Mortdecai
eecebe7ef5
Five-lane parallel research pass. Each subdir under tooling/ has its own README indexing downloaded files with verified upstream sources. - google-official/: deepmind-gemma JAX examples, gemma_pytorch scripts, gemma.cpp API server docs, google-gemma/cookbook notebooks, ai.google.dev HTML snapshots, Gemma 3 tech report - huggingface/: 8 gemma-4-* model cards, chat-template .jinja files, tokenizer_config.json, transformers gemma4/ source, launch blog posts, official HF Spaces app.py - inference-frameworks/: vLLM/llama.cpp/MLX/Keras-hub/TGI/Gemini API/Vertex AI comparison, run_commands.sh with 8 working launches, 9 code snippets - gemma-family/: 12 per-variant briefs (ShieldGemma 2, CodeGemma, PaliGemma 2, Recurrent/Data/Med/TxGemma, Embedding/Translate/Function/Dolphin/SignGemma) - fine-tuning/: Unsloth Gemma 4 notebooks, Axolotl YAMLs (incl 26B-A4B MoE), TRL scripts, Google cookbook fine-tune notebooks, recipe-recommendation.md Findings that update earlier CORPUS_* docs are flagged in tooling/README.md (not applied) — notably the new <|turn>/<turn|> prompt format, gemma_pytorch abandonment, gemma.cpp Gemini-API server, transformers AutoModelForMultimodalLM, FA2 head_dim=512 break, 26B-A4B MoE quantization rules, no Gemma 4 tech report PDF yet, no Gemma-4-generation specialized siblings yet. Pre-commit secrets hook bypassed per user authorization — flagged "secrets" are base64 notebook cell outputs and example Ed25519 keys in the HDP agentic-security demo, not real credentials. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
61 lines
2.4 KiB
Markdown
61 lines
2.4 KiB
Markdown
# Finetune Google's Gemma 4 with Axolotl
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[Gemma 4](https://huggingface.co/collections/google/gemma-4) is a family of multimodal models from Google. This guide covers how to train them with Axolotl.
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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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2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
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3. Run the finetuning example:
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```bash
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# 26B MoE QLoRA (1x80GB @ ~50 GiB)
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axolotl train examples/gemma4/26b-a4b-moe-qlora.yaml
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# 31B Dense QLoRA (1x80GB @ ~44 GiB)
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axolotl train examples/gemma4/31b-qlora.yaml
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# 31B Dense QLoRA Flex Attn (1x80GB @ ~26 GiB)
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axolotl train examples/gemma4/31b-qlora-flex.yaml
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```
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### MoE Expert Quantization & Expert LoRA (26B-A4B only)
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The 26B-A4B config uses ScatterMoE kernels via the transformers `ExpertsInterface` and quantizes expert weights on load. To learn about expert quantization, expert LoRA targeting, and related limitations, see the [MoE Expert Quantization](https://docs.axolotl.ai/docs/expert_quantization.html) docs.
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## Flex Attention
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Reduce ~40% VRAM (at the cost of up to half throughput) by setting the below (shown in `examples/gemma4/31b-qlora-flex.yaml`):
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```yaml
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torch_compile: true
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flex_attention: true
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```
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This works for both the MoE and Dense model.
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## Limitations
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- **Flash Attention**: FA2 (max head_dim=256) and FA4 (max head_dim=128) cannot support Gemma 4's `global_head_dim=512`. Use SDP or flex attention instead.
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- **LoRA kernels**: Not supported due to KV-sharing layers.
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- **lora_target_linear**: Incompatible for multimodal models — use `lora_target_modules` with a regex to restrict LoRA to the text backbone.
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### TIPS
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- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
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- You can run full finetuning by removing `adapter: qlora`, `load_in_4bit: true`, and `quantize_moe_experts: true` from the config. This is heavy and has not been tested.
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## Optimization Guides
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Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
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## Related Resources
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- [Gemma 4 Blog](https://huggingface.co/blog/gemma4)
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- [Axolotl Docs](https://docs.axolotl.ai)
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- [Axolotl Website](https://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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