mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-12-16 20:00:36 +08:00
[model] add qwen2.5 vl models (#6779)
This commit is contained in:
12
README.md
12
README.md
@@ -5,7 +5,7 @@
|
||||
[](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors)
|
||||
[](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml)
|
||||
[](https://pypi.org/project/llamafactory/)
|
||||
[](https://scholar.google.com/scholar?cites=12620864006390196564)
|
||||
[](https://scholar.google.com/scholar?cites=12620864006390196564)
|
||||
[](https://github.com/hiyouga/LLaMA-Factory/pulls)
|
||||
|
||||
[](https://twitter.com/llamafactory_ai)
|
||||
@@ -73,7 +73,7 @@ Choose your path:
|
||||
|
||||
## Features
|
||||
|
||||
- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
|
||||
- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, DeepSeek, Yi, Gemma, ChatGLM, Phi, etc.
|
||||
- **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
|
||||
- **Scalable resources**: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
|
||||
- **Advanced algorithms**: [GaLore](https://github.com/jiaweizzhao/GaLore), [BAdam](https://github.com/Ledzy/BAdam), [APOLLO](https://github.com/zhuhanqing/APOLLO), [Adam-mini](https://github.com/zyushun/Adam-mini), DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
|
||||
@@ -105,16 +105,18 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
|
||||
|
||||
## Changelog
|
||||
|
||||
[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.
|
||||
|
||||
[25/01/15] We supported **[APOLLO](https://arxiv.org/abs/2412.05270)** optimizer. See [examples](examples/README.md) for usage.
|
||||
|
||||
[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.
|
||||
|
||||
<details><summary>Full Changelog</summary>
|
||||
|
||||
[25/01/14] We supported fine-tuning the **[InternLM3](https://huggingface.co/collections/internlm/)** models. Thank [@hhaAndroid](https://github.com/hhaAndroid)'s PR.
|
||||
|
||||
[25/01/10] We supported fine-tuning the **[Phi-4](https://huggingface.co/microsoft/phi-4)** model.
|
||||
|
||||
<details><summary>Full Changelog</summary>
|
||||
|
||||
[24/12/21] We supported using **[SwanLab](https://github.com/SwanHubX/SwanLab)** for experiment tracking and visualization. See [this section](#use-swanlab-logger) for details.
|
||||
|
||||
[24/11/27] We supported fine-tuning the **[Skywork-o1](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B)** model and the **[OpenO1](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT)** dataset.
|
||||
@@ -243,7 +245,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
|
||||
| [Phi-4](https://huggingface.co/microsoft) | 14B | phi4 |
|
||||
| [Pixtral](https://huggingface.co/mistralai) | 12B | pixtral |
|
||||
| [Qwen/QwQ (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
|
||||
| [Qwen2-VL/QVQ](https://huggingface.co/Qwen) | 2B/7B/72B | qwen2_vl |
|
||||
| [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen) | 2B/3B/7B/72B | qwen2_vl |
|
||||
| [Skywork o1](https://huggingface.co/Skywork) | 8B | skywork_o1 |
|
||||
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
|
||||
| [TeleChat2](https://huggingface.co/Tele-AI) | 3B/7B/35B/115B | telechat2 |
|
||||
|
||||
Reference in New Issue
Block a user