130ef7c51a
* 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>
59 lines
2.8 KiB
Markdown
59 lines
2.8 KiB
Markdown
# Finetune Liquid Foundation Models 2 (LFM2) with Axolotl
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[Liquid Foundation Models 2 (LFM2)](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38) are a family of small, open-weight models from [Liquid AI](https://www.liquid.ai/) focused on quality, speed, and memory efficiency. Liquid AI released text-only [LFM2](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38) and text+vision [LFM2-VL](https://huggingface.co/collections/LiquidAI/lfm2-vl-68963bbc84a610f7638d5ffa) models.
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LFM2 features a new hybrid Liquid architecture with multiplicative gates, short-range convolutions, and grouped query attention, enabling fast training and inference.
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This guide shows how to fine-tune both the LFM2 and LFM2-VL models with Axolotl.
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## Getting Started
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1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
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Here is an example of how to install from pip:
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```bash
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# Ensure you have a compatible version of Pytorch installed
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pip3 install packaging setuptools wheel ninja
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pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
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```
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2. Run one of the finetuning examples below.
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**LFM2**
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```bash
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# FFT SFT (1x48GB @ 25GiB)
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axolotl train examples/LiquidAI/lfm2-350m-fft.yaml
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```
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**LFM2-VL**
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```bash
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# LoRA SFT (1x48GB @ 2.7GiB)
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axolotl train examples/LiquidAI/lfm2-vl-lora.yaml
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```
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### TIPS
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- **Installation Error**: If you encounter `ImportError: ... undefined symbol ...` or `ModuleNotFoundError: No module named 'causal_conv1d_cuda'`, the `causal-conv1d` package may have been installed incorrectly. Try uninstalling it:
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```bash
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pip uninstall -y causal-conv1d
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```
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- **Dataset Loading**: Read more on how to load your own dataset in our [documentation](https://docs.axolotl.ai/docs/dataset_loading.html).
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- **Dataset Formats**:
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- For LFM2 models, the dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
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- For LFM2-VL models, Axolotl follows the multi-content Messages format. See our [Multimodal docs](https://docs.axolotl.ai/docs/multimodal.html#dataset-format) for details.
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## Optimization Guides
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- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
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- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
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- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
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## Related Resources
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- [LFM2 Blog](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models)
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- [LFM2-VL Blog](https://www.liquid.ai/blog/lfm2-vl-efficient-vision-language-models)
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- [Axolotl Docs](https://docs.axolotl.ai)
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- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
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- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)
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