Various fixes for VLMs (#3063)

* fix to not use batch feature indexing

* more vlm fixes

* use AutoModelForImageTextToText

* add example yaml and need num2words for chat template

* improve handling of adding image tokens to conversation

* add lfm2-vl support

* update the lfm readme

* fix markdown and add rtol for loss checks

* feat: add smolvlm2 processing strat

* fix: check for causal-conv1d in lfm models

* feat: add docs for lfm2

* feat: add new models and tips to docs

* feat: add smolvlm2 docs and remove extra dep

* chore: update docs

* feat: add video instructions

* chore: cleanup

* chore: comments

* fix: typo

* feat: add usage stats

* chore: refactor

---------

Co-authored-by: NanoCode012 <nano@axolotl.ai>

examples/smolvlm2/README.md

@@ -0,0 +1,49 @@
+# Finetune SmolVLM2 with Axolotl
+
+[SmolVLM2](https://huggingface.co/collections/HuggingFaceTB/smolvlm2-smallest-video-lm-ever-67ab6b5e84bf8aaa60cb17c7) are a family of lightweight, open-source multimodal models from HuggingFace designed to analyze and understand video, image, and text content.
+
+These models are built for efficiency, making them well-suited for on-device applications where computational resources are limited. Models are available in multiple sizes, including 2.2B, 500M, and 256M.
+
+This guide shows how to fine-tune SmolVLM2 models with Axolotl.
+
+## Getting Started
+
+1.  Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+
+    Here is an example of how to install from pip:
+    ```bash
+    # Ensure you have a compatible version of Pytorch installed
+    pip3 install packaging setuptools wheel ninja
+    pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
+    ```
+
+2. Install an extra dependency:
+
+    ```bash
+    pip3 install num2words==0.5.14
+    ```
+
+3.  Run the finetuning example:
+
+    ```bash
+    # LoRA SFT (1x48GB @ 6.8GiB)
+    axolotl train examples/smolvlm2/smolvlm2-2B-lora.yaml
+    ```
+
+## TIPS
+
+- **Dataset Format**: For video finetuning, your dataset must be compatible with the multi-content Messages format. For more details, see our documentation on [Multimodal Formats](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
+- **Dataset Loading**: Read more on how to prepare and load your own datasets in our [documentation](https://docs.axolotl.ai/docs/dataset_loading.html).
+
+## Optimization Guides
+
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+
+## Related Resources
+
+- [SmolVLM2 Blog](https://huggingface.co/blog/smolvlm2)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)

examples/smolvlm2/smolvlm2-2B-lora.yaml

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+base_model: HuggingFaceTB/SmolVLM2-2.2B-Instruct
+trust_remote_code: true
+processor_type: AutoProcessor
+
+# 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
+
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+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.text_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
+
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config