provides a dataset with conversation where arguments is a JSON string.
+        """
+        user_content = "What is the weather in Boston?"
+        function_name = "get_current_weather"
+        arguments_dict = {"location": "Boston, MA", "unit": "celsius"}
+        arguments_str = json.dumps(arguments_dict)
+
+        data = [
+            {
+                "messages": [
+                    {"role": "user", "content": user_content},
+                    {
+                        "role": "assistant",
+                        "content": "",
+                        "tool_calls": [
+                            {
+                                "function": {
+                                    "name": function_name,
+                                    "arguments": arguments_str,  # str格式
+                                }
+                            }
+                        ],
+                    },
+                ],
+            }
+        ]
+        return Dataset.from_list(data)
+
+    @pytest.fixture(name="conversation_mixed_time_types_dataset")
+    def fixture_conversation_mixed_time_types_dataset(self):
+        """
+        Provides a dataset where 'time' field has different types in different tool calls.
+        """
+        data = [
+            {
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": "Get weather information at different times",
+                    },
+                    {
+                        "role": "assistant",
+                        "content": "",
+                        "tool_calls": [
+                            {
+                                "function": {
+                                    "name": "func1",
+                                    "arguments": json.dumps(
+                                        {"time": "2025-08-01"}
+                                    ),  # string type
+                                }
+                            },
+                            {
+                                "function": {
+                                    "name": "func2",
+                                    "arguments": json.dumps(
+                                        {"time": 1690876800}
+                                    ),  # number type
+                                }
+                            },
+                        ],
+                    },
+                ],
+            }
+        ]
+        return Dataset.from_list(data)
+
+    def test_dict_and_str_args_produce_identical_output(
+        self,
+        conversation_dict_args_dataset,
+        conversation_str_args_dataset,
+        qwen3_instruct_prompt_strategy,
+        qwen3_tokenizer,
+    ):
+        """
+        Tests that after tokenization and decoding, the outputs for both
+        dict and string `arguments` are exactly the same.
+        """
+        processed_dict_args = conversation_dict_args_dataset.map(
+            qwen3_instruct_prompt_strategy.tokenize_prompt,
+            batched=True,
+            remove_columns=["messages"],
+        )
+
+        processed_str_args = conversation_str_args_dataset.map(
+            qwen3_instruct_prompt_strategy.tokenize_prompt,
+            batched=True,
+            remove_columns=["messages"],
+        )
+
+        decoded_prompt_from_dict = qwen3_tokenizer.decode(
+            processed_dict_args[0]["input_ids"]
+        )
+
+        decoded_prompt_from_str = qwen3_tokenizer.decode(
+            processed_str_args[0]["input_ids"]
+        )
+
+        assert decoded_prompt_from_dict == decoded_prompt_from_str, (
+            f"Dict format output:\n{decoded_prompt_from_dict}\n"
+            f"String format output:\n{decoded_prompt_from_str}"
+        )
+
+        assert (
+            processed_dict_args[0]["input_ids"] == processed_str_args[0]["input_ids"]
+        ), "The tokenized input_ids should be identical for dict and str arguments"
+
+    def test_str_args_with_mixed_time_types_no_error(
+        self,
+        conversation_mixed_time_types_dataset,
+        qwen3_instruct_prompt_strategy,
+        qwen3_tokenizer,
+    ):
+        """
+        Tests that when 'time' field has different types (string vs number)
+        in different tool calls, str format arguments don't cause errors.
+        """
+        processed = conversation_mixed_time_types_dataset.map(
+            qwen3_instruct_prompt_strategy.tokenize_prompt,
+            batched=True,
+            remove_columns=["messages"],
+        )
+
+        assert len(processed) == 1
+        assert "input_ids" in processed[0]
+        assert len(processed[0]["input_ids"]) > 0
+
+        decoded = qwen3_tokenizer.decode(processed[0]["input_ids"])
+        assert "2025-08-01" in decoded, "String time value should be present"
+        assert "1690876800" in decoded, "Number time value should be present"