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[assets] update readme (#7612)
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README.md
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README.md
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| Support Date | Model Name |
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| ------------ | ------------------------------------------------------------ |
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| Day 0 | Qwen2.5 / Qwen2.5-VL / Gemma 3 / InternLM 3 / MiniCPM-o-2.6 |
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| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 |
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| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4 |
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## Benchmark
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@ -106,20 +106,24 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
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## Changelog
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[25/04/06] We supported fine-tuning the **[Llama 4](https://ai.meta.com/blog/llama-4-multimodal-intelligence/)** model. See [PR #7611](https://github.com/hiyouga/LLaMA-Factory/pull/7611) to get started.
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[25/03/31] We supported fine-tuning the **[Qwen2.5 Omni](https://qwenlm.github.io/blog/qwen2.5-omni/)** model. See [PR #7537](https://github.com/hiyouga/LLaMA-Factory/pull/7537) to get started.
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[25/03/15] We supported **[SGLang](https://github.com/sgl-project/sglang)** as inference backend. Try `infer_backend: sglang` to accelerate inference.
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[25/03/12] We supported fine-tuning the **[Gemma-3](https://huggingface.co/blog/gemma3)** model.
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[25/02/24] Announcing **[EasyR1](https://github.com/hiyouga/EasyR1)**, an efficient, scalable and multi-modality RL training framework for efficient GRPO training.
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<details><summary>Full Changelog</summary>
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[25/02/11] We supported saving the **[Ollama](https://github.com/ollama/ollama)** modelfile when exporting the model checkpoints. See [examples](examples/README.md) for usage.
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[25/02/05] We supported fine-tuning the **[Qwen2-Audio](Qwen/Qwen2-Audio-7B-Instruct)** and **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** on audio understanding tasks.
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[25/01/31] We supported fine-tuning the **[DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1)** and **[Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)** model.
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<details><summary>Full Changelog</summary>
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[25/01/15] We supported **[APOLLO](https://arxiv.org/abs/2412.05270)** optimizer. See [examples](examples/README.md) for usage.
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[25/01/14] We supported fine-tuning the **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** and **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** models. Thank [@BUAADreamer](https://github.com/BUAADreamer)'s PR.
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@ -243,6 +247,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
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| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
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| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
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| [Llama 3-3.3](https://huggingface.co/meta-llama) | 1B/3B/8B/70B | llama3 |
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| [Llama 4](https://huggingface.co/meta-llama) | 109B/402B | llama4 |
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| [Llama 3.2 Vision](https://huggingface.co/meta-llama) | 11B/90B | mllama |
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| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | llava |
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| [LLaVA-NeXT](https://huggingface.co/llava-hf) | 7B/8B/13B/34B/72B/110B | llava_next |
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11
README_zh.md
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README_zh.md
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| 适配时间 | 模型名称 |
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| ------------ | ------------------------------------------------------------ |
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| Day 0 | Qwen2.5 / Qwen2.5-VL / Gemma 3 / InternLM 3 / MiniCPM-o-2.6 |
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| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 |
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| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4 |
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## 性能指标
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## 更新日志
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[25/04/06] 我们支持了 **[Llama 4](https://ai.meta.com/blog/llama-4-multimodal-intelligence/)** 模型的微调。查看 [PR #7611](https://github.com/hiyouga/LLaMA-Factory/pull/7611) 以使用。
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[25/03/31] 我们支持了 **[Qwen2.5 Omni](https://qwenlm.github.io/blog/qwen2.5-omni/)** 模型的微调。查看 [PR #7537](https://github.com/hiyouga/LLaMA-Factory/pull/7537) 以使用。
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[25/03/15] 我们支持了 **[SGLang](https://github.com/sgl-project/sglang)** 推理后端，请使用 `infer_backend: sglang` 启用。
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[25/03/12] 我们支持了 **[Gemma-3](https://huggingface.co/blog/gemma3)** 模型的微调。
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[25/02/24] 我们宣布开源 **[EasyR1](https://github.com/hiyouga/EasyR1)**，一个高效可扩展的多模态强化学习框架，支持高效的 GRPO 训练。
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<details><summary>展开日志</summary>
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[25/02/11] 我们支持了在导出模型时保存 **[Ollama](https://github.com/ollama/ollama)** 配置文件。详细用法请参照 [examples](examples/README_zh.md)。
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[25/02/05] 我们支持了在语音理解任务上微调 **[Qwen2-Audio](Qwen/Qwen2-Audio-7B-Instruct)** 和 **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** 模型。
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[25/01/31] 我们支持了 **[DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1)** 和 **[Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)** 模型的微调。
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<details><summary>展开日志</summary>
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[25/01/15] 我们支持了 **[APOLLO](https://arxiv.org/abs/2412.05270)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。
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[25/01/14] 我们支持了 **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** 和 **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** 模型的微调。 感谢 [@BUAADreamer](https://github.com/BUAADreamer) 的 PR.
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@ -246,6 +250,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
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| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
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| [Llama 3-3.3](https://huggingface.co/meta-llama) | 1B/3B/8B/70B | llama3 |
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| [Llama 3.2 Vision](https://huggingface.co/meta-llama) | 11B/90B | mllama |
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| [Llama 4](https://huggingface.co/meta-llama) | 109B/402B | llama4 |
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| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | llava |
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| [LLaVA-NeXT](https://huggingface.co/llava-hf) | 7B/8B/13B/34B/72B/110B | llava_next |
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| [LLaVA-NeXT-Video](https://huggingface.co/llava-hf) | 7B/34B | llava_next_video |
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48
examples/train_lora/llama4_lora_sft.yaml
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examples/train_lora/llama4_lora_sft.yaml
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# pip install git+https://github.com/hiyouga/transformers.git@llama4_train
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### model
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model_name_or_path: meta-llama/Llama-4-Scout-17B-16E-Instruct
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trust_remote_code: true
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### method
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stage: sft
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do_train: true
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finetuning_type: lora
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lora_rank: 8
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lora_target: all
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deepspeed: examples/deepspeed/ds_z3_config.json # choices: [ds_z0_config.json, ds_z2_config.json, ds_z3_config.json]
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### dataset
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dataset: mllm_demo,identity,alpaca_en_demo
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template: llama4
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cutoff_len: 2048
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max_samples: 1000
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overwrite_cache: true
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preprocessing_num_workers: 16
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dataloader_num_workers: 4
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### output
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output_dir: saves/llama4-8b/lora/sft
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logging_steps: 10
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save_steps: 500
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plot_loss: true
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overwrite_output_dir: true
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save_only_model: false
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### train
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per_device_train_batch_size: 1
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gradient_accumulation_steps: 8
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learning_rate: 1.0e-4
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num_train_epochs: 3.0
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lr_scheduler_type: cosine
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warmup_ratio: 0.1
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bf16: true
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ddp_timeout: 180000000
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resume_from_checkpoint: null
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### eval
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# eval_dataset: alpaca_en_demo
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# val_size: 0.1
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# per_device_eval_batch_size: 1
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# eval_strategy: steps
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# eval_steps: 500
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