precommit

fix tests
2025-03-21 16:43:14 +00:00 · 2025-03-21 16:36:56 +00:00 · 2025-03-21 16:36:54 +00:00 · 2025-03-21 16:36:34 +00:00 · 2025-03-21 16:36:34 +00:00 · 2025-03-21 16:36:32 +00:00
48 changed files with 547 additions and 3384 deletions
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -23,7 +23,6 @@ jobs:
        - name: Install dependencies
          run: |
            python3 -m pip install jupyter quartodoc
-            python3 -m pip install -e . --no-deps
        - name: Build autodoc
          run: quartodoc build
        - name: Publish to GitHub Pages (and render)
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -133,7 +133,6 @@ quartodoc:
        - utils.schemas.datasets
        - utils.schemas.peft
        - utils.schemas.trl
-        - utils.schemas.multimodal
        - utils.schemas.integrations
        - utils.schemas.enums
        - utils.schemas.utils
--- a/docs/config.qmd
+++ b/docs/config.qmd
@@ -466,7 +466,6 @@ auto_find_batch_size: # Optional[bool]

 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
-do_causal_lm_eval: # Whether to run causal language model evaluation for metrics in `eval_causal_lm_metrics`.
 eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", "chrf", "perplexity"]

 profiler_steps: # enable the pytorch profiler to capture the first N steps of training to the output_dir.
@@ -507,58 +506,36 @@ lr_div_factor: # Learning rate div factor

 # Specify optimizer
 # Valid values are driven by the Transformers OptimizerNames class, see:
-# https://github.com/huggingface/transformers/blob/cbf924b76c03828101a34069a96d209314114fd5/src/transformers/training_args.py#L144-L189
+# https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
 #
 # Note that not all optimizers may be available in your environment, ex: 'adamw_anyprecision' is part of
 # torchdistx, 'adamw_bnb_8bit' is part of bnb.optim.Adam8bit, etc. When in doubt, it is recommended to start with the optimizer used
 # in the examples/ for your model and fine-tuning use case.
 #
 # Valid values for 'optimizer' include:
+# - adamw_hf
 # - adamw_torch
 # - adamw_torch_fused
 # - adamw_torch_xla
-# - adamw_torch_npu_fused
 # - adamw_apex_fused
-# - adopt_adamw  (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
+# - adopt_adamw (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
 # - adafactor
 # - adamw_anyprecision
-# - adamw_torch_4bit
-# - ademamix
 # - sgd
 # - adagrad
 # - adamw_bnb_8bit
-# - adamw_8bit   # alias for adamw_bnb_8bit
-# - ademamix_8bit
 # - lion_8bit
 # - lion_32bit
 # - paged_adamw_32bit
 # - paged_adamw_8bit
-# - paged_ademamix_32bit
-# - paged_ademamix_8bit
 # - paged_lion_32bit
 # - paged_lion_8bit
-# - rmsprop
-# - rmsprop_bnb
-# - rmsprop_bnb_8bit
-# - rmsprop_bnb_32bit
 # - galore_adamw
 # - galore_adamw_8bit
 # - galore_adafactor
 # - galore_adamw_layerwise
 # - galore_adamw_8bit_layerwise
 # - galore_adafactor_layerwise
-# - lomo
-# - adalomo
-# - grokadamw
-# - schedule_free_adamw
-# - schedule_free_sgd
-# - apollo_adamw
-# - apollo_adamw_layerwise
-#
-# Additional custom optimizers include:
-# - optimi_adamw
-# - ao_adamw_8bit
-# - ao_adamw_fp8
 optimizer:
 # Dictionary of arguments to pass to the optimizer
 optim_args:
@@ -610,14 +587,6 @@ resume_from_checkpoint:
 # Be careful with this being turned on between different models.
 auto_resume_from_checkpoints: false

-## Multimodal section
-# int | tuple[int, int] | None . Size to resize images to, width x height.
-# Will read from model/processor config if not set.
-image_size:
-# str. Algorithm to use for image resizing. "bilinear", "bicubic", "lanczos". Default is "bilinear".
-image_resize_algorithm: 'bilinear'
-## End of multimodal section
-
 # Don't mess with this, it's here for accelerate and torchrun
 local_rank:

--- a/docs/docker.qmd
+++ b/docs/docker.qmd
@@ -103,7 +103,8 @@ This uses the same tags as the [`main` image](#sec-main-tags).

 - `JUPYTER_DISABLE`: Disable Jupyter lab.
 - `JUPYTER_PASSWORD`: Set a password for the Jupyter lab.
- `PUBLIC_KEY` / `SSH_KEY`: Add a public key for the SSH service.
+- `PUBLIC_KEY`: Add a public key for the SSH service.
+- `SSH_KEY`: Add a private key for the SSH service.

 #### Volume mounts

--- a/docs/multimodal.qmd
+++ b/docs/multimodal.qmd
@@ -1,171 +1,28 @@
---
-title: MultiModal / Vision Language Models (BETA)
-format:
-  html:
-    toc: true
-    toc-depth: 3
---
+# MultiModal / Vision Language Models (BETA)

-## Supported Models
+### Supported Models

- [Mllama](#sec-mllama)
- [Pixtral](#sec-pixtral)
- [Llava-1.5](#sec-llava-15)
- [Mistral-Small-3.1](#sec-mistral-small-31)
- [Gemma-3](#sec-gemma-3)
- [Qwen2-VL](#sec-qwen2-vl)
- [Qwen2.5-VL](#sec-qwen25-vl)
+- Mllama, i.e. llama with vision models

-## Usage
+### Usage

-Multimodal support is limited and doesn't have full feature parity.
-
-Here are the hyperparams you'll need to use to finetune a multimodal model.
+Currently multimodal support is limited and doesn't have full feature parity. To finetune a multimodal Llama w/ LoRA,
+you'll need to use the following in YAML in combination with the rest of the required hyperparams.

 ```yaml
+base_model: alpindale/Llama-3.2-11B-Vision-Instruct
 processor_type: AutoProcessor
-
 skip_prepare_dataset: true
-remove_unused_columns: false  # leave columns in place as they are needed to handle image embeddings during training
-sample_packing: false  # not yet supported with multimodal

-chat_template:  # see in next section
-
-# example dataset
+chat_template: llama3_2_vision
 datasets:
  - path: HuggingFaceH4/llava-instruct-mix-vsft
    type: chat_template
    split: train[:1%]
    field_messages: messages
+remove_unused_columns: false
+sample_packing: false

-# (optional) if doing lora, only finetune the Language model,
-# leave the vision model and vision tower frozen
-# load_in_8bit: true
-adapter: lora
+# only finetune the Language model, leave the vision model and vision tower frozen
 lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
-
-# (optional) if you want to resize images to a set size
-image_size: 512
-image_resize_algorithm: bilinear
-```
-
-Please see [examples](https://github.com/axolotl-ai/axolotl/tree/main/examples) folder for full configs.
-
-::: {.callout-warning}
-Some of our chat_templates have been extended to support broader dataset types. This should not break any existing configs.
-:::
-
-### Mllama {#sec-mllama}
-
-```yaml
-base_model: meta-llama/Llama-3.2-11B-Vision-Instruct
-
-chat_template: llama3_2_vision
-```
-
-### Pixtral {#sec-pixtral}
-
-```yaml
-base_model: mistralai/Pixtral-12B-2409
-
-chat_template: pixtral
-```
-
-### Llava-1.5 {#sec-llava-15}
-
-```yaml
-base_model: llava-hf/llava-1.5-7b-hf
-
-chat_template: llava
-```
-
-### Mistral-Small-3.1 {#sec-mistral-small-31}
-
-```yaml
-base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
-
-chat_template: mistral_v7_tekken
-```
-
-### Gemma-3 {#sec-gemma-3}
-
-::: {.callout-tip}
-The Gemma3-1B model is a text-only model, so please train as regular text model.
-:::
-
-For multi-modal 4B/12B/27B models, use the following config:
-
-```yaml
-base_model: google/gemma-3-4b-it
-
-chat_template: gemma3
-```
-
-### Qwen2-VL {#sec-qwen2-vl}
-
-```yaml
-base_model: Qwen/Qwen2-VL-7B-Instruct
-
-chat_template: qwen2_vl
-```
-
-### Qwen2.5-VL {#sec-qwen25-vl}
-
-```yaml
-base_model: Qwen/Qwen2.5-VL-7B-Instruct
-
-chat_template: qwen2_vl  # same as qwen2-vl
-```
-
-## Dataset Format
-
-For multi-modal datasets, we adopt an extended `chat_template` format similar to OpenAI's Message format.
-
- A message is a list of `role` and `content`.
- `role` can be `system`, `user`, `assistant`, etc.
- `content` is a list of `type` and (`text` or `image` or `path` or `url` or `base64`).
-
-::: {.callout-note}
-For backwards compatibility:
-
- If the dataset has a `images` or `image` column of `list[Image]`, it will be appended to the first `content` list as `{"type": "image", "image": ...}`. However, if the content already has a `{"type": "image"}` but no `image` key, it will be set the `image` key.
- If `content` is a string, it will be converted to a list with `type` as `text`.
-:::
-
-::: {.callout-tip}
-For image loading, you can use the following keys within `content` alongside `"type": "image"`:
-
- `"path": "/path/to/image.jpg"`
- `"url": "https://example.com/image.jpg"`
- `"base64": "..."`
- `"image": PIL.Image`
-:::
-
-Here is an example of a multi-modal dataset:
-```json
-[
-  {
-    "messages": [
-        {
-            "role": "system",
-            "content": [
-              {"type": "text", "text": "You are a helpful assistant."}
-              ]
-        },
-        {
-            "role": "user",
-            "content": [
-                {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
-                {"type": "text", "text": "Describe this image in detail."}
-            ]
-        },
-        {
-            "role": "assistant",
-            "content": [
-              {"type": "text", "text": "The image is a bee."}
-            ]
-        }
-    ]
-  }
-]
 ```
--- a/examples/cohere/command-r-7b-qlora.yml
+++ b/examples/cohere/command-r-7b-qlora.yml
@@ -1,71 +0,0 @@
-base_model: CohereForAI/c4ai-command-r7b-12-2024
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-# huggingface repo
-chat_template: cohere
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: qlora
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: false
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch:
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
--- a/examples/gemma3/gemma-3-4b-lora.yml
+++ b/examples/gemma3/gemma-3-4b-lora.yml
@@ -1,63 +0,0 @@
-base_model: google/gemma-3-4b-it
-processor_type: AutoProcessor
-strict: false
-
-# 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
-
-chat_template: gemma3
-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
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 2048
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'language_model.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: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: true
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
--- a/examples/gemma3/qlora.yml
+++ b/examples/gemma3/qlora.yml
@@ -1,74 +0,0 @@
-base_model: google/gemma-3-1b-it
-# optionally might have model_type or tokenizer_type
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-# huggingface repo
-chat_template: gemma3_text
-datasets:
-  - path: cgato/SlimOrcaDedupCleaned
-    type: chat_template
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-adapter: qlora
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-
-sequence_len: 2048
-sample_packing: true
-eval_sample_packing: false
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_ratio: 0.1
-evals_per_epoch:
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
--- a/examples/llava/lora-7b.yaml
+++ b/examples/llava/lora-7b.yaml
@@ -1,63 +0,0 @@
-base_model: llava-hf/llava-1.5-7b-hf
-processor_type: AutoProcessor
-strict: false
-
-# 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
-
-chat_template: llava
-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
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 8192
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'language_model.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: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: true
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
--- a/examples/mistral/mistral-small-3.1-24B-lora.yml
+++ b/examples/mistral/mistral-small-3.1-24B-lora.yml
@@ -1,66 +0,0 @@
-base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
-processor_type: AutoProcessor
-strict: false
-
-load_in_8bit: 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
-
-chat_template: mistral_v7_tekken
-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
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 2048
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'language_model.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
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: false # PixtralVisionModel does not support Flash Attention 2.0 yet.
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
--- a/examples/pixtral/lora-12b.yml
+++ b/examples/pixtral/lora-12b.yml
@@ -1,65 +0,0 @@
-base_model: mistral-community/pixtral-12b
-processor_type: AutoProcessor
-strict: false
-
-# 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
-
-chat_template: pixtral
-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
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 8192
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: 'language_model.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: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: false # PixtralVisionModel does not support Flash Attention 2.0 yet
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  pad_token: <pad>
--- a/examples/qwen2-vl/lora-7b.yaml
+++ b/examples/qwen2-vl/lora-7b.yaml
@@ -1,63 +0,0 @@
-base_model: Qwen/Qwen2-VL-7B-Instruct
-processor_type: AutoProcessor
-strict: false
-
-# 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
-
-chat_template: qwen2_vl
-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
-
-adapter: lora
-lora_model_dir:
-
-sequence_len: 8192
-pad_to_sequence_len: false
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules: '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: 4
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16:
-tf32: true
-
-gradient_checkpointing: true
-local_rank:
-logging_steps: 1
-flash_attention: true
-eager_attention:
-
-warmup_ratio: 0.1
-evals_per_epoch: 1
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
--- a/requirements.txt
+++ b/requirements.txt
@@ -12,7 +12,7 @@ liger-kernel==0.5.3
 packaging==23.2

 peft==0.15.0
-transformers==4.50.0
+transformers==4.49.0
 tokenizers>=0.21.1
 accelerate==1.5.2
 datasets==3.4.1
--- a/requirements_env.txt
+++ b/requirements_env.txt
@@ -0,0 +1,315 @@
+accelerate==0.34.1
+addict==2.4.0
+aiofiles==23.2.1
+aiohttp==3.9.0
+aiosignal==1.3.1
+aiostream==0.5.2
+alembic==1.13.1
+annotated-types==0.6.0
+annoy==1.17.3
+ansible==6.7.0
+ansible-core==2.13.13
+ansible-vault==2.1.0
+anyio==3.7.1
+appdirs==1.4.4
+art==6.0
+asgiref==3.7.2
+async-timeout==4.0.2
+attrdict==2.0.1
+attrs==22.2.0
+awscli==1.32.75
+-e git+ssh://git@github.com/OpenAccess-AI-Collective/axolotl.git@6e354682e3c1735d3f7fb9e362280c38e922260f#egg=axolotl
+backoff==2.2.1
+base58==2.1.1
+beartype==0.17.2
+bitnet==0.2.1
+bitsandbytes==0.42.0
+bittensor==6.7.0
+black==23.7.0
+blinker==1.7.0
+boto3==1.34.75
+botocore==1.34.75
+cachetools==5.3.3
+cachy==0.1.1
+certifi==2023.7.22
+cffi==1.16.0
+cfgv==3.3.1
+chai-guanaco==1.2.4
+charset-normalizer==3.2.0
+cleo==0.6.8
+click==8.1.7
+cloudpickle==2.0.0
+cohere==4.11.2
+colorama==0.4.4
+coloredlogs==15.0.1
+CoLT5-attention==0.10.20
+contextlib2==21.6.0
+contourpy==1.2.0
+cryptography==41.0.3
+cycler==0.12.1
+cytoolz==0.12.3
+databricks-cli==0.18.0
+dataclasses-json==0.5.7
+datasets==2.11.0
+ddt==1.6.0
+decorator==5.1.1
+deepspeed==0.15.0
+# Editable Git install with no remote (dialogpt==0.1)
+-e /Users/wing/Projects/ml/dialogpt/src
+dill==0.3.6
+distlib==0.3.6
+docker==7.0.0
+docker-pycreds==0.4.0
+docstring-parser==0.15
+docutils==0.16
+ecdsa==0.18.0
+einops==0.7.0
+einops-exts==0.0.4
+einx==0.1.3
+entrypoints==0.4
+eth-hash==0.6.0
+eth-keys==0.5.0
+eth-typing==4.0.0
+eth-utils==2.3.1
+evaluate==0.4.0
+exceptiongroup==1.1.1
+fastapi==0.109.2
+fastcore==1.5.29
+ffmpy==0.4.0
+filelock==3.12.2
+-e git+https://github.com/NousResearch/finetuning-subnet.git@24e9407d6b4430a7ca39d344692f89ce5a97d27e#egg=finetuning_subnet
+fire==0.5.0
+first==2.0.2
+flake8==7.0.0
+Flask==3.0.1
+fonttools==4.47.2
+frozendict==2.4.1
+frozenlist==1.3.3
+fschat @ git+https://github.com/lm-sys/FastChat.git@27a05b04a35510afb1d767ae7e5990cbd278f8fe
+fsspec==2023.6.0
+fuzzywuzzy==0.18.0
+gitdb==4.0.10
+GitPython==3.1.31
+google-pasta==0.2.0
+gradio==4.42.0
+gradio_client==1.3.0
+greenlet==2.0.2
+grpclib==0.4.7
+gunicorn==21.2.0
+h11==0.14.0
+h2==4.1.0
+hpack==4.0.0
+httpcore==0.17.3
+httpx==0.24.1
+huggingface-hub==0.23.4
+humanfriendly==10.0
+hyperframe==6.0.1
+identify==2.5.24
+idna==3.4
+immutables==0.20
+importlib-metadata==6.7.0
+importlib-resources==6.1.1
+inflection==0.5.1
+iniconfig==2.0.0
+itsdangerous==2.1.2
+Jinja2==3.1.2
+jmespath==1.0.1
+joblib==1.3.2
+jsonlines==3.1.0
+jsonschema==2.6.0
+kiwisolver==1.4.5
+langchain==0.0.144
+Levenshtein==0.24.0
+libcst==1.1.0
+liger-kernel==0.0.0
+lion-pytorch==0.1.2
+llama-cpp-python==0.1.36
+llvmlite==0.40.1
+local-attention==1.9.0
+loguru==0.7.0
+Mako==1.3.2
+Markdown==3.5.2
+markdown-it-py==3.0.0
+markdown2==2.4.10
+MarkupSafe==2.1.2
+marshmallow==3.19.0
+marshmallow-enum==1.5.1
+matplotlib==3.8.2
+mccabe==0.7.0
+mdurl==0.1.2
+MEGABYTE-pytorch==0.0.7
+-e git+https://github.com/cg123/mergekit.git@53c5f414774a0558b8d84858fb6374bc93a8f1c1#egg=mergekit
+mlflow==2.10.0
+modal==0.62.77
+more-itertools==10.2.0
+mpmath==1.2.1
+msgpack==1.0.7
+msgpack-numpy-opentensor==0.5.0
+multidict==6.0.4
+multiprocess==0.70.14
+munch==2.5.0
+mypy==1.3.0
+mypy-extensions==1.0.0
+nest-asyncio==1.6.0
+netaddr==0.10.1
+networkx==3.0rc1
+nh3==0.2.14
+nodeenv==1.8.0
+nomic==2.0.2
+numba==0.57.1
+numexpr==2.8.4
+numpy==1.24.4
+oauthlib==3.2.2
+openai==0.27.4
+openapi==1.1.0
+openapi-schema-pydantic==1.2.4
+optimum==1.8.6
+orjson==3.10.7
+packaging==23.1
+pandas==2.0.0
+parameterized==0.9.0
+password-strength==0.0.3.post2
+pastel==0.1.1
+pathos==0.3.0
+pathspec==0.11.1
+pathtools==0.1.2
+peft==0.11.1
+pendulum==3.0.0
+Pillow==9.5.0
+pip-tools==1.11.0
+platformdirs==3.2.0
+pluggy==1.4.0
+poetry==0.7.1
+pox==0.3.2
+ppft==1.7.6.6
+pre-commit==3.3.2
+prettytable==3.10.0
+prompt-toolkit==3.0.39
+protobuf==3.20.2
+protobuf3-to-dict==0.1.5
+psutil==5.9.5
+psycopg==3.1.18
+PuLP==2.8.0
+py==1.11.0
+py-bip39-bindings==0.1.11
+py-cpuinfo==9.0.0
+py-ed25519-zebra-bindings==1.0.1
+py-sr25519-bindings==0.2.0
+pyarrow==11.0.0
+pyasn1==0.6.0
+pycodestyle==2.11.1
+pycparser==2.21
+pycryptodome==3.20.0
+pydantic==2.5.3
+pydantic_core==2.14.6
+pydub==0.25.1
+pyfiglet==0.8.post1
+pyflakes==3.2.0
+Pygments==2.15.1
+PyJWT==2.8.0
+pylev==1.4.0
+PyNaCl==1.5.0
+pynvml==11.5.0
+pyparsing==2.4.7
+pyrsistent==0.14.11
+pytest==8.0.2
+pytest-asyncio==0.23.4
+python-dateutil==2.8.2
+python-dotenv==1.0.1
+python-Levenshtein==0.24.0
+python-multipart==0.0.9
+pytz==2023.3
+PyYAML==6.0.1
+querystring-parser==1.2.4
+rapidfuzz==3.6.1
+regex==2023.6.3
+requests==2.31.0
+requests-toolbelt==0.8.0
+resolvelib==0.8.1
+responses==0.18.0
+retry==0.9.2
+rich==13.7.0
+rsa==4.7.2
+ruff==0.6.3
+s3transfer==0.10.1
+safetensors==0.4.5
+sagemaker==2.148.0
+scalecodec==1.2.7
+schedulefree==1.2.1
+schema==0.7.5
+scikit-learn==1.4.0
+scipy==1.9.3
+seaborn==0.13.2
+semantic-version==2.10.0
+sentencepiece==0.2.0
+sentry-sdk==1.19.1
+setproctitle==1.3.2
+shellingham==1.5.4
+shortuuid==1.0.11
+shtab==1.6.5
+sigtools==4.0.1
+six==1.16.0
+skypilot==0.4.1
+smdebug-rulesconfig==1.0.1
+smmap==5.0.0
+sniffio==1.3.0
+SQLAlchemy==1.4.47
+sqlparse==0.4.4
+starlette==0.36.3
+substrate-interface==1.5.2
+svgwrite==1.4.3
+sympy==1.11.1
+synchronicity==0.6.7
+tabulate==0.9.0
+tblib==1.7.0
+tenacity==8.2.2
+tensor-parallel==2.0.0
+termcolor==2.2.0
+text2art==0.2.0
+threadpoolctl==3.2.0
+tiktoken==0.6.0
+time-machine==2.14.1
+timm==0.9.16
+tokenizers==0.19.1
+tokenmonster==1.1.12
+toml==0.9.6
+tomli==2.0.1
+tomlkit==0.12.0
+toolz==0.12.1
+torch==2.2.0
+torchdata==0.6.1
+torchdiffeq==0.2.3
+TorchFix==0.4.0
+torchtext==0.15.2
+torchvision==0.17.0
+tqdm==4.66.2
+transformers==4.44.2
+trl==0.9.6
+typer==0.12.5
+types-certifi==2021.10.8.3
+types-requests==2.31.0.20240125
+types-setuptools==69.0.0.20240125
+types-toml==0.10.8.7
+typing==3.7.4.3
+typing-inspect==0.8.0
+typing_extensions==4.9.0
+tyro==0.5.18
+tzdata==2023.3
+unique-names-generator==1.0.2
+urllib3==2.2.2
+uvicorn==0.22.0
+vector_quantize_pytorch==1.14.1
+virtualenv==20.23.0
+voyager==2.0.2
+wandb==0.16.2
+watchfiles==0.21.0
+wavedrom==2.0.3.post3
+wcwidth==0.2.6
+websocket-client==1.7.0
+websockets==12.0
+Werkzeug==3.0.1
+wonderwords==2.2.0
+xxhash==3.2.0
+yarl==1.8.2
+zetascale==2.2.7
+zipp==3.15.0
--- a/setup.py
+++ b/setup.py
@@ -16,7 +16,9 @@ def parse_requirements():
    with open("./requirements.txt", encoding="utf-8") as requirements_file:
        lines = [r.strip() for r in requirements_file.readlines()]
        for line in lines:
-            is_extras = "deepspeed" in line or "mamba-ssm" in line
+            is_extras = (
+                "deepspeed" in line or "mamba-ssm" in line or "lion-pytorch" in line
+            )
            if line.startswith("--extra-index-url"):
                # Handle custom index URLs
                _, url = line.split()
@@ -133,15 +135,15 @@ setup(
        "mlflow": [
            "mlflow",
        ],
+        "lion-pytorch": [
+            "lion-pytorch==0.1.2",
+        ],
        "galore": [
            "galore_torch",
        ],
-        "apollo": [
-            "apollo-torch",
-        ],
        "optimizers": [
            "galore_torch",
-            "apollo-torch",
+            "lion-pytorch==0.1.2",
            "lomo-optim==0.1.1",
            "torch-optimi==0.2.1",
        ],
--- a/src/axolotl/cli/inference.py
+++ b/src/axolotl/cli/inference.py
@@ -56,7 +56,7 @@ def do_inference(
        cfg: Dictionary mapping `axolotl` config keys to values.
        cli_args: Inference-specific CLI arguments.
    """
-    model, tokenizer, _ = load_model_and_tokenizer(cfg=cfg, inference=True)
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg, inference=True)
    prompter = cli_args.prompter

    prompter_module = None
@@ -151,7 +151,7 @@ def do_inference_gradio(
    """
    import gradio as gr

-    model, tokenizer, _ = load_model_and_tokenizer(cfg=cfg, inference=True)
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg, inference=True)
    prompter = cli_args.prompter

    prompter_module = None
--- a/src/axolotl/cli/merge_lora.py
+++ b/src/axolotl/cli/merge_lora.py
@@ -27,7 +27,7 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
    """
    print_axolotl_text_art()

-    model, tokenizer, processor = load_model_and_tokenizer(cfg=cfg)
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg)
    safe_serialization = cfg.save_safetensors is True

    LOG.info("Running merge of LoRA with base model...")
@@ -44,9 +44,6 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
        )
        tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))

-        if processor:
-            processor.save_pretrained(str(Path(cfg.output_dir) / "merged"))
-

 def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
    """
--- a/src/axolotl/cli/train.py
+++ b/src/axolotl/cli/train.py
@@ -17,7 +17,6 @@ from axolotl.cli.config import load_cfg
 from axolotl.common.datasets import load_datasets, load_preference_datasets
 from axolotl.integrations.base import PluginManager
 from axolotl.train import train
-from axolotl.utils import set_pytorch_cuda_alloc_conf
 from axolotl.utils.config import normalize_config, resolve_dtype
 from axolotl.utils.dict import DictDefault

@@ -34,9 +33,6 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs):
        cfg: Dictionary mapping `axolotl` config keys to values.
        cli_args: Training-specific CLI arguments.
    """
-    # Enable expandable segments for cuda allocation to improve VRAM usage
-    set_pytorch_cuda_alloc_conf()
-
    print_axolotl_text_art()
    check_accelerate_default_config()
    if int(os.getenv("LOCAL_RANK", "0")) == 0:
--- a/src/axolotl/cli/utils.py
+++ b/src/axolotl/cli/utils.py
@@ -13,16 +13,11 @@ from typing import Any, Callable, Type, Union, get_args, get_origin
 import click
 import requests
 from pydantic import BaseModel
-from transformers import (
-    PreTrainedModel,
-    PreTrainedTokenizer,
-    PreTrainedTokenizerFast,
-    ProcessorMixin,
-)
+from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast

 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_model, load_processor, load_tokenizer
+from axolotl.utils.models import load_model, load_tokenizer

 configure_logging()
 LOG = logging.getLogger(__name__)
@@ -300,13 +295,9 @@ def load_model_and_tokenizer(
    *,
    cfg: DictDefault,
    inference: bool = False,
-) -> tuple[
-    PreTrainedModel,
-    PreTrainedTokenizer | PreTrainedTokenizerFast | Any,
-    ProcessorMixin | None,
-]:
+) -> tuple[PreTrainedModel, PreTrainedTokenizer | PreTrainedTokenizerFast | Any]:
    """
-    Helper function for loading a model, tokenizer, and processor specified in the given `axolotl`
+    Helper function for loading a model and tokenizer specified in the given `axolotl`
    config.

    Args:
@@ -314,7 +305,7 @@ def load_model_and_tokenizer(
        inference: Boolean denoting inference mode.

    Returns:
-        Tuple of (PreTrainedModel, PreTrainedTokenizer, ProcessorMixin).
+        `transformers` model and tokenizer.
    """
    LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
    tokenizer = load_tokenizer(cfg)
@@ -322,9 +313,4 @@ def load_model_and_tokenizer(
    LOG.info("loading model...")
    model, _ = load_model(cfg, tokenizer, inference=inference)

-    processor = None
-    if cfg.is_multimodal:
-        LOG.info("loading processor...")
-        processor = load_processor(cfg, tokenizer)
-
-    return model, tokenizer, processor
+    return model, tokenizer
--- a/src/axolotl/core/trainer_builder.py
+++ b/src/axolotl/core/trainer_builder.py
@@ -60,7 +60,6 @@ from axolotl.core.training_args import (
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback
-from axolotl.processing_strategies import get_processing_strategy
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.callbacks import (
    EvalFirstStepCallback,
@@ -748,12 +747,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                self.cfg.accelerator_config
            )

-        if self.cfg.image_size:
-            training_arguments_kwargs["image_size"] = self.cfg.image_size
-        if self.cfg.image_resize_algorithm:
-            training_arguments_kwargs["image_resize_algorithm"] = (
-                self.cfg.image_resize_algorithm
-            )
        if self.cfg.kd_ce_alpha is not None:
            training_arguments_kwargs["kd_ce_alpha"] = self.cfg.kd_ce_alpha
        if self.cfg.kd_alpha is not None:
@@ -897,13 +890,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
        else:
            if self.cfg.processor_type and self.processor:
                collator = MultiModalChatDataCollator
-                kwargs["processing_strategy"] = get_processing_strategy(
-                    self.processor,
-                    training_args.chat_template,
-                    self.cfg.chat_template,
-                    image_size=training_args.image_size,
-                    image_resize_algorithm=training_args.image_resize_algorithm,
-                )
+                kwargs["processor"] = self.processor
+                kwargs["chat_template"] = training_args.chat_template
            elif self.cfg.batch_flattening:
                collator = DataCollatorWithFlattening
                collator_args.pop(0)
--- a/src/axolotl/core/trainers/trl.py
+++ b/src/axolotl/core/trainers/trl.py
@@ -1,7 +1,5 @@
 """Module for TRL PPO trainer"""

-from typing import Literal, Union
-
 import torch
 from tqdm import tqdm
 from trl import (
@@ -81,78 +79,6 @@ class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):

    tag_names = ["axolotl", "orpo"]

-    def get_batch_loss_metrics(
-        self,
-        model,
-        batch: dict[str, Union[list, torch.LongTensor]],
-        train_eval: Literal["train", "eval"] = "train",
-    ):
-        """Compute the ORPO loss and other metrics for the given batch of inputs for train or test."""
-
-        # TODO remove once https://github.com/huggingface/trl/pull/3069 is included in a trl release
-
-        metrics = {}
-
-        forward_output = self.concatenated_forward(model, batch)
-        (
-            policy_chosen_logps,
-            policy_rejected_logps,
-            policy_chosen_logits,
-            policy_rejected_logits,
-            policy_nll_loss,
-        ) = forward_output[:5]
-        if self.aux_loss_enabled:
-            aux_loss = forward_output[5]
-
-        losses, chosen_rewards, rejected_rewards, log_odds_ratio, log_odds_chosen = (
-            self.odds_ratio_loss(policy_chosen_logps, policy_rejected_logps)
-        )
-        # full ORPO loss
-        loss = policy_nll_loss - losses.mean()
-
-        reward_accuracies = (chosen_rewards > rejected_rewards).float()
-
-        prefix = "eval_" if train_eval == "eval" else ""
-        metrics[f"{prefix}rewards/chosen"] = self.accelerator.gather_for_metrics(
-            chosen_rewards
-        ).mean()
-        metrics[f"{prefix}rewards/rejected"] = self.accelerator.gather_for_metrics(
-            rejected_rewards
-        ).mean()
-        metrics[f"{prefix}rewards/accuracies"] = self.accelerator.gather_for_metrics(
-            reward_accuracies
-        ).mean()
-        metrics[f"{prefix}rewards/margins"] = self.accelerator.gather_for_metrics(
-            chosen_rewards - rejected_rewards
-        ).mean()
-        metrics[f"{prefix}logps/rejected"] = (
-            self.accelerator.gather_for_metrics(policy_rejected_logps).detach().mean()
-        )
-        metrics[f"{prefix}logps/chosen"] = (
-            self.accelerator.gather_for_metrics(policy_chosen_logps).detach().mean()
-        )
-        metrics[f"{prefix}logits/rejected"] = self.accelerator.gather_for_metrics(
-            policy_rejected_logits.detach().mean()
-        ).mean()
-        metrics[f"{prefix}logits/chosen"] = self.accelerator.gather_for_metrics(
-            policy_chosen_logits.detach().mean()
-        ).mean()
-        metrics[f"{prefix}nll_loss"] = (
-            self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean()
-        )
-        metrics[f"{prefix}log_odds_ratio"] = (
-            self.accelerator.gather_for_metrics(log_odds_ratio).detach().mean()
-        )
-        metrics[f"{prefix}log_odds_chosen"] = (
-            self.accelerator.gather_for_metrics(log_odds_chosen).detach().mean()
-        )
-        for k, v in metrics.items():
-            metrics[k] = v.item()
-        if self.aux_loss_enabled:
-            loss += self.aux_loss_coef * aux_loss
-
-        return loss, metrics
-

 class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
    """
@@ -169,80 +95,6 @@ class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):

    tag_names = ["axolotl", "cpo"]

-    def get_batch_loss_metrics(
-        self,
-        model,
-        batch: dict[str, Union[list, torch.LongTensor]],
-        train_eval: Literal["train", "eval"] = "train",
-    ):
-        """Compute the CPO loss and other metrics for the given batch of inputs for train or test."""
-        metrics = {}
-
-        forward_output = self.concatenated_forward(model, batch)
-        (
-            policy_chosen_logps,
-            policy_rejected_logps,
-            policy_chosen_logits,
-            policy_rejected_logits,
-            policy_nll_loss,
-        ) = forward_output[:5]
-        if self.aux_loss_enabled:
-            aux_loss = forward_output[5]
-
-        losses, chosen_rewards, rejected_rewards = self.cpo_loss(
-            policy_chosen_logps,
-            policy_rejected_logps,
-        )
-
-        loss = losses.mean() + self.cpo_alpha * policy_nll_loss
-        reward_accuracies = (chosen_rewards > rejected_rewards).float()
-
-        prefix = "eval_" if train_eval == "eval" else ""
-        metrics[f"{prefix}rewards/chosen"] = (
-            self.accelerator.gather_for_metrics(chosen_rewards).mean().item()
-        )
-        metrics[f"{prefix}rewards/rejected"] = (
-            self.accelerator.gather_for_metrics(rejected_rewards).mean().item()
-        )
-        metrics[f"{prefix}rewards/accuracies"] = (
-            self.accelerator.gather_for_metrics(reward_accuracies).mean().item()
-        )
-        metrics[f"{prefix}rewards/margins"] = (
-            self.accelerator.gather_for_metrics(chosen_rewards - rejected_rewards)
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}logps/rejected"] = (
-            self.accelerator.gather_for_metrics(policy_rejected_logps)
-            .detach()
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}logps/chosen"] = (
-            self.accelerator.gather_for_metrics(policy_chosen_logps)
-            .detach()
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}logits/rejected"] = (
-            self.accelerator.gather_for_metrics(policy_rejected_logits.detach().mean())
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}logits/chosen"] = (
-            self.accelerator.gather_for_metrics(policy_chosen_logits.detach().mean())
-            .mean()
-            .item()
-        )
-        metrics[f"{prefix}nll_loss"] = (
-            self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean().item()
-        )
-
-        if self.aux_loss_enabled:
-            loss += self.aux_loss_coef * aux_loss
-
-        return loss, metrics
-

 class AxolotlRewardTrainer(SchedulerMixin, RewardTrainer):
    """
--- a/src/axolotl/core/training_args.py
+++ b/src/axolotl/core/training_args.py
@@ -5,7 +5,6 @@ extra axolotl specific training args
 from dataclasses import dataclass, field
 from typing import Optional

-from PIL.Image import Resampling
 from transformers import TrainingArguments
 from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig

@@ -213,20 +212,6 @@ class AxolotlTrainingMixins:
        metadata={"help": "The number of workers to use in sequence parallelism"},
    )

-    # multi-modal section
-
-    image_size: int | tuple[int, int] | None = field(
-        default=None,
-        metadata={"help": "The size of the image to resize to"},
-    )
-
-    image_resize_algorithm: Resampling | None = field(
-        default=None,
-        metadata={"help": "The algorithm to use for image resizing"},
-    )
-
-    # end of multi-modal section
-

@dataclass
 class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):
--- a/src/axolotl/integrations/cut_cross_entropy/README.md
+++ b/src/axolotl/integrations/cut_cross_entropy/README.md
@@ -1,6 +1,6 @@
 # Cut Cross Entropy

-Cut Cross Entropy (CCE) reduces VRAM usage through optimization on the cross-entropy operation during loss calculation.
+Cut Cross Entropy reduces VRAM usage through optimization on the cross-entropy operation during loss calculation.

 See https://github.com/apple/ml-cross-entropy

@@ -29,20 +29,6 @@ plugins:
 cut_cross_entropy: true
 ```

-## Supported Models
-
- llama
- phi3
- gemma
- gemma2
- gemma3
- gemma3_text
- mistral
- mistral3
- qwen2
- cohere
- cohere2
-
 ## Citation

 ```bib
--- a/src/axolotl/integrations/cut_cross_entropy/init.py
+++ b/src/axolotl/integrations/cut_cross_entropy/init.py
@@ -72,9 +72,7 @@ class CutCrossEntropyPlugin(BasePlugin):
        if cfg.cut_cross_entropy:
            self._check_requirements()

-            from axolotl.integrations.cut_cross_entropy.monkeypatch.patch import (
-                cce_patch,
-            )
+            from cut_cross_entropy.transformers import cce_patch

            with zero_only():
                LOG.info(
--- a/src/axolotl/integrations/cut_cross_entropy/monkeypatch/cohere.py
+++ b/src/axolotl/integrations/cut_cross_entropy/monkeypatch/cohere.py
@@ -1,201 +0,0 @@
-"""Cohere and Cohere2 CCE patch."""
-
-# This patch is based off transformers 4.50.0.
-# It patches the forward function for CohereForCausalLM and Cohere2ForCausalLM.
-# It scales the hidden states by the logit scale in advance instead of the logits as the
-# operation is done internally and should be mathematically equivalent.
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-from typing import Optional, Tuple, Union
-
-import torch
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-    apply_lce,
-)
-from transformers.cache_utils import Cache
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.models.cohere.modeling_cohere import (
-    _CONFIG_FOR_DOC,
-    COHERE_INPUTS_DOCSTRING,
-    KwargsForCausalLM,
-)
-from transformers.processing_utils import Unpack
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
-from transformers.utils.deprecation import deprecate_kwarg
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(COHERE_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
-def cce_forward(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **kwargs: Unpack[KwargsForCausalLM],
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-    Returns:
-
-    Example:
-
-    ```python
-    >> from transformers import AutoTokenizer, CohereForCausalLM
-
-    >> model = CohereForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01")
-    >> tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")
-
-    >> prompt = "Hey, are you conscious? Can you talk to me?"
-    >> inputs = tokenizer(prompt, return_tensors="pt")
-
-    >> # Generate
-    >> generate_ids = model.generate(inputs.input_ids, max_length=30)
-    >> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-    ```"""
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        **kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-    slice_indices = (
-        slice(-logits_to_keep, None)
-        if isinstance(logits_to_keep, int)
-        else logits_to_keep
-    )
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        # scale weight by logit_scale in-place of logits
-        loss = apply_lce(
-            hidden_states[:, slice_indices, :],
-            self.lm_head.weight * self.logit_scale,
-            labels,
-            _PATCH_OPTS,
-            **kwargs,
-        )
-    else:
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-        logits = logits * self.logit_scale  # main diff from Llama
-
-        if labels is not None:
-            loss = self.loss_function(
-                logits=logits,
-                labels=labels,
-                vocab_size=self.config.vocab_size,
-                **kwargs,
-            )
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-def patch_cohere(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.cohere import modeling_cohere
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_cohere.CohereForCausalLM
-        ), f"Expected a CohereForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_cohere.CohereForCausalLM.forward = cce_forward
-    return None
-
-
-def patch_cohere2(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.cohere2 import modeling_cohere2
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_cohere2.Cohere2ForCausalLM
-        ), f"Expected a Cohere2ForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_cohere2.Cohere2ForCausalLM.forward = cce_forward
-    return None
--- a/src/axolotl/integrations/cut_cross_entropy/monkeypatch/gemma.py
+++ b/src/axolotl/integrations/cut_cross_entropy/monkeypatch/gemma.py
@@ -1,175 +0,0 @@
-"""Gemma CCE patch"""
-
-# This patch is based off transformers 4.50.0.
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-from typing import Optional, Tuple, Union
-
-import torch
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-    apply_lce,
-)
-from transformers.cache_utils import Cache
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.models.gemma.modeling_gemma import (
-    _CONFIG_FOR_DOC,
-    GEMMA_INPUTS_DOCSTRING,
-    KwargsForCausalLM,
-)
-from transformers.processing_utils import Unpack
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
-from transformers.utils.deprecation import deprecate_kwarg
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
-def cce_forward(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **kwargs: Unpack[KwargsForCausalLM],
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from transformers import AutoTokenizer, GemmaForCausalLM
-
-    >>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b")
-    >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
-
-    >>> prompt = "What is your favorite condiment?"
-    >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    "What is your favorite condiment?"
-    ```"""
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        **kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-    slice_indices = (
-        slice(-logits_to_keep, None)
-        if isinstance(logits_to_keep, int)
-        else logits_to_keep
-    )
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        loss = apply_lce(
-            hidden_states[:, slice_indices, :],
-            self.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            **kwargs,
-        )
-    else:
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-        if labels is not None:
-            loss = self.loss_function(
-                logits=logits,
-                labels=labels,
-                vocab_size=self.config.vocab_size,
-                **kwargs,
-            )
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-def patch_gemma(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.gemma import modeling_gemma
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_gemma.GemmaForCausalLM
-        ), f"Expected a GemmaForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_gemma.GemmaForCausalLM.forward = cce_forward
-    return None
--- a/src/axolotl/integrations/cut_cross_entropy/monkeypatch/gemma3.py
+++ b/src/axolotl/integrations/cut_cross_entropy/monkeypatch/gemma3.py
@@ -1,465 +0,0 @@
-"""Gemma2 and Gemma3 (text and multimodal) CCE patch."""
-
-# Implementation originally adapted from https://github.com/apple/ml-cross-entropy/pull/29
-# and updated for transformers 4.50.0.
-# This is a modified version of the patch that allows for deferred logits calculation for gemma3 and works
-# with both gemma3 (text and multimodal) models.
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-from typing import Optional, Tuple, Union
-
-import torch
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-    apply_lce,
-)
-from torch import nn
-from transformers.cache_utils import Cache, HybridCache
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.models.gemma3.modeling_gemma3 import (
-    _CONFIG_FOR_DOC,
-    GEMMA3_INPUTS_DOCSTRING,
-    Gemma3CausalLMOutputWithPast,
-    logger,
-)
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    is_torchdynamo_compiling,
-    replace_return_docstrings,
-)
-from transformers.utils.deprecation import deprecate_kwarg
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
-def cce_forward(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[HybridCache] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    defer_logits_calculation: bool = False,
-    **loss_kwargs,
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-        defer_logits_calculation (`bool`, *optional*):
-            If `True`, defer logits calculation to the ConditionalGeneration forward. This is used to avoid the
-            memory overhead of calculating logits using regular lm_head forward pass and to use CCE.
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from transformers import AutoTokenizer, Gemma3ForCausalLM
-
-    >>> model = Gemma3ForCausalLM.from_pretrained("google/gemma-2-9b")
-    >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
-
-    >>> prompt = "What is your favorite condiment?"
-    >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    "What is your favorite condiment?"
-    ```"""
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        **loss_kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-    slice_indices = (
-        slice(-logits_to_keep, None)
-        if isinstance(logits_to_keep, int)
-        else logits_to_keep
-    )
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        if self.config.final_logit_softcapping is not None:
-            logger.warning_once(
-                "final_logit_softcapping is not supported for gemma3_text with CCE. Disabling."
-            )
-        loss = apply_lce(
-            hidden_states[:, slice_indices, :],
-            self.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            **loss_kwargs,
-        )
-    elif _PATCH_OPTS is not None and defer_logits_calculation:
-        # defer logits calculation to the ConditionalGeneration forward
-        logits = hidden_states[:, slice_indices, :]
-
-        if self.config.final_logit_softcapping is not None:
-            logger.warning_once(
-                "final_logit_softcapping is not supported for gemma3 with CCE. Disabling."
-            )
-    else:
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-        if self.config.final_logit_softcapping is not None:
-            logits = logits / self.config.final_logit_softcapping
-            logits = torch.tanh(logits)
-            logits = logits * self.config.final_logit_softcapping
-
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(GEMMA3_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=Gemma3CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
-def cce_forward_multimodal(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    pixel_values: torch.FloatTensor | None = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
-    token_type_ids: Optional[torch.LongTensor] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **lm_kwargs,
-) -> Union[Tuple, Gemma3CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from PIL import Image
-    >>> import requests
-    >>> from transformers import AutoProcessor, Gemma3ForConditionalGeneration
-
-    >>> model = Gemma3ForConditionalGeneration.from_pretrained("google/Gemma3-test-224px-hf")
-    >>> processor = AutoProcessor.from_pretrained("google/Gemma3-test-224px-hf")
-
-    >>> prompt = "answer en Where is the cow standing?"
-    >>> url = "https://huggingface.co/gv-hf/Gemma3-test-224px-hf/resolve/main/cow_beach_1.png"
-    >>> image = Image.open(requests.get(url, stream=True).raw)
-
-    >>> inputs = processor(images=image, text=prompt,  return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(**inputs, max_length=30)
-    >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    "answer en Where is the cow standing?\nbeach"
-    ```"""
-
-    if (input_ids is None) ^ (inputs_embeds is not None):
-        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    is_training = token_type_ids is not None and labels is not None
-
-    # Replace image id woth PAD if the image token if OOV, to avoid index-errors
-    if input_ids is not None and self.config.image_token_index >= self.vocab_size:
-        special_image_mask = input_ids == self.config.image_token_index
-        llm_input_ids = input_ids.clone()
-        llm_input_ids[special_image_mask] = 0
-    else:
-        llm_input_ids = input_ids  # type: ignore
-
-    if inputs_embeds is None:
-        inputs_embeds = self.get_input_embeddings()(llm_input_ids)
-
-    if cache_position is None:
-        past_seen_tokens = (
-            past_key_values.get_seq_length() if past_key_values is not None else 0  # type: ignore
-        )
-        cache_position = torch.arange(  # type: ignore
-            past_seen_tokens,
-            past_seen_tokens + inputs_embeds.shape[1],
-            device=inputs_embeds.device,
-        )
-
-    # Merge text and images
-    if pixel_values is not None:
-        image_features = self.get_image_features(pixel_values)
-
-        if input_ids is None:
-            special_image_mask = inputs_embeds == self.get_input_embeddings()(
-                torch.tensor(
-                    self.config.image_token_index,
-                    dtype=torch.long,
-                    device=inputs_embeds.device,
-                )
-            )
-        else:
-            special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(
-                -1
-            )
-            special_image_mask = special_image_mask.expand_as(inputs_embeds).to(
-                inputs_embeds.device
-            )
-
-        if (
-            not is_torchdynamo_compiling()
-            and inputs_embeds[special_image_mask].numel() != image_features.numel()
-        ):
-            image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0]
-            raise ValueError(
-                f"Number of images does not match number of special image tokens in the input text. "
-                f"Got {image_tokens_in_text} image tokens in the text but {image_features.shape[0] * image_features.shape[1]} "
-                "tokens from image embeddings."
-            )
-        image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
-        inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)  # type: ignore
-
-    # mask out pad-token-ids in labels for BC
-    if labels is not None and self.pad_token_id in labels:
-        logger.warning_once(
-            "`labels` contains `pad_token_id` which will be masked with `config.ignore_index`. "
-            "You have to mask out `pad_token_id` when preparing `labels`, this behavior will be removed in v.4.46.",
-        )
-        labels = torch.where(  # type: ignore
-            input_ids == self.pad_token_id, self.config.ignore_index, labels
-        )
-
-    causal_mask = self._update_causal_mask(  # pylint: disable=protected-access
-        attention_mask,
-        token_type_ids,
-        past_key_values,
-        cache_position,
-        inputs_embeds,
-        is_training,
-    )
-    outputs = self.language_model(
-        attention_mask=causal_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        logits_to_keep=logits_to_keep,
-        defer_logits_calculation=True,  # enable deferred logits calculation
-        **lm_kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        loss = apply_lce(
-            hidden_states,
-            self.language_model.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            **lm_kwargs,
-        )
-    else:
-        logits = hidden_states
-        if labels is not None:
-            # Upcast to float if we need to compute the loss to avoid potential precision issues
-            logits = logits.float()
-            shift_logits = logits[..., :-1, :]
-            shift_labels = labels[..., 1:]
-            if attention_mask is not None:
-                # we use the input attention mask to shift the logits and labels, because it is 2D.
-                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
-                shift_attention_mask = attention_mask[:, -shift_logits.shape[1] :].to(
-                    logits.device
-                )
-                shift_logits = shift_logits[
-                    shift_attention_mask.to(logits.device) != 0
-                ].contiguous()
-                shift_labels = shift_labels[
-                    shift_attention_mask.to(shift_labels.device) != 0
-                ].contiguous()
-            else:
-                shift_logits = shift_logits.contiguous()
-                shift_labels = shift_labels.contiguous()
-            # Flatten the tokens
-            loss_fct = nn.CrossEntropyLoss()
-
-            flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
-            flat_labels = shift_labels.view(-1).to(shift_logits.device)
-            loss = loss_fct(flat_logits, flat_labels)
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return Gemma3CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-        image_hidden_states=image_features if pixel_values is not None else None,
-    )
-
-
-def patch_gemma2(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.gemma2 import modeling_gemma2
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_gemma2.Gemma2ForCausalLM
-        ), f"Expected a Gemma2ForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_gemma2.Gemma2ForCausalLM.forward = cce_forward
-    return None
-
-
-def patch_gemma3_text(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.gemma3 import modeling_gemma3
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_gemma3.Gemma3ForCausalLM
-        ), f"Expected a Gemma3ForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_gemma3.Gemma3ForCausalLM.forward = cce_forward
-    return None
-
-
-def patch_gemma3(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.gemma3 import modeling_gemma3
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_gemma3.Gemma3ForConditionalGeneration
-        ), f"Expected a Gemma3ForConditionalGeneration model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
-
-        # patch the causal model to enable deferred logits calculation
-        maybe_model.language_model.forward = MethodType(
-            cce_forward, maybe_model.language_model
-        )
-        return maybe_model
-
-    modeling_gemma3.Gemma3ForConditionalGeneration.forward = cce_forward_multimodal
-    # patch the causal model to enable deferred logits calculation
-    modeling_gemma3.Gemma3ForCausalLM.forward = cce_forward
-    return None
--- a/src/axolotl/integrations/cut_cross_entropy/monkeypatch/mistral3.py
+++ b/src/axolotl/integrations/cut_cross_entropy/monkeypatch/mistral3.py
@@ -1,392 +0,0 @@
-"""Mistral and Mistral3 CCE patch."""
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-from typing import Optional, Tuple, Union
-
-import torch
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-    apply_lce,
-)
-from torch import nn
-from transformers.cache_utils import Cache
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.models.mistral3.modeling_mistral3 import (
-    Mistral3CausalLMOutputWithPast,
-)
-from transformers.models.mistral.modeling_mistral import (
-    _CONFIG_FOR_DOC,
-    MISTRAL_INPUTS_DOCSTRING,
-    KwargsForCausalLM,
-)
-from transformers.processing_utils import Unpack
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    is_torchdynamo_compiling,
-    replace_return_docstrings,
-)
-from transformers.utils.deprecation import deprecate_kwarg
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-)
-def cce_forward(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    attention_mask: Optional[torch.Tensor] | None = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    defer_logits_calculation: bool = False,
-    **kwargs: Unpack[KwargsForCausalLM],
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-        defer_logits_calculation (`bool`, *optional*):
-            If `True`, defer logits calculation to the ConditionalGeneration forward. This is used to avoid the
-            memory overhead of calculating logits using regular lm_head forward pass and to use CCE.
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from transformers import AutoTokenizer, MistralForCausalLM
-
-    >>> model = MistralForCausalLM.from_pretrained("meta-mistral/Mistral-2-7b-hf")
-    >>> tokenizer = AutoTokenizer.from_pretrained("meta-mistral/Mistral-2-7b-hf")
-
-    >>> prompt = "Hey, are you conscious? Can you talk to me?"
-    >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-    ```"""
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        **kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-    slice_indices = (
-        slice(-logits_to_keep, None)
-        if isinstance(logits_to_keep, int)
-        else logits_to_keep
-    )
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        loss = apply_lce(
-            hidden_states[:, slice_indices, :],
-            self.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            **kwargs,
-        )
-    elif _PATCH_OPTS is not None and defer_logits_calculation:
-        # defer logits calculation to the ConditionalGeneration forward
-        logits = hidden_states[:, slice_indices, :]
-    else:
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
-        if labels is not None:
-            loss = self.loss_function(
-                logits=logits,
-                labels=labels,
-                vocab_size=self.config.vocab_size,
-                **kwargs,
-            )
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-def cce_forward_multimodal(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    pixel_values: torch.FloatTensor | None = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[list[torch.FloatTensor]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    vision_feature_layer: Optional[Union[int, list[int]]] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    image_sizes: torch.Tensor | None = None,
-    **lm_kwargs,
-) -> Union[Tuple, Mistral3CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from PIL import Image
-    >>> import requests
-    >>> from transformers import AutoProcessor, Mistral3ForConditionalGeneration
-
-    >>> model = Mistral3ForConditionalGeneration.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
-    >>> processor = AutoProcessor.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
-
-    >>> prompt = "<s>[INST][IMG]What is the image?[/INST]"
-    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-    >>> image = Image.open(requests.get(url, stream=True).raw)
-
-    >>> inputs = processor(images=image, text=prompt, return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(**inputs, max_new_tokens=15)
-    >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    "What is the image?The image depicts two cats lying on a pink blanket."
-    ```"""
-
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-    vision_feature_layer = (
-        vision_feature_layer
-        if vision_feature_layer is not None
-        else self.config.vision_feature_layer
-    )
-
-    if (input_ids is None) ^ (inputs_embeds is not None):
-        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-    if pixel_values is not None and inputs_embeds is not None:
-        raise ValueError(
-            "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
-        )
-
-    if inputs_embeds is None:
-        inputs_embeds = self.get_input_embeddings()(input_ids)
-
-    if pixel_values is not None:
-        image_features = self.get_image_features(
-            pixel_values=pixel_values,
-            vision_feature_layer=vision_feature_layer,
-            image_sizes=image_sizes,
-        )
-
-        special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1)
-        special_image_mask = special_image_mask.expand_as(inputs_embeds).to(
-            inputs_embeds.device
-        )
-        if (
-            not is_torchdynamo_compiling()
-            and inputs_embeds[special_image_mask].numel() != image_features.numel()
-        ):
-            n_image_tokens = (input_ids == self.config.image_token_index).sum()
-            n_image_features = image_features.shape[0] * image_features.shape[1]
-            raise ValueError(
-                f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
-            )
-        image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
-        inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)  # type: ignore
-
-    outputs = self.language_model(
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        logits_to_keep=logits_to_keep,
-        defer_logits_calculation=True,  # enable deferred logits calculation
-        **lm_kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        loss = apply_lce(
-            hidden_states,
-            self.language_model.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            **lm_kwargs,
-        )
-    else:
-        logits = hidden_states
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            if attention_mask is not None:
-                # we use the input attention mask to shift the logits and labels, because it is 2D.
-                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
-                shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(
-                    logits.device
-                )
-                shift_logits = logits[..., :-1, :][
-                    shift_attention_mask.to(logits.device) != 0
-                ].contiguous()
-                shift_labels = labels[..., 1:][
-                    shift_attention_mask.to(labels.device) != 0
-                ].contiguous()
-            else:
-                shift_logits = logits[..., :-1, :].contiguous()
-                shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = nn.CrossEntropyLoss()
-            loss = loss_fct(
-                shift_logits.view(-1, shift_logits.size(-1)),
-                shift_labels.view(-1).to(shift_logits.device),
-            )
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return Mistral3CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-        image_hidden_states=image_features if pixel_values is not None else None,
-    )
-
-
-def patch_mistral(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.mistral import modeling_mistral
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_mistral.MistralForCausalLM
-        ), f"Expected a MistralForCausalLM model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward, maybe_model)
-        return maybe_model
-
-    modeling_mistral.MistralForCausalLM.forward = cce_forward
-    return None
-
-
-def patch_mistral3(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.mistral import modeling_mistral
-    from transformers.models.mistral3 import modeling_mistral3
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_mistral3.Mistral3ForConditionalGeneration
-        ), f"Expected a Mistral3ForConditionalGeneration model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
-
-        # patch the causal model to enable deferred logits calculation
-        maybe_model.language_model.forward = MethodType(
-            cce_forward, maybe_model.language_model
-        )
-        return maybe_model
-
-    modeling_mistral3.Mistral3ForConditionalGeneration.forward = cce_forward_multimodal
-    # patch the causal model to enable deferred logits calculation
-    modeling_mistral.MistralForCausalLM.forward = cce_forward
-    return None
--- a/src/axolotl/integrations/cut_cross_entropy/monkeypatch/mllama.py
+++ b/src/axolotl/integrations/cut_cross_entropy/monkeypatch/mllama.py
@@ -1,379 +0,0 @@
-"""Mllama CCE patch."""
-
-# pylint: disable=duplicate-code
-
-from types import MethodType
-from typing import Optional, Tuple, Union
-
-import torch
-import transformers
-from cut_cross_entropy.transformers.utils import (
-    PatchOptions,
-    TransformersModelT,
-    apply_lce,
-)
-from transformers.cache_utils import Cache
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.models.mllama.modeling_mllama import (
-    MLLAMA_INPUTS_DOCSTRING,
-    _prepare_cross_attention_mask,
-)
-from transformers.utils import (
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
-from transformers.utils.deprecation import deprecate_kwarg
-
-_PATCH_OPTS: PatchOptions | None = None
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(MLLAMA_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class="MllamaTextConfig"
-)
-def cce_forward(
-    self,
-    input_ids: torch.LongTensor | None = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    cross_attention_states: Optional[torch.LongTensor] = None,
-    cross_attention_mask: Optional[torch.LongTensor] = None,
-    full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    defer_logits_calculation: bool = False,
-    **loss_kwargs,
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-        defer_logits_calculation (`bool`, *optional*):
-            If `True`, defer logits calculation to the ConditionalGeneration forward. This is used to avoid the
-            memory overhead of calculating logits using regular lm_head forward pass and to use CCE.
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from transformers import AutoTokenizer, MllamaForCausalLM
-
-    >>> model = MllamaForCausalLM.from_pretrained("Llama-3.2-11B-Vision")
-    >>> tokenizer = AutoTokenizer.from_pretrained("Llama-3.2-11B-Vision")
-
-    >>> prompt = "If I had to write a haiku, it would be:"
-    >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-    >>> # Generate
-    >>> generate_ids = model.generate(inputs.input_ids, max_length=40, do_sample=True, temperature=0.6)
-    >>> result = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    >>> print(result)
-    If I had to write a haiku, it would be: "Snowflakes gently fall" - simple, yet peaceful.
-    I love the idea of snowflakes gently falling, each one
-    ```
-    """
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-    outputs = self.model(
-        input_ids=input_ids,
-        cross_attention_states=cross_attention_states,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        cross_attention_mask=cross_attention_mask,
-        full_text_row_masked_out_mask=full_text_row_masked_out_mask,
-        past_key_values=past_key_values,
-        inputs_embeds=inputs_embeds,
-        use_cache=use_cache,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-        cache_position=cache_position,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    slice_indices = (
-        slice(-logits_to_keep, None)
-        if isinstance(logits_to_keep, int)
-        else logits_to_keep
-    )
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        loss = apply_lce(
-            hidden_states[:, slice_indices, :],
-            self.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            **loss_kwargs,
-        )
-    elif _PATCH_OPTS is not None and defer_logits_calculation:
-        # defer logits calculation to the ConditionalGeneration forward
-        logits = hidden_states[:, slice_indices, :]
-    else:
-        logits = self.lm_head(hidden_states[:, slice_indices, :]).float()
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
-
-    if not return_dict:
-        output = (logits,) + outputs[1:]
-        return (loss,) + output if loss is not None else output
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
-@add_start_docstrings_to_model_forward(MLLAMA_INPUTS_DOCSTRING)
-@replace_return_docstrings(
-    output_type=CausalLMOutputWithPast, config_class="MllamaConfig"
-)
-def cce_forward_multimodal(
-    self,
-    input_ids: Optional[torch.LongTensor] = None,
-    pixel_values: Optional[torch.FloatTensor] = None,
-    aspect_ratio_mask: Optional[torch.Tensor] = None,
-    aspect_ratio_ids: Optional[torch.Tensor] = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    cross_attention_mask: Optional[torch.Tensor] = None,
-    cross_attention_states: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[list[torch.FloatTensor]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    labels: Optional[torch.LongTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    cache_position: Optional[torch.LongTensor] = None,
-    logits_to_keep: Union[int, torch.Tensor] = 0,
-    **loss_kwargs,
-) -> Union[Tuple, CausalLMOutputWithPast]:
-    r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        logits_to_keep (`int` or `torch.Tensor`, *optional*):
-            If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
-            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
-            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
-            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
-            This is useful when using packed tensor format (single dimension for batch and sequence length).
-
-
-    Returns:
-
-    Example:
-
-    ```python
-    >>> from PIL import Image
-    >>> import requests
-    >>> from transformers import AutoProcessor, MllamaForConditionalGeneration
-
-    >>> checkpoint = "meta-llama/Llama-3.2-11B-Vision"
-    >>> model = MllamaForConditionalGeneration.from_pretrained(checkpoint)
-    >>> processor = AutoProcessor.from_pretrained(checkpoint)
-
-    >>> prompt = "<|image|>If I had to write a haiku for this one"
-    >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
-    >>> image = Image.open(requests.get(url, stream=True).raw)
-
-    >>> inputs = processor(text=prompt, images=image, return_tensors="pt")
-
-    >>> # Generate
-    >>> output = model.generate(**inputs, max_new_tokens=15)
-
-    >>> prompt_len = inputs.input_ids.shape[-1]
-    >>> generated_ids = output[:, prompt_len:]
-    >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
-    >>> print(generated_text)
-    [', it would be:.\\nA stop sign in Chinatown.\\n']
-    ```
-    """
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    if (input_ids is None) ^ (inputs_embeds is not None):
-        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-    if pixel_values is not None and inputs_embeds is not None:
-        raise ValueError(
-            "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
-        )
-
-    if pixel_values is not None and cross_attention_states is not None:
-        raise ValueError(
-            "`pixel_values` and `cross_attention_states` cannot be provided simultaneously"
-        )
-
-    if pixel_values is not None:
-        if aspect_ratio_ids is None:
-            raise ValueError(
-                "`aspect_ratio_ids` must be provided if `pixel_values` is provided"
-            )
-        # get vision tokens from vision model
-        vision_outputs = self.vision_model(
-            pixel_values=pixel_values,
-            aspect_ratio_ids=aspect_ratio_ids,
-            aspect_ratio_mask=aspect_ratio_mask,
-            output_hidden_states=output_hidden_states,
-            output_attentions=output_attentions,
-            return_dict=return_dict,
-        )
-        cross_attention_states = vision_outputs[0]
-        cross_attention_states = self.multi_modal_projector(
-            cross_attention_states
-        ).reshape(
-            -1, cross_attention_states.shape[-2], self.hidden_size  # type: ignore
-        )
-
-    if cross_attention_mask is not None:
-        cross_attention_mask, full_text_row_masked_out_mask = (
-            _prepare_cross_attention_mask(
-                cross_attention_mask,
-                num_vision_tokens=self.vision_model.num_patches,
-                dtype=self.dtype,
-            )
-        )
-    else:
-        full_text_row_masked_out_mask = None
-
-    if cross_attention_mask is not None and cache_position is not None:
-        cross_attention_mask = cross_attention_mask[:, :, cache_position]
-        full_text_row_masked_out_mask = full_text_row_masked_out_mask[
-            :, :, cache_position
-        ]
-
-    outputs = self.language_model(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        cross_attention_states=cross_attention_states,
-        cross_attention_mask=cross_attention_mask,
-        full_text_row_masked_out_mask=full_text_row_masked_out_mask,
-        past_key_values=past_key_values,
-        use_cache=use_cache,
-        inputs_embeds=inputs_embeds,
-        output_hidden_states=output_hidden_states,
-        output_attentions=output_attentions,
-        return_dict=return_dict,
-        cache_position=cache_position,
-        logits_to_keep=logits_to_keep,
-        defer_logits_calculation=True,  # enable deferred logits calculation
-        **loss_kwargs,
-    )
-
-    hidden_states = outputs[0]
-    loss = None
-    logits = None
-
-    if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
-        assert labels is not None
-        loss = apply_lce(
-            hidden_states,
-            self.language_model.lm_head.weight,
-            labels,
-            _PATCH_OPTS,
-            **loss_kwargs,
-        )
-    else:
-        # Temporary fix to calculate the loss in main class, as the model's vocab size may be resized
-        logits = hidden_states
-
-        if labels is not None:
-            loss = self.loss_function(
-                logits, labels, self.config.get_text_config().vocab_size, **loss_kwargs
-            )
-
-    if not return_dict:
-        return (loss,) + outputs if loss is not None else outputs
-
-    return CausalLMOutputWithPast(
-        loss=loss,
-        logits=outputs.logits,
-        past_key_values=outputs.past_key_values,
-        hidden_states=outputs.hidden_states,
-        attentions=outputs.attentions,
-    )
-
-
-def patch_mllama(
-    maybe_model: TransformersModelT | str | transformers.PretrainedConfig,
-    patch_options: PatchOptions,
-) -> TransformersModelT | None:
-
-    global _PATCH_OPTS  # pylint: disable=global-statement
-    from transformers.models.mllama import modeling_mllama
-
-    _PATCH_OPTS = patch_options
-
-    if isinstance(maybe_model, transformers.PreTrainedModel):
-        assert isinstance(
-            maybe_model, modeling_mllama.MllamaForConditionalGeneration
-        ), f"Expected a MllamaForConditionalGeneration model. Got {type(maybe_model)}."
-        maybe_model.forward = MethodType(cce_forward_multimodal, maybe_model)
-
-        # patch the language model
-        maybe_model.language_model.forward = MethodType(
-            cce_forward, maybe_model.language_model
-        )
-        return maybe_model
-
-    modeling_mllama.MllamaForConditionalGeneration.forward = cce_forward_multimodal
-
-    # patch the causal language model
-    modeling_mllama.MllamaForCausalLM.forward = cce_forward
-    return None
--- a/src/axolotl/integrations/cut_cross_entropy/monkeypatch/patch.py
+++ b/src/axolotl/integrations/cut_cross_entropy/monkeypatch/patch.py
@@ -1,85 +0,0 @@
-# Copyright (C) 2024 Apple Inc. All Rights Reserved.
-
-"""Cut Cross Entropy patcher"""
-
-import transformers
-from cut_cross_entropy.cce_utils import LinearCrossEntropyImpl
-from cut_cross_entropy.linear_cross_entropy import LCE_IMPL_DEFAULT
-from cut_cross_entropy.transformers.llama import patch_llama
-from cut_cross_entropy.transformers.phi3 import patch_phi3
-from cut_cross_entropy.transformers.qwen2 import patch_qwen2
-from cut_cross_entropy.transformers.utils import PatchOptions, TransformersModelT
-
-from axolotl.integrations.cut_cross_entropy.monkeypatch.cohere import (
-    patch_cohere,
-    patch_cohere2,
-)
-from axolotl.integrations.cut_cross_entropy.monkeypatch.gemma import patch_gemma
-from axolotl.integrations.cut_cross_entropy.monkeypatch.gemma3 import (
-    patch_gemma2,
-    patch_gemma3,
-    patch_gemma3_text,
-)
-from axolotl.integrations.cut_cross_entropy.monkeypatch.mistral3 import (
-    patch_mistral,
-    patch_mistral3,
-)
-from axolotl.integrations.cut_cross_entropy.monkeypatch.mllama import patch_mllama
-
-CUT_CROSS_ENTROPY_MODEL_MAPPING = {
-    "llama": patch_llama,
-    "mllama": patch_mllama,
-    "phi3": patch_phi3,
-    "gemma": patch_gemma,
-    "gemma2": patch_gemma2,
-    "gemma3": patch_gemma3,
-    "gemma3_text": patch_gemma3_text,
-    "mistral": patch_mistral,
-    "mistral3": patch_mistral3,
-    "qwen2": patch_qwen2,
-    "cohere": patch_cohere,
-    "cohere2": patch_cohere2,
-}
-
-
-def cce_patch(
-    model_type_or_model: str | TransformersModelT | transformers.PretrainedConfig,
-    impl: str | LinearCrossEntropyImpl = LCE_IMPL_DEFAULT,
-    reduction: str = "mean",
-    filter_eps: float | str | None = "auto",
-    accum_e_fp32: bool = False,
-    accum_c_fp32: bool = False,
-    filter_e_grad: bool = True,
-    filter_c_grad: bool = True,
-    train_only: bool = False,
-) -> TransformersModelT | None:
-    if isinstance(impl, LinearCrossEntropyImpl):
-        impl = impl.name.lower()
-
-    if impl not in (v.name.lower() for v in LinearCrossEntropyImpl):
-        raise ValueError(f"Unknown {impl=}")
-
-    if isinstance(model_type_or_model, transformers.PreTrainedModel):
-        model_type = model_type_or_model.config.model_type
-    elif isinstance(model_type_or_model, transformers.PretrainedConfig):
-        model_type = model_type_or_model.model_type
-    else:
-        model_type = model_type_or_model
-
-    patch_options = PatchOptions(
-        impl=impl,
-        reduction=reduction,
-        filter_eps=filter_eps,
-        accum_e_fp32=accum_e_fp32,
-        accum_c_fp32=accum_c_fp32,
-        filter_e_grad=filter_e_grad,
-        filter_c_grad=filter_c_grad,
-        train_only=train_only,
-    )
-
-    if model_type in CUT_CROSS_ENTROPY_MODEL_MAPPING:
-        return CUT_CROSS_ENTROPY_MODEL_MAPPING[model_type](
-            model_type_or_model, patch_options
-        )
-
-    raise RuntimeError(f"Unknown model type {model_type}")
--- a/src/axolotl/integrations/liger/init.py
+++ b/src/axolotl/integrations/liger/init.py
@@ -114,5 +114,3 @@ class LigerPlugin(BasePlugin):
                modeling_mod.CrossEntropyLoss = LigerCrossEntropyLoss
            if cfg.liger_fused_linear_cross_entropy:
                modeling_mod.DeepseekV2ForCausalLM.forward = deepseekv2_lce_forward
-        elif cfg.model_config_type in ["gemma3_text", "deepseek_v3"]:
-            raise ValueError(f"Unsupported model config type: {cfg.model_config_type}")
--- a/src/axolotl/monkeypatch/attention/ring_attn.py
+++ b/src/axolotl/monkeypatch/attention/ring_attn.py
@@ -27,7 +27,7 @@ def get_ring_attn_group() -> dist.ProcessGroup:
    return RING_ATTN_GROUP


-def set_ring_attn_group(ring_attn_group: dist.ProcessGroup | None):
+def set_ring_attn_group(ring_attn_group: dist.ProcessGroup):
    """
    Setter for ring attention group on this rank.

--- a/src/axolotl/monkeypatch/multipack.py
+++ b/src/axolotl/monkeypatch/multipack.py
@@ -22,9 +22,6 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
    "phi3",
    "gemma",
    "gemma2",
-    "gemma3_text",
-    "cohere",
-    "cohere2",
    "gemmoe",
    "starcoder2",
    "deepseek_v2",
--- a/src/axolotl/processing_strategies.py
+++ b/src/axolotl/processing_strategies.py
@@ -1,313 +0,0 @@
-"""Module containing ProcessingStrategy classes and its derivative for different MultiModal Model types"""
-
-import ast
-from copy import deepcopy
-from typing import Optional
-
-from PIL import Image, ImageOps
-from PIL.Image import Resampling
-from torch import Tensor
-from transformers import ProcessorMixin
-from transformers.image_utils import load_image
-
-
-class ProcessingStrategy:
-    """Base Processing Strategy class"""
-
-    def __init__(
-        self,
-        processor: ProcessorMixin,
-        chat_template: Optional[str] = None,
-        image_size: int | tuple[int, int] | None = None,
-        image_resize_algorithm: Resampling | None = None,
-    ):
-        self.processor = processor
-        self.chat_template = chat_template
-        self.image_token = None
-        self.image_token_id = None
-
-        self.image_size = image_size
-        self.image_resize_algorithm = (
-            image_resize_algorithm or Image.Resampling.BILINEAR
-        )
-
-        if hasattr(processor, "image_token"):
-            self.image_token = processor.image_token
-            self.image_token_id = processor.tokenizer.convert_tokens_to_ids(
-                self.image_token
-            )
-
-    def __call__(self, examples: list[dict]) -> list[dict]:
-        """
-        Preprocess conversation examples to ensure consistent format.
-        Converts different conversation formats to OpenAI format with 'messages'.
-        Supports two formats:
-        1. OpenAI format with 'messages'
-        2. Legacy format with 'conversations'
-
-        Args:
-            examples: list of conversation dictionaries
-
-        Returns:
-            list of dicts in OpenAI format with 'messages' key
-
-        Raises:
-            ValueError: If the conversation format is not supported
-        """
-        role_mapping = {
-            "human": "user",
-            "gpt": "assistant",
-        }
-
-        def normalize_role(role: str) -> str:
-            """Normalize role names to OpenAI format. Default to original role if not found."""
-            return role_mapping.get(role, role)
-
-        def convert_legacy_format(example: dict) -> dict:
-            """Convert legacy 'conversations' format to OpenAI 'messages' format."""
-            messages = [
-                {"role": normalize_role(convo["from"]), "content": convo["value"]}
-                for convo in example["conversations"]
-            ]
-
-            # Create new dict without 'conversations' key
-            result = deepcopy(example)
-            result.pop("conversations")
-            result["messages"] = messages
-            return result
-
-        def convert_multiple_choice_to_multimedia_messages(
-            messages: dict,
-        ) -> list[dict]:
-
-            def construct_prompt(sample):
-                question = sample["question"]
-                options = sample["options"]
-                if isinstance(options, str):
-                    options = ast.literal_eval(options)
-
-                example = ""
-                start_chr = "A"
-                prediction_range = []
-                index2ans = {}
-                for option in options:
-                    prediction_range.append(start_chr)
-                    example += f"({start_chr}) {option}\n"
-                    index2ans[start_chr] = option
-                    start_chr = chr(ord(start_chr) + 1)
-
-                empty_prompt_sample_structure = "{}\n\n{}\n\nAnswer with the option's letter from the given choices directly."
-                empty_prompt = empty_prompt_sample_structure.format(question, example)
-
-                return empty_prompt
-
-            new_messages = []
-
-            user_content = construct_prompt(messages)
-            assistant_response = messages["answer"]
-
-            new_messages.append(
-                {"role": "user", "content": [{"type": "text", "text": user_content}]}
-            )
-
-            new_messages.append(
-                {
-                    "role": "assistant",
-                    "content": [{"type": "text", "text": assistant_response}],
-                }
-            )
-
-            return new_messages
-
-        def convert_messages_to_multimedia_messages(messages: list[dict]) -> list[dict]:
-            """Convert regular messages format to Messages format with content type"""
-
-            new_messages = []
-            for message in messages:
-                if isinstance(message["content"], str):
-                    new_messages.append(
-                        {
-                            "role": message["role"],
-                            "content": [
-                                {
-                                    "type": "text",
-                                    "text": message["content"],
-                                }
-                            ],
-                        }
-                    )
-                elif isinstance(message["content"], list):
-                    content = message["content"]
-
-                    new_messages.append(
-                        {
-                            "role": message["role"],
-                            "content": content,
-                        }
-                    )
-
-            return new_messages
-
-        processed_examples = []
-        for example in examples:
-            if not (
-                "messages" in example
-                or "conversations" in example
-                or "question" in example
-            ):
-                raise ValueError(
-                    "Only `messages`, `conversations`, and `question` message keys are currently supported."
-                )
-
-            processed_example = None
-            if "messages" in example:  # OpenAI format
-                processed_example = example
-                # convert regular messages format to Messages format with content type
-                # for compatibility with apply_chat_template
-                processed_example["messages"] = convert_messages_to_multimedia_messages(
-                    processed_example["messages"]
-                )
-            elif "question" in example:  # Multiple choice format
-                processed_example = {}
-                processed_example["messages"] = (
-                    convert_multiple_choice_to_multimedia_messages(example)
-                )
-            else:  # Legacy format
-                processed_example = convert_legacy_format(example)
-                processed_example["messages"] = convert_messages_to_multimedia_messages(
-                    processed_example["messages"]
-                )
-
-            # find the image key if it exists
-
-            image_keys = []
-            for key in example.keys():
-                if "image" in key:
-                    image_keys.append(key)
-
-            for im_key in image_keys:
-                if example[im_key] is None:
-                    continue
-                if isinstance(example[im_key], list):
-                    if len(example[im_key]) == 0:
-                        continue
-                    image_value = example[im_key][0]
-                else:
-                    image_value = example[im_key]
-
-                image_value = load_image(image_value)
-
-                if self.image_size is not None:
-                    assert hasattr(
-                        image_value, "resize"
-                    ), "Image does not have a resize method"
-
-                    if isinstance(self.image_size, tuple):
-                        image_value = image_value.resize(
-                            self.image_size, self.image_resize_algorithm
-                        )
-                    else:
-                        # Set the padding value; here we use black (0, 0, 0) for RGB images
-                        padding_color = (0, 0, 0)
-
-                        # When image_size is an int (square target), preserve aspect ratio then pad
-                        # This is to prevent aspect ratio distortion when resizing to square
-                        image_value = ImageOps.pad(
-                            image_value,
-                            (self.image_size, self.image_size),
-                            method=self.image_resize_algorithm,
-                            color=padding_color,
-                        )
-
-                processed_example["messages"][0]["content"].append(
-                    {
-                        "type": "image",
-                        "image": image_value,
-                    }
-                )
-
-            processed_examples.append(processed_example)
-
-        return processed_examples
-
-    def process_labels(self, input_ids: Tensor) -> Tensor:
-        labels = input_ids.clone()
-
-        # The labels are the input_ids, and we mask the padding tokens in the loss computation
-        labels[labels == self.processor.tokenizer.pad_token_id] = -100
-
-        # Ignore the image token index in the loss computation (model specific)
-        labels[labels == self.image_token_id] = -100
-
-        return labels
-
-
-class Qwen2VLProcessingStrategy(ProcessingStrategy):
-    """Processing Strategy class for Qwen2-VL"""
-
-    def __init__(
-        self,
-        processor: ProcessorMixin,
-        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)
-        self.image_token = "<|image_pad|>"  # nosec
-        self.image_token_id = processor.tokenizer.convert_tokens_to_ids(
-            self.image_token
-        )
-
-
-class Gemma3ProcessingStrategy(ProcessingStrategy):
-    """Processing Strategy class for Gemma3"""
-
-    def __init__(
-        self,
-        processor: ProcessorMixin,
-        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)
-        self.image_token = processor.tokenizer.special_tokens_map["boi_token"]
-        self.image_token_id = processor.tokenizer.convert_tokens_to_ids(
-            self.image_token
-        )
-
-    def process_labels(self, input_ids):
-        labels = input_ids.clone()
-
-        # Follows https://ai.google.dev/gemma/docs/core/huggingface_vision_finetune_qlora
-        labels[labels == self.processor.tokenizer.pad_token_id] = -100
-        labels[labels == self.image_token_id] = -100
-        labels[labels == 262144] = -100  # corresponds to <image_soft_token>
-
-        return labels
-
-
-def get_processing_strategy(
-    processor: ProcessorMixin,
-    chat_template,
-    chat_template_type,
-    image_size: int | tuple[int, int] | None = None,
-    image_resize_algorithm: Resampling | None = None,
-):
-    if chat_template_type == "qwen2_vl":
-        return Qwen2VLProcessingStrategy(
-            processor, chat_template, image_size, image_resize_algorithm
-        )
-    if chat_template_type == "gemma3":
-        return Gemma3ProcessingStrategy(
-            processor, chat_template, image_size, image_resize_algorithm
-        )
-    if chat_template_type in [
-        "llama3_2_vision",
-        "llava",
-        "mistral_v7_tekken",
-        "pixtral",
-    ]:
-        return ProcessingStrategy(
-            processor, chat_template, image_size, image_resize_algorithm
-        )
-    raise ValueError(f"Unsupported chat template type: {chat_template_type}")
--- a/src/axolotl/train.py
+++ b/src/axolotl/train.py
@@ -314,7 +314,6 @@ def save_initial_configs(
    tokenizer: PreTrainedTokenizer,
    model: PreTrainedModel,
    peft_config: PeftConfig | None,
-    processor: ProcessorMixin | None,
 ):
    """
    Save initial configurations before training.
@@ -342,10 +341,6 @@ def save_initial_configs(
        LOG.info(f"Pre-saving model config to {cfg.output_dir}...")
        model.config.save_pretrained(str(output_dir))

-    if processor:
-        LOG.info(f"Pre-saving processor to {cfg.output_dir}...")
-        processor.save_pretrained(str(output_dir))
-

 def setup_model_card(cfg: DictDefault):
    """
@@ -413,7 +408,6 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
    PeftModel | PreTrainedModel,
    PreTrainedTokenizer,
    PeftConfig | None,
-    ProcessorMixin | None,
 ]:
    """
    Load model, tokenizer, trainer, etc. Helper function to encapsulate the full
@@ -429,7 +423,6 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
            - Model
            - Tokenizer
            - PEFT config
-            - Processor
    """
    # Load tokenizer, processor and model
    model, tokenizer, peft_config, processor = setup_model_and_tokenizer(cfg)
@@ -460,7 +453,6 @@ def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) ->
        model,
        tokenizer,
        peft_config,
-        processor,
    )


@@ -483,7 +475,6 @@ def train(
        model,
        tokenizer,
        peft_config,
-        processor,
    ) = setup_model_and_trainer(cfg, dataset_meta)

    # Determine if we need to resume from a checkpoint
@@ -499,7 +490,7 @@ def train(
    )

    # Save initial configs
-    save_initial_configs(cfg, tokenizer, model, peft_config, processor)
+    save_initial_configs(cfg, tokenizer, model, peft_config)

    # Set up signal handler for graceful termination
    setup_signal_handler(cfg, model, safe_serialization)
--- a/src/axolotl/utils/chat_templates.py
+++ b/src/axolotl/utils/chat_templates.py
--- a/src/axolotl/utils/collators/mm_chat.py
+++ b/src/axolotl/utils/collators/mm_chat.py
@@ -2,17 +2,15 @@
 Collators for multi-modal chat messages and packing
 """

+from copy import deepcopy
 from dataclasses import dataclass
 from typing import Any, Optional, Union

-import torch
-from torch import Tensor
-from transformers import PreTrainedTokenizerBase
+from PIL import Image
+from transformers import PreTrainedTokenizerBase, ProcessorMixin
 from transformers.data.data_collator import DataCollatorMixin
 from transformers.utils import PaddingStrategy

-from axolotl.processing_strategies import ProcessingStrategy
-

@dataclass
 class MultiModalChatDataCollator(DataCollatorMixin):
@@ -21,9 +19,11 @@ class MultiModalChatDataCollator(DataCollatorMixin):
    """

    tokenizer: PreTrainedTokenizerBase
-    processing_strategy: ProcessingStrategy
-    packing: bool = False
+    processor: ProcessorMixin
    return_tensors: str = "pt"
+    chat_template: Optional[str] = None
+    packing: bool = False
+    max_images: int = -1
    padding: Union[bool, str, PaddingStrategy] = True
    pad_to_multiple_of: Optional[int] = None

@@ -31,62 +31,162 @@ class MultiModalChatDataCollator(DataCollatorMixin):
        if self.packing:
            raise ValueError("Packing is currently not supported.")

-    def torch_call(self, examples: list[dict]) -> dict[str, Any]:
-        return self.process_rows(examples)
+    def torch_call(
+        self, examples: list[Union[list[int], Any, dict[str, Any]]]
+    ) -> dict[str, Any]:
+        # Handle dict or lists with proper padding and conversion to tensor.
+
+        return self.__class__.process_rows(
+            examples, self.processor, self.chat_template, self.max_images
+        )
+
+    @staticmethod
+    def process_rows(examples, processor, chat_template, max_images, length_only=False):
+        # HINT: use `_torch_collate_batch` to stack and pad tensors
+        # see also DataCollatorWithFlattening and DefaultDataCollator
+
+        # *** This is COPIED from the trl example sft_vlm.py code ***
+        # use this as a starting point
+
+        def _preprocess(examples: list[dict]) -> list[dict]:
+            """
+            Preprocess conversation examples to ensure consistent format.
+
+            Converts different conversation formats to OpenAI format with 'messages'.
+            Supports two formats:
+            1. OpenAI format with 'messages'
+            2. Legacy format with 'conversations'
+
+            Args:
+                examples: list of conversation dictionaries
+
+            Returns:
+                dict in OpenAI format with 'messages' key
+
+            Raises:
+                ValueError: If the conversation format is not supported
+            """
+            role_mapping = {
+                "human": "user",
+                "gpt": "assistant",
+            }
+
+            def normalize_role(role: str) -> str:
+                """Normalize role names to OpenAI format. Default to original role if not found."""
+                return role_mapping.get(role, role)
+
+            def convert_legacy_format(example: dict) -> dict:
+                """Convert legacy 'conversations' format to OpenAI 'messages' format."""
+                messages = [
+                    {
+                        "role": normalize_role(convo["from"]),
+                        "content": convo["value"],
+                    }
+                    for convo in example["conversations"]
+                ]
+
+                # Create new dict without 'conversations' key
+                result = deepcopy(example)
+                result.pop("conversations")
+                return {"messages": messages, **result}
+
+            processed_examples = []
+            for example in examples:
+                # OpenAI format
+                if "messages" in example:
+                    processed_examples.append(example)
+
+                # Legacy format
+                elif "conversations" in example:
+                    processed_examples.append(convert_legacy_format(example))
+
+                else:
+                    raise ValueError(
+                        "Only `messages` and `conversations` message keys are currently supported."
+                    )
+
+            return processed_examples
+
+        def _process_images(examples, max_images):
+            """
+            Process images from examples, ensuring consistency in image presence and applying max_images limit.
+
+            Args:
+                examples: List of dictionaries that may contain 'images' key
+                max_images: Maximum number of images to keep per example (0 means no limit)
+
+            Returns:
+                Either None (if no images) or List[Image objects] (if all examples have images)
+
+            Raises:
+                ValueError: If there's a mix of None and non-None images
+            """
+
+            def get_image(example):
+                if "images" not in example:
+                    return None
+                images = example["images"]
+                if isinstance(images, str):
+                    return Image.open(images)
+                return images
+
+            images = [get_image(example) for example in examples]
+
+            # Count None and non-None images
+            none_count = sum(1 for img in images if img is None)
+
+            # All images are None
+            if none_count == len(images):
+                return None
+
+            # Mix of None and non-None images
+            if none_count > 0:
+                raise ValueError(
+                    "All images should be either None or not None. "
+                    "Please provide images for all examples or None."
+                )
+
+            # Apply max_images limit if specified
+            if max_images > 0:
+                images = [
+                    (
+                        img_batch[:max_images]
+                        if isinstance(img_batch, (list, tuple))
+                        else img_batch
+                    )
+                    for img_batch in images
+                ]
+
+            return images

-    def process_rows(
-        self,
-        examples: list[dict],
-    ) -> dict[str, Tensor]:
        # Preprocess the examples
-        examples = self.processing_strategy(examples)
+        examples = _preprocess(examples)

-        # Initialize batch
-        batch: dict[str, Any] = {}
-
-        # Process each example
-        for example in examples:
-            # Apply chat template to process the example
-            # This method requires transformers>=4.49.0
-            result = self.processing_strategy.processor.apply_chat_template(
-                example["messages"],
-                add_generation_prompt=True,
-                tokenize=True,
-                return_tensors="pt",
-                padding=True,
-                return_dict=True,
-                chat_template=self.processing_strategy.chat_template,
+        # Get the texts and images, and apply the chat template
+        texts = [
+            processor.apply_chat_template(
+                example["messages"], chat_template=chat_template, tokenize=False
            )
+            for example in examples
+        ]

-            # TODO: Check if need handling for len(input_ids) > sequence_len
+        images = _process_images(examples, max_images=max_images)

-            # Add the processed tensors to our batch
-            for key in result.keys():
-                if key not in batch:
-                    batch[key] = []
+        # Tokenize the texts and process the images
+        batch = processor(text=texts, images=images, return_tensors="pt", padding=True)

-                batch[key].append(result[key].squeeze(0))
-
-        # Pad sequences to the same length
-        input_ids = torch.nn.utils.rnn.pad_sequence(
-            batch["input_ids"],
-            batch_first=True,
-            padding_value=self.tokenizer.pad_token_id,
+        # The labels are the input_ids, and we mask the padding tokens in the loss computation
+        labels = batch["input_ids"].clone()
+        labels[labels == processor.tokenizer.pad_token_id] = -100  #
+        # Ignore the image token index in the loss computation (model specific)
+        image_token_id = processor.tokenizer.convert_tokens_to_ids(
+            processor.image_token
        )
+        labels[labels == image_token_id] = -100
+        batch["labels"] = labels

-        attention_mask = torch.nn.utils.rnn.pad_sequence(
-            batch["attention_mask"], batch_first=True, padding_value=0
-        )
-
-        # Create the final batch
-        final_batch = {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-        }
-
-        # Process the labels
-        final_batch["labels"] = self.processing_strategy.process_labels(
-            final_batch["input_ids"]
-        )
-
-        return final_batch
+        if length_only:
+            return {
+                "length": [len(sample["input_ids"]) for sample in batch["input_ids"]]
+            }
+        return batch
--- a/src/axolotl/utils/config/init.py
+++ b/src/axolotl/utils/config/init.py
@@ -13,7 +13,7 @@ from axolotl.integrations.base import PluginManager
 from axolotl.integrations.config import merge_input_args
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import MULTIMODAL_AUTO_MODEL_MAPPING, load_model_config
+from axolotl.utils.models import load_model_config
 from axolotl.utils.schemas.config import (
    AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
 )
@@ -158,7 +158,7 @@ def normalize_config(cfg):

    cfg.is_multimodal = (
        hasattr(model_config, "model_type")
-        and model_config.model_type in MULTIMODAL_AUTO_MODEL_MAPPING
+        and model_config.model_type in ["llava", "mllama"]
        or any(
            multimodal_name in cfg.base_model.lower()
            for multimodal_name in [
@@ -171,6 +171,7 @@ def normalize_config(cfg):
        cfg.processor_config = (
            cfg.processor_config or cfg.base_model_config or cfg.base_model
        )
+        model_config = model_config.text_config

    cfg.model_config_type = model_config.model_type

--- a/src/axolotl/utils/models.py
+++ b/src/axolotl/utils/models.py
@@ -34,16 +34,12 @@ from transformers import (  # noqa: F401
    AutoTokenizer,
    AwqConfig,
    BitsAndBytesConfig,
-    Gemma3ForConditionalGeneration,
    GPTQConfig,
    LlavaForConditionalGeneration,
-    Mistral3ForConditionalGeneration,
    MllamaForConditionalGeneration,
    PreTrainedModel,
    PreTrainedTokenizerBase,
    ProcessorMixin,
-    Qwen2_5_VLForConditionalGeneration,
-    Qwen2VLForConditionalGeneration,
 )
 from transformers.integrations.deepspeed import (
    HfTrainerDeepSpeedConfig,
@@ -73,13 +69,9 @@ from axolotl.utils.model_shard_quant import load_sharded_model, load_sharded_mod

 LOG = logging.getLogger(__name__)

-MULTIMODAL_AUTO_MODEL_MAPPING = {
-    "mllama": MllamaForConditionalGeneration,
+MULTIMODEL_AUTO_MODEL_MAPPING = {
    "llava": LlavaForConditionalGeneration,
-    "qwen2_vl": Qwen2VLForConditionalGeneration,
-    "qwen2_5_vl": Qwen2_5_VLForConditionalGeneration,
-    "mistral3": Mistral3ForConditionalGeneration,
-    "gemma3": Gemma3ForConditionalGeneration,
+    "mllama": MllamaForConditionalGeneration,
 }


@@ -109,21 +101,7 @@ def get_module_class_from_name(module, name):

 def check_model_config(cfg: DictDefault, model_config: Union[AutoConfig, DictDefault]):
    if cfg.is_multimodal:
-        if hasattr(model_config, "text_config"):
-            model_config = model_config.text_config
-            model_config.use_cache = False
-        elif hasattr(model_config, "get_text_config"):
-            model_config = model_config.get_text_config()
-            model_config.use_cache = False
-
-        # check if image_size is not set and load image size from model config if available
-        if (
-            cfg.image_size is None
-            and hasattr(model_config, "vision_config")
-            and hasattr(model_config.vision_config, "image_size")
-        ):
-            cfg.image_size = model_config.vision_config.image_size
-            LOG.debug(f"Loaded image size: {cfg.image_size} from model config")
+        model_config = model_config.text_config

    quant_config_exists = (
        hasattr(model_config, "quantization_config")
@@ -462,31 +440,6 @@ def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
        **processor_kwargs,
    )

-    # Attempt to load image size from processor if available
-    if (
-        cfg.image_size is None
-        and hasattr(processor, "size")
-        and any(dim in processor.size for dim in ["width", "height"])
-    ):
-        im_width = None
-        im_height = None
-        if "width" in processor.size:
-            im_width = processor.size["width"]
-        if "height" in processor.size:
-            im_height = processor.size["height"]
-
-        # If both width and height are set, use a tuple
-        if im_width is not None and im_height is not None:
-            cfg.image_size = (im_width, im_height)
-        # If only width is set, use as integer
-        elif im_width is not None:
-            cfg.image_size = im_width
-        # If only height is set, use as integer
-        elif im_height is not None:
-            cfg.image_size = im_height
-
-        LOG.debug(f"Loaded image size: {cfg.image_size} from processor")
-
    return processor


@@ -524,11 +477,7 @@ class ModelLoader:
        # init model config
        self.model_config = load_model_config(cfg)
        if cfg.is_multimodal:
-            if hasattr(self.model_config, "text_config"):
-                self.text_model_config = self.model_config.text_config
-            else:
-                # for qwen2_vl
-                self.text_model_config = self.model_config.get_text_config()
+            self.text_model_config = self.model_config.text_config
        else:
            self.text_model_config = self.model_config

@@ -724,7 +673,7 @@ class ModelLoader:
        should be set according to the type of the model.
        """
        if self.cfg.is_multimodal:
-            self.auto_model_loader = MULTIMODAL_AUTO_MODEL_MAPPING.get(
+            self.auto_model_loader = MULTIMODEL_AUTO_MODEL_MAPPING.get(
                self.model_config.model_type, AutoModelForVision2Seq
            )

@@ -1245,9 +1194,7 @@ class ModelLoader:
            )
        ):
            resize_kwargs = {}
-            if self.cfg.mean_resizing_embeddings is not None and not (
-                self.model_config.model_type == "llava"
-            ):
+            if self.cfg.mean_resizing_embeddings is not None:
                resize_kwargs["mean_resizing"] = self.cfg.mean_resizing_embeddings
            self.model.resize_token_embeddings(embeddings_len, **resize_kwargs)
        else:
--- a/src/axolotl/utils/schemas/config.py
+++ b/src/axolotl/utils/schemas/config.py
@@ -42,7 +42,6 @@ from axolotl.utils.schemas.model import (
    ModelOutputConfig,
    SpecialTokensConfig,
 )
-from axolotl.utils.schemas.multimodal import MultiModalConfig
 from axolotl.utils.schemas.peft import LoraConfig, ReLoRAConfig
 from axolotl.utils.schemas.training import HyperparametersConfig
 from axolotl.utils.schemas.trl import TRLConfig
@@ -65,7 +64,6 @@ class AxolotlInputConfig(
    LISAConfig,
    GradioConfig,
    RayConfig,
-    MultiModalConfig,
    RemappedParameters,
    DeprecatedParameters,
    BaseModel,
--- a/src/axolotl/utils/schemas/enums.py
+++ b/src/axolotl/utils/schemas/enums.py
@@ -22,7 +22,6 @@ class ChatTemplate(str, Enum):
    mistral_v1 = "mistral_v1"  # pylint: disable=invalid-name
    mistral_v2v3 = "mistral_v2v3"  # pylint: disable=invalid-name
    mistral_v3_tekken = "mistral_v3_tekken"  # pylint: disable=invalid-name
-    mistral_v7_tekken = "mistral_v7_tekken"  # pylint: disable=invalid-name
    gemma = "gemma"  # pylint: disable=invalid-name
    cohere = "cohere"  # pylint: disable=invalid-name
    llama3 = "llama3"  # pylint: disable=invalid-name
@@ -37,10 +36,6 @@ class ChatTemplate(str, Enum):
    tokenizer_default = "tokenizer_default"  # pylint: disable=invalid-name
    exaone = "exaone"  # pylint: disable=invalid-name
    metharme = "metharme"  # pylint: disable=invalid-name
-    pixtral = "pixtral"  # pylint: disable=invalid-name
-    llava = "llava"  # pylint: disable=invalid-name
-    qwen2_vl = "qwen2_vl"  # pylint: disable=invalid-name
-    gemma3 = "gemma3"  # pylint: disable=invalid-name


 class CustomSupportedOptimizers(str, Enum):
--- a/src/axolotl/utils/schemas/multimodal.py
+++ b/src/axolotl/utils/schemas/multimodal.py
@@ -1,48 +0,0 @@
-"""Pydantic models for multimodal-related configuration"""
-
-from typing import Literal
-
-from PIL.Image import Resampling
-from pydantic import BaseModel, Field, field_validator
-
-
-class MultiModalConfig(BaseModel):
-    """Multi-modal configuration subset"""
-
-    image_size: int | tuple[int, int] | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": (
-                "The size of the image to resize to. It can be an integer (resized into padded-square image) or a tuple (width, height)."
-                "If not provided, we will attempt to load from preprocessor.size, otherwise, images won't be resized."
-            )
-        },
-    )
-    image_resize_algorithm: (
-        Literal["bilinear", "bicubic", "lanczos"] | Resampling | None
-    ) = Field(
-        default=None,
-        json_schema_extra={
-            "description": "The resampling algorithm to use for image resizing. Default is bilinear. Please refer to PIL.Image.Resampling for more details."
-        },
-    )
-
-    @field_validator("image_resize_algorithm", mode="before")
-    @classmethod
-    def convert_image_resize_algorithm(cls, image_resize_algorithm):
-        """
-        Convert the image resize algorithm to a PIL.Image.Resampling enum.
-        """
-        if isinstance(image_resize_algorithm, str):
-            image_resize_algorithm = image_resize_algorithm.lower()
-            if image_resize_algorithm == "bilinear":
-                image_resize_algorithm = Resampling.BILINEAR
-            elif image_resize_algorithm == "bicubic":
-                image_resize_algorithm = Resampling.BICUBIC
-            elif image_resize_algorithm == "lanczos":
-                image_resize_algorithm = Resampling.LANCZOS
-            else:
-                raise ValueError(
-                    f"Invalid image resize algorithm: {image_resize_algorithm}"
-                )
-        return image_resize_algorithm
--- a/tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py
+++ b/tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py
@@ -144,7 +144,7 @@ def test_swiglu_mlp_integration(small_llama_model):
 def test_geglu_model_integration():
    """Test GeGLU activation with Gemma model."""
    model = AutoModelForCausalLM.from_pretrained(
-        "mhenrichsen/gemma-2b", torch_dtype=torch.float16, device_map="auto"
+        "mhenrichsen/gemma-2b", torch_dtype=torch.float16, device_map="cuda"
    )
    peft_config = get_peft_config(
        {
@@ -347,7 +347,7 @@ def test_model_architecture(model_config):
    """Test LoRA kernel patches across different model architectures."""
    # Load model with appropriate dtype
    model = AutoModelForCausalLM.from_pretrained(
-        model_config["name"], torch_dtype=model_config["dtype"], device_map="auto"
+        model_config["name"], torch_dtype=model_config["dtype"], device_map="cuda"
    )

    # Apply LoRA configuration
@@ -408,7 +408,7 @@ def test_kernel_training_integration():
    )

    # Load model
-    model, _, _ = load_model_and_tokenizer(cfg=cfg)
+    model, _ = load_model_and_tokenizer(cfg=cfg)

    # Verify correct activation function
    layer = model.model.model.layers[0]
--- a/tests/e2e/patched/test_sp.py
+++ b/tests/e2e/patched/test_sp.py
@@ -8,13 +8,14 @@ import pytest
 import torch
 from accelerate.state import PartialState

-from axolotl.monkeypatch.attention.ring_attn import (
-    get_ring_attn_group,
-    set_ring_attn_group,
-)
-from axolotl.utils.collators.batching import adjust_position_ids_for_slice
 from axolotl.utils.dict import DictDefault

+# Use a single patch for ring_flash_attn if it's not available
+ring_flash_attn_mock = MagicMock()
+with patch.dict("sys.modules", {"ring_flash_attn": ring_flash_attn_mock}):
+    from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
+    from axolotl.utils.collators.batching import adjust_position_ids_for_slice
+

@pytest.fixture
 def partial_state():
@@ -78,22 +79,6 @@ class TestSequenceParallelHelpers:
 class TestRingAttention:
    """Tests for the ring attention functionality."""

-    @patch("torch.distributed.get_rank")
-    @patch("torch.distributed.get_world_size")
-    def test_get_ring_attn_group_no_registration(
-        self, mock_world_size, mock_rank, partial_state
-    ):
-        """Test that get_ring_attn_group returns None when no group has been registered."""
-        # Setup mocks
-        mock_world_size.return_value = 4
-        mock_rank.return_value = 0
-
-        # Get the group without registration
-        group = get_ring_attn_group()
-
-        # Verify that None was returned
-        assert group is None
-
    @patch("torch.distributed.new_group")
    @patch("torch.distributed.get_rank")
    @patch("torch.distributed.get_world_size")
@@ -115,11 +100,24 @@ class TestRingAttention:
        # Verify the number of calls without examining the arguments
        assert mock_new_group.call_count == 2

-        # Verify that new_group was called
+        # Just verify that new_group was called
        mock_new_group.assert_called()

-        # Clean up
-        set_ring_attn_group(None)
+    @patch("torch.distributed.get_rank")
+    @patch("torch.distributed.get_world_size")
+    def test_get_ring_attn_group_no_registration(
+        self, mock_world_size, mock_rank, partial_state
+    ):
+        """Test that get_ring_attn_group returns None when no group has been registered."""
+        # Setup mocks
+        mock_world_size.return_value = 4
+        mock_rank.return_value = 0
+
+        # Get the group without registration
+        group = get_ring_attn_group()
+
+        # Verify that None was returned
+        assert group is None


 # Mock a simplified DataCollator test
--- a/tests/e2e/test_deepseekv3.py
+++ b/tests/e2e/test_deepseekv3.py
@@ -1,5 +1,5 @@
 """
-E2E tests for deepseekv3
+E2E tests for lora llama
 """

 import logging
--- a/tests/e2e/test_gemma2.py
+++ b/tests/e2e/test_gemma2.py
@@ -1,133 +0,0 @@
-"""
-E2E tests for gemma2
-"""
-
-import logging
-import os
-from pathlib import Path
-
-import pytest
-
-from axolotl.cli.args import TrainerCliArgs
-from axolotl.common.datasets import load_datasets
-from axolotl.train import train
-from axolotl.utils.config import normalize_config, validate_config
-from axolotl.utils.dict import DictDefault
-
-LOG = logging.getLogger("axolotl.tests.e2e")
-os.environ["WANDB_DISABLED"] = "true"
-
-
-class TestGemma2:
-    """
-    Test case for Gemma2 models
-    """
-
-    @pytest.mark.parametrize(
-        "sample_packing",
-        [True, False],
-    )
-    def test_lora_gemma2(self, temp_dir, sample_packing):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "axolotl-ai-co/gemma-2-33M",
-                "trust_remote_code": True,
-                "sample_packing": sample_packing,
-                "flash_attention": True,
-                "sequence_len": 2048,
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0,
-                "datasets": [
-                    {
-                        "path": "mlabonne/FineTome-100k",
-                        "type": "chat_template",
-                        "field_messages": "conversations",
-                        "message_property_mappings": {
-                            "role": "from",
-                            "content": "value",
-                        },
-                        "drop_system_message": True,
-                        "split": "train[:1%]",
-                    },
-                ],
-                "special_tokens": {
-                    "bos_token": "<bos>",
-                    "eos_token": "<eos>",
-                },
-                "chat_template": "gemma",  # gemma2's template is same as gemma
-                "num_epochs": 1,
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 4,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_bnb_8bit",
-                "lr_scheduler": "cosine",
-                "max_steps": 5,
-                "save_safetensors": True,
-                "bf16": True,
-            }
-        )
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "adapter_model.safetensors").exists()
-
-    @pytest.mark.parametrize(
-        "sample_packing",
-        [True, False],
-    )
-    def test_fft_gemma2(self, temp_dir, sample_packing):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "axolotl-ai-co/gemma-2-33M",
-                "trust_remote_code": True,
-                "sample_packing": sample_packing,
-                "flash_attention": True,
-                "sequence_len": 2048,
-                "val_set_size": 0,
-                "datasets": [
-                    {
-                        "path": "mlabonne/FineTome-100k",
-                        "type": "chat_template",
-                        "field_messages": "conversations",
-                        "message_property_mappings": {
-                            "role": "from",
-                            "content": "value",
-                        },
-                        "split": "train[:1%]",
-                        "drop_system_message": True,
-                    },
-                ],
-                "chat_template": "gemma",  # gemma2's template is same as gemma
-                "special_tokens": {
-                    "bos_token": "<bos>",
-                    "eos_token": "<eos>",
-                },
-                "num_epochs": 1,
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 4,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_bnb_8bit",
-                "lr_scheduler": "cosine",
-                "max_steps": 5,
-                "save_safetensors": True,
-                "bf16": True,
-            }
-        )
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "model.safetensors").exists()
--- a/tests/e2e/test_gemma3_text.py
+++ b/tests/e2e/test_gemma3_text.py
@@ -1,131 +0,0 @@
-"""
-E2E tests for gemma3_text
-"""
-
-import logging
-import os
-from pathlib import Path
-
-import pytest
-
-from axolotl.cli.args import TrainerCliArgs
-from axolotl.common.datasets import load_datasets
-from axolotl.train import train
-from axolotl.utils.config import normalize_config, validate_config
-from axolotl.utils.dict import DictDefault
-
-LOG = logging.getLogger("axolotl.tests.e2e")
-os.environ["WANDB_DISABLED"] = "true"
-
-
-class TestGemma3Text:
-    """
-    Test case for Gemma3Text models
-    """
-
-    @pytest.mark.parametrize(
-        "sample_packing",
-        [True, False],
-    )
-    def test_lora_gemma3_text(self, temp_dir, sample_packing):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "axolotl-ai-co/gemma-3-34M",
-                "trust_remote_code": True,
-                "sample_packing": sample_packing,
-                "flash_attention": True,
-                "sequence_len": 2048,
-                "adapter": "lora",
-                "lora_r": 8,
-                "lora_alpha": 16,
-                "lora_dropout": 0.05,
-                "lora_target_linear": True,
-                "val_set_size": 0,
-                "datasets": [
-                    {
-                        "path": "mlabonne/FineTome-100k",
-                        "type": "chat_template",
-                        "field_messages": "conversations",
-                        "message_property_mappings": {
-                            "role": "from",
-                            "content": "value",
-                        },
-                        "split": "train[:1%]",
-                    },
-                ],
-                "special_tokens": {
-                    "bos_token": "<bos>",
-                    "eos_token": "<eos>",
-                },
-                "chat_template": "gemma3",
-                "num_epochs": 1,
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 4,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_bnb_8bit",
-                "lr_scheduler": "cosine",
-                "max_steps": 5,
-                "save_safetensors": True,
-                "bf16": True,
-            }
-        )
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "adapter_model.safetensors").exists()
-
-    @pytest.mark.parametrize(
-        "sample_packing",
-        [True, False],
-    )
-    def test_fft_gemma3_text(self, temp_dir, sample_packing):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "axolotl-ai-co/gemma-3-34M",
-                "trust_remote_code": True,
-                "sample_packing": sample_packing,
-                "flash_attention": True,
-                "sequence_len": 2048,
-                "val_set_size": 0,
-                "datasets": [
-                    {
-                        "path": "mlabonne/FineTome-100k",
-                        "type": "chat_template",
-                        "field_messages": "conversations",
-                        "message_property_mappings": {
-                            "role": "from",
-                            "content": "value",
-                        },
-                        "split": "train[:1%]",
-                    },
-                ],
-                "chat_template": "gemma3",
-                "special_tokens": {
-                    "bos_token": "<bos>",
-                    "eos_token": "<eos>",
-                },
-                "num_epochs": 1,
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 4,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_bnb_8bit",
-                "lr_scheduler": "cosine",
-                "max_steps": 5,
-                "save_safetensors": True,
-                "bf16": True,
-            }
-        )
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "model.safetensors").exists()
--- a/tests/e2e/test_schedulers.py
+++ b/tests/e2e/test_schedulers.py
@@ -54,7 +54,7 @@ class TestCustomSchedulers(unittest.TestCase):
                "gradient_accumulation_steps": 1,
                "output_dir": temp_dir,
                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch_fused",
+                "optimizer": "adamw_hf",
                "max_steps": 20,
                "lr_scheduler": "rex",
                "warmup_steps": 5,
Author	SHA1	Message	Date
Dan Saunders	4ac65462f0	precommit	2025-03-21 16:43:14 +00:00
Dan Saunders	ce35b2a95f	precommit	2025-03-21 16:36:56 +00:00
Dan Saunders	ab3b36339a	fix tests	2025-03-21 16:36:54 +00:00
Dan Saunders	22cfa42961	small updates	2025-03-21 16:36:34 +00:00
Dan Saunders	0b2c2ed68c	refactors, SP mixin	2025-03-21 16:36:34 +00:00
Dan Saunders	2f0b4626b9	review comments, docstrings	2025-03-21 16:36:32 +00:00
Dan Saunders	a26985c53c	small changes	2025-03-21 16:36:17 +00:00
Dan Saunders	c1a58339e8	add SP doc, review comments	2025-03-21 16:36:17 +00:00
Dan Saunders	411df76a97	bugfix	2025-03-21 16:36:17 +00:00
Dan Saunders	a09d1ccbf2	removing print statement	2025-03-21 16:36:17 +00:00
Dan Saunders	2727d86544	non-seq2se1 collator fix	2025-03-21 16:36:17 +00:00
Dan Saunders	64c203cdef	sampler / dataloader refactor	2025-03-21 16:36:17 +00:00
Dan Saunders	7d7042f602	test fix	2025-03-21 16:36:17 +00:00
Dan Saunders	d187f1f8e2	using field validator instead of model validator	2025-03-21 16:36:17 +00:00
Dan Saunders	1cced52719	rename file, delete another	2025-03-21 16:36:17 +00:00
Dan Saunders	11321b17e7	removing flash-attn from requirements.txt (in setup.py extras already)	2025-03-21 16:36:17 +00:00
Wing Lian	7a1a211c99	move ring flash attn to extras with flash-attn (#2414 )	2025-03-21 16:36:17 +00:00
Dan Saunders	e1a02a32b5	fix	2025-03-21 16:36:17 +00:00
Dan Saunders	a6ef6c7764	fix	2025-03-21 16:36:17 +00:00
Dan Saunders	cb3a9e99a3	gracefully handle no ring-flash-attn	2025-03-21 16:36:17 +00:00
Dan Saunders	3ae47ec7de	actually isolate CLI tests	2025-03-21 16:36:17 +00:00
Dan Saunders	e36dc763ab	isolate cli tests	2025-03-21 16:36:17 +00:00
Dan Saunders	03027cf6bf	pernicious Fire CLI bugfix	2025-03-21 16:36:16 +00:00
Dan Saunders	0ade60d455	another import scoping change	2025-03-21 16:35:56 +00:00
Dan Saunders	02e1a42f04	scoping down problematic import	2025-03-21 16:35:56 +00:00
Dan Saunders	919b88f11b	update config.qmd and rename option	2025-03-21 16:35:55 +00:00
Dan Saunders	345a9dd831	removing some obvious comments	2025-03-21 16:35:38 +00:00
Dan Saunders	4ff97bc9d4	eval dataloader and sampler changes	2025-03-21 16:35:38 +00:00
Dan Saunders	d0e178d52f	remove debug logs and simplify	2025-03-21 16:35:38 +00:00
Dan Saunders	5731cdc0cf	fixing sample packing	2025-03-21 16:35:38 +00:00
Dan Saunders	b7738d57c4	working multi-group SP	2025-03-21 16:35:38 +00:00
Dan Saunders	698e599bf7	precommit fixes	2025-03-21 16:35:38 +00:00
Dan Saunders	1d339e4007	fixes	2025-03-21 16:35:38 +00:00
Dan Saunders	4190ad0647	updates	2025-03-21 16:35:36 +00:00
Dan Saunders	b44a207248	update	2025-03-21 16:35:10 +00:00
Dan Saunders	51c326150b	pytest	2025-03-21 16:35:10 +00:00
Dan Saunders	14baaf6e0a	updates	2025-03-21 16:35:10 +00:00
Dan Saunders	f487910444	removing unused code	2025-03-21 16:35:08 +00:00
Dan Saunders	c5071dfd8a	fix req	2025-03-21 16:34:12 +00:00
Dan Saunders	e323145ba9	remove errant file	2025-03-21 16:34:12 +00:00
Dan Saunders	7efc787ac8	cleanup	2025-03-21 16:34:12 +00:00
Dan Saunders	dce61cdab1	progress on ring attn impl	2025-03-21 16:34:12 +00:00
Dan Saunders	bd952de9d2	progress on ring attn impl	2025-03-21 16:34:10 +00:00
Dan Saunders	3f8a43cab6	adding easy_context as integration for now	2025-03-21 16:33:46 +00:00