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update 0718

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README.md
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......@@ -75,7 +75,7 @@ LLaMA Factory是一个大语言模型训练和推理的框架，支持了魔搭
基于光源pytorch2.4.1基础镜像环境：镜像下载地址：[https://sourcefind.cn/#/image/dcu/pytorch](https://sourcefind.cn/#/image/dcu/pytorch)，根据pytorch2.4.1、python、dtk及系统下载对应的镜像版本。
```bash
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10
docker pull docker pull image.sourcefind.cn:5000/dcu/admin/base/vllm:0.8.5-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250711
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/llama_factory
......
README_en.md
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......@@ -5,7 +5,7 @@
[![GitHub contributors](https://img.shields.io/github/contributors/hiyouga/LLaMA-Factory?color=orange)](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors)
[![GitHub workflow](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml/badge.svg)](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml)
[![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
[![Citation](https://img.shields.io/badge/citation-614-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
[![Citation](https://img.shields.io/badge/citation-651-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
[![Docker Pulls](https://img.shields.io/docker/pulls/hiyouga/llamafactory)](https://hub.docker.com/r/hiyouga/llamafactory/tags)
[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
......@@ -51,7 +51,8 @@ https://github.com/user-attachments/assets/3991a3a8-4276-4d30-9cab-4cb0c4b9b99e
Choose your path:
- **Documentation**: https://llamafactory.readthedocs.io/en/latest/
- **Documentation (WIP)**: https://llamafactory.readthedocs.io/en/latest/
- **Documentation (AMD GPU)**: https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/fine_tune/llama_factory_llama3.html
- **Colab (free)**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
- **Local machine**: Please refer to [usage](#getting-started)
- **PAI-DSW (free trial)**: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
......@@ -98,10 +99,10 @@ Choose your path:
### Day-N Support for Fine-Tuning Cutting-Edge Models
| Support Date | Model Name |
| ------------ | ------------------------------------------------------------ |
| Day 0 | Qwen3 / Qwen2.5-VL / Gemma 3 / InternLM 3 / MiniCPM-o-2.6 |
| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4 |
| Support Date | Model Name |
| ------------ | -------------------------------------------------------------------- |
| Day 0 | Qwen3 / Qwen2.5-VL / Gemma 3 / GLM-4.1V / InternLM 3 / MiniCPM-o-2.6 |
| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4 |
## Blogs
......@@ -121,6 +122,8 @@ Choose your path:
## Changelog
[25/07/02] We supported fine-tuning the **[GLM-4.1V-9B-Thinking](https://github.com/THUDM/GLM-4.1V-Thinking)** model. Please install transformers from **main** branch to use.
[25/04/28] We supported fine-tuning the **[Qwen3](https://qwenlm.github.io/blog/qwen3/)** model family.
[25/04/21] We supported the **[Muon](https://github.com/KellerJordan/Muon)** optimizer. See [examples](examples/README.md) for usage. Thank [@tianshijing](https://github.com/tianshijing)'s PR.
......@@ -262,9 +265,11 @@ Choose your path:
| [DeepSeek 2.5/3](https://huggingface.co/deepseek-ai) | 236B/671B | deepseek3 |
| [DeepSeek R1 (Distill)](https://huggingface.co/deepseek-ai) | 1.5B/7B/8B/14B/32B/70B/671B | deepseekr1 |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
| [Gemma 3](https://huggingface.co/google) | 1B/4B/12B/27B | gemma3/gemma (1B) |
| [Falcon-H1](https://huggingface.co/tiiuae) | 0.5B/1.5B/3B/7B/34B | falcon_h1 |
| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma/gemma2 |
| [Gemma 3/Gemma 3n](https://huggingface.co/google) | 1B/4B/6B/8B/12B/27B | gemma3/gemma3n |
| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM) | 9B/32B | glm4/glmz1 |
| [GLM-4.1V](https://huggingface.co/THUDM)* | 9B | glm4v |
| [GPT-2](https://huggingface.co/openai-community) | 0.1B/0.4B/0.8B/1.5B | - |
| [Granite 3.0-3.3](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
......@@ -444,7 +449,7 @@ huggingface-cli login
| python | 3.9 | 3.10 |
| torch | 2.0.0 | 2.6.0 |
| torchvision | 0.15.0 | 0.21.0 |
| transformers | 4.45.0 | 4.50.0 |
| transformers | 4.49.0 | 4.50.0 |
| datasets | 2.16.0 | 3.2.0 |
| accelerate | 0.34.0 | 1.2.1 |
| peft | 0.14.0 | 0.15.1 |
......@@ -486,7 +491,7 @@ cd LLaMA-Factory
pip install -e ".[torch,metrics]" --no-build-isolation
```
Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, aqlm, vllm, sglang, galore, apollo, badam, adam-mini, qwen, minicpm_v, modelscope, openmind, swanlab, dev
Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, aqlm, vllm, sglang, galore, apollo, badam, adam-mini, qwen, minicpm_v, openmind, swanlab, dev
#### Install from Docker Image
......
README_zh.md
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......@@ -5,7 +5,7 @@
[![GitHub contributors](https://img.shields.io/github/contributors/hiyouga/LLaMA-Factory?color=orange)](https://github.com/hiyouga/LLaMA-Factory/graphs/contributors)
[![GitHub workflow](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml/badge.svg)](https://github.com/hiyouga/LLaMA-Factory/actions/workflows/tests.yml)
[![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
[![Citation](https://img.shields.io/badge/citation-614-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
[![Citation](https://img.shields.io/badge/citation-651-green)](https://scholar.google.com/scholar?cites=12620864006390196564)
[![Docker Pulls](https://img.shields.io/docker/pulls/hiyouga/llamafactory)](https://hub.docker.com/r/hiyouga/llamafactory/tags)
[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
......@@ -52,6 +52,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
选择你的打开方式：
- **入门教程**：https://zhuanlan.zhihu.com/p/695287607
- **微调视频教程**：https://www.bilibili.com/video/BV1djgRzxEts/
- **框架文档**：https://llamafactory.readthedocs.io/zh-cn/latest/
- **框架文档（昇腾 NPU）**：https://ascend.github.io/docs/sources/llamafactory/
- **Colab（免费）**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
......@@ -100,10 +101,10 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
### 最新模型的 Day-N 微调适配
| 适配时间 | 模型名称 |
| ------------ | ------------------------------------------------------------ |
| Day 0 | Qwen3 / Qwen2.5-VL / Gemma 3 / InternLM 3 / MiniCPM-o-2.6 |
| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4 |
| 适配时间 | 模型名称 |
| ------------ | -------------------------------------------------------------------- |
| Day 0 | Qwen3 / Qwen2.5-VL / Gemma 3 / GLM-4.1V / InternLM 3 / MiniCPM-o-2.6 |
| Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4 |
## 官方博客
......@@ -123,6 +124,8 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
## 更新日志
[25/07/02] 我们支持了 **[GLM-4.1V-9B-Thinking](https://github.com/THUDM/GLM-4.1V-Thinking)** 模型的微调。请安装 transformers 的 main 分支版本以使用。
[25/04/28] 我们支持了 **[Qwen3](https://qwenlm.github.io/blog/qwen3/)** 系列模型的微调。
[25/04/21] 我们支持了 **[Muon](https://github.com/KellerJordan/Muon)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。感谢 [@tianshijing](https://github.com/tianshijing) 的 PR。
......@@ -264,9 +267,11 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
| [DeepSeek 2.5/3](https://huggingface.co/deepseek-ai) | 236B/671B | deepseek3 |
| [DeepSeek R1 (Distill)](https://huggingface.co/deepseek-ai) | 1.5B/7B/8B/14B/32B/70B/671B | deepseekr1 |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
| [Gemma 3](https://huggingface.co/google) | 1B/4B/12B/27B | gemma3/gemma (1B) |
| [Falcon-H1](https://huggingface.co/tiiuae) | 0.5B/1.5B/3B/7B/34B | falcon_h1 |
| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma/gemma2 |
| [Gemma 3/Gemma 3n](https://huggingface.co/google) | 1B/4B/6B/8B/12B/27B | gemma3/gemma3n |
| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM) | 9B/32B | glm4/glmz1 |
| [GLM-4.1V](https://huggingface.co/THUDM)* | 9B | glm4v |
| [GPT-2](https://huggingface.co/openai-community) | 0.1B/0.4B/0.8B/1.5B | - |
| [Granite 3.0-3.3](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
......@@ -446,7 +451,7 @@ huggingface-cli login
| python | 3.9 | 3.10 |
| torch | 2.0.0 | 2.6.0 |
| torchvision | 0.15.0 | 0.21.0 |
| transformers | 4.45.0 | 4.50.0 |
| transformers | 4.49.0 | 4.50.0 |
| datasets | 2.16.0 | 3.2.0 |
| accelerate | 0.34.0 | 1.2.1 |
| peft | 0.14.0 | 0.15.1 |
......@@ -488,7 +493,7 @@ cd LLaMA-Factory
pip install -e ".[torch,metrics]" --no-build-isolation
```
可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、aqlm、vllm、sglang、galore、apollo、badam、adam-mini、qwen、minicpm_v、modelscope、openmind、swanlab、dev
可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、aqlm、vllm、sglang、galore、apollo、badam、adam-mini、qwen、minicpm_v、openmind、swanlab、dev
#### 从镜像安装
......
docker/Dockerfile 0 → 100644
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FROM docker pull image.sourcefind.cn:5000/dcu/admin/base/vllm:0.8.5-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250711
\ No newline at end of file
docker/docker-cuda/Dockerfile deleted 100644 → 0
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# Default use the NVIDIA official image with PyTorch 2.6.0
# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html
ARG BASE_IMAGE=nvcr.io/nvidia/pytorch:24.12-py3
FROM ${BASE_IMAGE}
# Define environments
ENV MAX_JOBS=4
ENV FLASH_ATTENTION_FORCE_BUILD=TRUE
ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
# Define installation arguments
ARG INSTALL_BNB=false
ARG INSTALL_VLLM=false
ARG INSTALL_DEEPSPEED=false
ARG INSTALL_FLASHATTN=false
ARG INSTALL_LIGER_KERNEL=false
ARG INSTALL_HQQ=false
ARG INSTALL_EETQ=false
ARG PIP_INDEX=https://pypi.org/simple
ARG HTTP_PROXY=
# Set the working directory
WORKDIR /app
# Set http proxy
RUN if [ -n "$HTTP_PROXY" ]; then \
echo "Configuring proxy..."; \
export http_proxy=$HTTP_PROXY; \
export https_proxy=$HTTP_PROXY; \
fi
# Install the requirements
COPY requirements.txt /app
RUN pip config set global.index-url "$PIP_INDEX" && \
pip config set global.extra-index-url "$PIP_INDEX" && \
python -m pip install --upgrade pip && \
if [ -n "$HTTP_PROXY" ]; then \
python -m pip install --proxy=$HTTP_PROXY -r requirements.txt; \
else \
python -m pip install -r requirements.txt; \
fi
# Copy the rest of the application into the image
COPY . /app
# Install the LLaMA Factory
RUN EXTRA_PACKAGES="metrics"; \
if [ "$INSTALL_BNB" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},bitsandbytes"; \
fi; \
if [ "$INSTALL_VLLM" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},vllm"; \
fi; \
if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
fi; \
if [ "$INSTALL_LIGER_KERNEL" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},liger-kernel"; \
fi; \
if [ "$INSTALL_HQQ" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},hqq"; \
fi; \
if [ "$INSTALL_EETQ" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},eetq"; \
fi; \
if [ -n "$HTTP_PROXY" ]; then \
pip install --proxy=$HTTP_PROXY -e ".[$EXTRA_PACKAGES]"; \
else \
pip install -e ".[$EXTRA_PACKAGES]"; \
fi
# Rebuild flash attention
RUN pip uninstall -y transformer-engine flash-attn && \
if [ "$INSTALL_FLASHATTN" == "true" ]; then \
pip uninstall -y ninja && \
if [ -n "$HTTP_PROXY" ]; then \
pip install --proxy=$HTTP_PROXY ninja && \
pip install --proxy=$HTTP_PROXY --no-cache-dir flash-attn --no-build-isolation; \
else \
pip install ninja && \
pip install --no-cache-dir flash-attn --no-build-isolation; \
fi; \
fi
# Unset http proxy
RUN if [ -n "$HTTP_PROXY" ]; then \
unset http_proxy; \
unset https_proxy; \
fi
# Set up volumes
VOLUME [ "/root/.cache/huggingface", "/root/.cache/modelscope", "/app/data", "/app/output" ]
# Expose port 7860 for the LLaMA Board
ENV GRADIO_SERVER_PORT 7860
EXPOSE 7860
# Expose port 8000 for the API service
ENV API_PORT 8000
EXPOSE 8000
docker/docker-cuda/docker-compose.yml deleted 100644 → 0
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services:
llamafactory:
build:
dockerfile: ./docker/docker-cuda/Dockerfile
context: ../..
args:
INSTALL_BNB: "false"
INSTALL_VLLM: "false"
INSTALL_DEEPSPEED: "false"
INSTALL_FLASHATTN: "false"
INSTALL_LIGER_KERNEL: "false"
INSTALL_HQQ: "false"
INSTALL_EETQ: "false"
PIP_INDEX: https://pypi.org/simple
container_name: llamafactory
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../om_cache:/root/.cache/openmind
- ../../data:/app/data
- ../../output:/app/output
ports:
- "7860:7860"
- "8000:8000"
ipc: host
tty: true
shm_size: "16gb"
stdin_open: true
command: bash
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: "all"
capabilities: [gpu]
restart: unless-stopped
docker/docker-npu/Dockerfile deleted 100644 → 0
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# Use the Ubuntu 22.04 image with CANN 8.0.rc1
# More versions can be found at https://hub.docker.com/r/ascendai/cann/tags
# FROM ascendai/cann:8.0.rc1-910-ubuntu22.04-py3.8
FROM ascendai/cann:8.0.0-910b-ubuntu22.04-py3.10
# FROM ascendai/cann:8.0.rc1-910-openeuler22.03-py3.8
# FROM ascendai/cann:8.0.rc1-910b-openeuler22.03-py3.8
# Define environments
ENV DEBIAN_FRONTEND=noninteractive
# Define installation arguments
ARG INSTALL_DEEPSPEED=false
ARG PIP_INDEX=https://pypi.org/simple
ARG TORCH_INDEX=https://download.pytorch.org/whl/cpu
ARG HTTP_PROXY=
# Set the working directory
WORKDIR /app
# Set http proxy
RUN if [ -n "$HTTP_PROXY" ]; then \
echo "Configuring proxy..."; \
export http_proxy=$HTTP_PROXY; \
export https_proxy=$HTTP_PROXY; \
fi
# Install the requirements
COPY requirements.txt /app
RUN pip config set global.index-url "$PIP_INDEX" && \
pip config set global.extra-index-url "$TORCH_INDEX" && \
python -m pip install --upgrade pip && \
if [ -n "$HTTP_PROXY" ]; then \
python -m pip install --proxy=$HTTP_PROXY -r requirements.txt; \
else \
python -m pip install -r requirements.txt; \
fi
# Copy the rest of the application into the image
COPY . /app
# Install the LLaMA Factory
RUN EXTRA_PACKAGES="torch-npu,metrics"; \
if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
fi; \
if [ -n "$HTTP_PROXY" ]; then \
pip install --proxy=$HTTP_PROXY -e ".[$EXTRA_PACKAGES]"; \
else \
pip install -e ".[$EXTRA_PACKAGES]"; \
fi
# Unset http proxy
RUN if [ -n "$HTTP_PROXY" ]; then \
unset http_proxy; \
unset https_proxy; \
fi
# Set up volumes
VOLUME [ "/root/.cache/huggingface", "/root/.cache/modelscope", "/app/data", "/app/output" ]
# Expose port 7860 for the LLaMA Board
ENV GRADIO_SERVER_PORT 7860
EXPOSE 7860
# Expose port 8000 for the API service
ENV API_PORT 8000
EXPOSE 8000
docker/docker-npu/docker-compose.yml deleted 100644 → 0
View file @ 358bd2a0
services:
llamafactory:
build:
dockerfile: ./docker/docker-npu/Dockerfile
context: ../..
args:
INSTALL_DEEPSPEED: "false"
PIP_INDEX: https://pypi.org/simple
container_name: llamafactory
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../om_cache:/root/.cache/openmind
- ../../data:/app/data
- ../../output:/app/output
- /usr/local/dcmi:/usr/local/dcmi
- /usr/local/bin/npu-smi:/usr/local/bin/npu-smi
- /usr/local/Ascend/driver:/usr/local/Ascend/driver
- /etc/ascend_install.info:/etc/ascend_install.info
ports:
- "7860:7860"
- "8000:8000"
ipc: host
tty: true
shm_size: "16gb"
stdin_open: true
command: bash
devices:
- /dev/davinci0
- /dev/davinci_manager
- /dev/devmm_svm
- /dev/hisi_hdc
restart: unless-stopped
docker/docker-rocm/Dockerfile deleted 100644 → 0
View file @ 358bd2a0
FROM hardandheavy/transformers-rocm:2.2.0
# Define environments
ENV MAX_JOBS=4
ENV FLASH_ATTENTION_FORCE_BUILD=TRUE
ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
# Define installation arguments
ARG INSTALL_BNB=false
ARG INSTALL_VLLM=false
ARG INSTALL_DEEPSPEED=false
ARG INSTALL_FLASHATTN=false
ARG INSTALL_LIGER_KERNEL=false
ARG INSTALL_HQQ=false
ARG INSTALL_PYTORCH=true
ARG PIP_INDEX=https://pypi.org/simple
ARG HTTP_PROXY=
ARG PYTORCH_INDEX=https://download.pytorch.org/whl/nightly/rocm6.3
# Use Bash instead of default /bin/sh
SHELL ["/bin/bash", "-c"]
# Set the working directory
WORKDIR /app
# Set http proxy
RUN if [ -n "$HTTP_PROXY" ]; then \
echo "Configuring proxy..."; \
export http_proxy=$HTTP_PROXY; \
export https_proxy=$HTTP_PROXY; \
fi
# Install the requirements
COPY requirements.txt /app
RUN pip config set global.index-url "$PIP_INDEX" && \
pip config set global.extra-index-url "$PIP_INDEX" && \
python -m pip install --upgrade pip && \
if [ -n "$HTTP_PROXY" ]; then \
python -m pip install --proxy=$HTTP_PROXY -r requirements.txt; \
else \
python -m pip install -r requirements.txt; \
fi
# Copy the rest of the application into the image
COPY . /app
# Install the LLaMA Factory
RUN EXTRA_PACKAGES="metrics"; \
if [ "$INSTALL_BNB" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},bitsandbytes"; \
fi; \
if [ "$INSTALL_VLLM" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},vllm"; \
fi; \
if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
fi; \
if [ "$INSTALL_LIGER_KERNEL" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},liger-kernel"; \
fi; \
if [ "$INSTALL_HQQ" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},hqq"; \
fi; \
if [ -n "$HTTP_PROXY" ]; then \
pip install --proxy=$HTTP_PROXY -e ".[$EXTRA_PACKAGES]"; \
else \
pip install -e ".[$EXTRA_PACKAGES]"; \
fi
# Reinstall pytorch
# This is necessary to ensure that the correct version of PyTorch is installed
RUN if [ "$INSTALL_PYTORCH" == "true" ]; then \
pip uninstall -y torch torchvision torchaudio && \
pip install --pre torch torchvision torchaudio --index-url "$PYTORCH_INDEX"; \
fi
# Rebuild flash attention
RUN pip uninstall -y transformer-engine flash-attn && \
if [ "$INSTALL_FLASHATTN" == "true" ]; then \
pip uninstall -y ninja && \
if [ -n "$HTTP_PROXY" ]; then \
pip install --proxy=$HTTP_PROXY ninja && \
pip install --proxy=$HTTP_PROXY --no-cache-dir flash-attn --no-build-isolation; \
else \
pip install ninja && \
pip install --no-cache-dir flash-attn --no-build-isolation; \
fi; \
fi
# Unset http proxy
RUN if [ -n "$HTTP_PROXY" ]; then \
unset http_proxy; \
unset https_proxy; \
fi
# Set up volumes
VOLUME [ "/root/.cache/huggingface", "/root/.cache/modelscope", "/app/data", "/app/output" ]
# Expose port 7860 for the LLaMA Board
ENV GRADIO_SERVER_PORT 7860
EXPOSE 7860
# Expose port 8000 for the API service
ENV API_PORT 8000
EXPOSE 8000
docker/docker-rocm/docker-compose.yml deleted 100644 → 0
View file @ 358bd2a0
services:
llamafactory:
build:
dockerfile: ./docker/docker-rocm/Dockerfile
context: ../..
args:
INSTALL_BNB: "false"
INSTALL_VLLM: "false"
INSTALL_DEEPSPEED: "false"
INSTALL_FLASHATTN: "false"
INSTALL_LIGER_KERNEL: "false"
INSTALL_PYTORCH: "true"
INSTALL_HQQ: "false"
PIP_INDEX: https://pypi.org/simple
PYTORCH_INDEX: https://download.pytorch.org/whl/nightly/rocm6.3
container_name: llamafactory
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../om_cache:/root/.cache/openmind
- ../../data:/app/data
- ../../output:/app/output
- ../../saves:/app/saves
ports:
- "7860:7860"
- "8000:8000"
ipc: host
tty: true
shm_size: "16gb"
stdin_open: true
command: bash
devices:
- /dev/kfd:/dev/kfd
- /dev/dri:/dev/dri
restart: unless-stopped
requirements.txt
View file @ d1588ee7
transformers>=4.45.0,<=4.52.4,!=4.46.*,!=4.47.*,!=4.48.0,!=4.52.0; sys_platform != 'darwin'
transformers>=4.45.0,<=4.51.3,!=4.46.*,!=4.47.*,!=4.48.0,!=4.52.0; sys_platform == 'darwin'
# core deps
transformers>=4.49.0,<=4.52.4,!=4.52.0; sys_platform != 'darwin'
transformers>=4.49.0,<=4.51.3,!=4.52.0; sys_platform == 'darwin'
datasets>=2.16.0,<=3.6.0
accelerate>=0.34.0,<=1.7.0
accelerate>=1.3.0,<=1.7.0
peft>=0.14.0,<=0.15.2
trl>=0.8.6,<=0.9.6
tokenizers>=0.19.0,<=0.21.1
# gui
gradio>=4.38.0,<=5.31.0
scipy
matplotlib>=3.7.0
tyro<0.9.0
# ops
einops
numpy<2.0.0
pandas>=2.0.0
scipy
# model and tokenizer
sentencepiece
tiktoken
protobuf
uvicorn
fastapi
sse-starlette
matplotlib>=3.7.0
modelscope>=1.14.0
hf-transfer
# python
fire
omegaconf
packaging
protobuf
pyyaml
numpy<2.0.0
pydantic<=2.10.6
pandas>=2.0.0
# api
uvicorn
fastapi
sse-starlette
# media
av
librosa
tyro<0.9.0
setup.py
View file @ d1588ee7
......@@ -43,7 +43,7 @@ def get_console_scripts() -> list[str]:
extra_require = {
"torch": ["torch>=2.0.0", "torchvision>=0.15.0"],
"torch-npu": ["torch==2.4.0", "torch-npu==2.4.0.post2", "decorator"],
"torch-npu": ["torch-npu==2.5.1", "torchvision==0.20.1", "decorator"],
"metrics": ["nltk", "jieba", "rouge-chinese"],
"deepspeed": ["deepspeed>=0.10.0,<=0.16.9"],
"liger-kernel": ["liger-kernel>=0.5.5"],
......@@ -52,7 +52,7 @@ extra_require = {
"eetq": ["eetq"],
"gptq": ["optimum>=1.24.0", "gptqmodel>=2.0.0"],
"aqlm": ["aqlm[gpu]>=1.1.0"],
"vllm": ["vllm>=0.4.3,<=0.8.6"],
"vllm": ["vllm>=0.4.3,<=0.9.1"],
"sglang": ["sglang[srt]>=0.4.5", "transformers==4.51.1"],
"galore": ["galore-torch"],
"apollo": ["apollo-torch"],
......@@ -68,7 +68,6 @@ extra_require = {
"referencing",
"jsonschema_specifications",
],
"modelscope": ["modelscope"],
"openmind": ["openmind"],
"swanlab": ["swanlab"],
"dev": ["pre-commit", "ruff", "pytest", "build"],
......
src/llamafactory/api/chat.py
View file @ d1588ee7
......@@ -132,7 +132,7 @@ def _process_request(
if re.match(r"^data:video\/(mp4|mkv|avi|mov);base64,(.+)$", video_url): # base64 video
video_stream = io.BytesIO(base64.b64decode(video_url.split(",", maxsplit=1)[1]))
elif os.path.isfile(video_url): # local file
video_stream = open(video_url, "rb")
video_stream = video_url
else: # web uri
video_stream = requests.get(video_url, stream=True).raw
......@@ -143,7 +143,7 @@ def _process_request(
if re.match(r"^data:audio\/(mpeg|mp3|wav|ogg);base64,(.+)$", audio_url): # base64 audio
audio_stream = io.BytesIO(base64.b64decode(audio_url.split(",", maxsplit=1)[1]))
elif os.path.isfile(audio_url): # local file
audio_stream = open(audio_url, "rb")
audio_stream = audio_url
else: # web uri
audio_stream = requests.get(audio_url, stream=True).raw
......
src/llamafactory/data/collator.py
View file @ d1588ee7
......@@ -210,7 +210,8 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
if (
self.model is not None
and getattr(self.model.config, "model_type", None) in ["qwen2_vl", "qwen2_5_vl", "qwen2_5_omni_thinker"]
and getattr(self.model.config, "model_type", None)
in ["glm4v", "qwen2_vl", "qwen2_5_vl", "qwen2_5_omni_thinker"]
and ("position_ids" not in features or features["position_ids"].dim() != 3)
):
raise ValueError("Qwen2-VL/Qwen2.5-Omni model requires 3D position ids for mrope.")
......
src/llamafactory/data/loader.py
View file @ d1588ee7
......@@ -91,7 +91,7 @@ def _load_single_dataset(
raise NotImplementedError(f"Unknown load type: {dataset_attr.load_from}.")
if dataset_attr.load_from == "ms_hub":
check_version("modelscope>=1.11.0", mandatory=True)
check_version("modelscope>=1.14.0", mandatory=True)
from modelscope import MsDataset # type: ignore
from modelscope.utils.config_ds import MS_DATASETS_CACHE # type: ignore
......
src/llamafactory/data/mm_plugin.py
View file @ d1588ee7
......@@ -27,6 +27,10 @@ from typing import TYPE_CHECKING, BinaryIO, Literal, Optional, TypedDict, Union
import numpy as np
import torch
from transformers.image_utils import get_image_size, is_valid_image, to_numpy_array
from transformers.models.mllama.processing_mllama import (
convert_sparse_cross_attention_mask_to_dense,
get_cross_attention_token_mask,
)
from typing_extensions import override
from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
......@@ -51,17 +55,10 @@ if is_pyav_available():
import av
if is_transformers_version_greater_than("4.45.0"):
from transformers.models.mllama.processing_mllama import (
convert_sparse_cross_attention_mask_to_dense,
get_cross_attention_token_mask,
)
if is_transformers_version_greater_than("4.52.0"):
from transformers.image_utils import make_flat_list_of_images
from transformers.video_utils import make_batched_videos
elif is_transformers_version_greater_than("4.49.0"):
else:
from transformers.image_utils import make_batched_videos, make_flat_list_of_images
......@@ -298,11 +295,8 @@ class MMPluginMixin:
r"""Regularizes audios to avoid error. Including reading and resampling."""
results, sampling_rates = [], []
for audio in audios:
if isinstance(audio, (str, BinaryIO)):
audio, sampling_rate = librosa.load(audio, sr=sampling_rate)
if not isinstance(audio, np.ndarray):
raise ValueError(f"Expect input is a list of audios, but got {type(audio)}.")
audio, sampling_rate = librosa.load(audio, sr=sampling_rate)
results.append(audio)
sampling_rates.append(sampling_rate)
......@@ -391,7 +385,7 @@ class MMPluginMixin:
return_tensors="pt",
)
)
mm_inputs["feature_attention_mask"] = mm_inputs.pop("attention_mask") # prevent conflicts
mm_inputs["feature_attention_mask"] = mm_inputs.pop("attention_mask", None) # prevent conflicts
return mm_inputs
......@@ -512,6 +506,39 @@ class Gemma3Plugin(BasePlugin):
return mm_inputs
class Gemma3nPlugin(Gemma3Plugin):
@override
def process_messages(
self,
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
self._validate_messages(messages, images, videos, audios)
messages = deepcopy(messages)
boi_token: str = getattr(processor, "boi_token")
boa_token: str = getattr(processor, "boa_token")
full_image_sequence: str = getattr(processor, "full_image_sequence")
full_audio_sequence: str = getattr(processor, "full_audio_sequence")
image_str = full_image_sequence if self.expand_mm_tokens else boi_token
audio_str = full_audio_sequence if self.expand_mm_tokens else boa_token
for message in messages:
content = message["content"]
while IMAGE_PLACEHOLDER in content:
content = content.replace(IMAGE_PLACEHOLDER, image_str, 1)
while AUDIO_PLACEHOLDER in content:
content = content.replace(AUDIO_PLACEHOLDER, audio_str, 1)
message["content"] = content
return messages
@dataclass
class InternVLPlugin(BasePlugin):
@override
......@@ -1501,6 +1528,133 @@ class Qwen2VLPlugin(BasePlugin):
return messages
@dataclass
class GLM4VPlugin(Qwen2VLPlugin):
@override
def _get_mm_inputs(
self,
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: "MMProcessor",
) -> dict[str, "torch.Tensor"]:
image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
video_processor: BaseImageProcessor = getattr(processor, "video_processor", None)
mm_inputs = {}
if len(images) != 0:
images = self._regularize_images(
images,
image_max_pixels=getattr(processor, "image_max_pixels", 768 * 768),
image_min_pixels=getattr(processor, "image_min_pixels", 32 * 32),
)["images"]
mm_inputs.update(image_processor(images, return_tensors="pt"))
if len(videos) != 0:
video_data = self._regularize_videos(
videos,
image_max_pixels=getattr(processor, "video_max_pixels", 256 * 256),
image_min_pixels=getattr(processor, "video_min_pixels", 16 * 16),
video_fps=getattr(processor, "video_fps", 2.0),
video_maxlen=getattr(processor, "video_maxlen", 128),
)
# prepare video metadata
video_metadata = [
{"fps": 2, "duration": len(video), "total_frames": len(video)} for video in video_data["videos"]
]
mm_inputs.update(video_processor(images=None, videos=video_data["videos"], video_metadata=video_metadata))
return mm_inputs
@override
def process_messages(
self,
messages: list[dict[str, str]],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: Optional["MMProcessor"],
) -> list[dict[str, str]]:
self._validate_input(processor, images, videos, audios)
self._validate_messages(messages, images, videos, audios)
num_image_tokens, num_video_tokens = 0, 0
messages = deepcopy(messages)
image_processor: BaseImageProcessor = getattr(processor, "image_processor")
merge_length: int = getattr(image_processor, "merge_size") ** 2
if self.expand_mm_tokens:
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
image_grid_thw = mm_inputs.get("image_grid_thw", [])
video_grid_thw = mm_inputs.get("video_grid_thw", [])
num_frames = video_grid_thw[0][0] if len(video_grid_thw) > 0 else 0 # hard code for now
timestamps = mm_inputs.get("timestamps", [])
if hasattr(timestamps, "tolist"):
timestamps = timestamps.tolist()
if not timestamps:
timestamps_list = []
elif isinstance(timestamps[0], list):
timestamps_list = timestamps[0]
else:
timestamps_list = timestamps
unique_timestamps = timestamps_list.copy()
selected_timestamps = unique_timestamps[:num_frames]
while len(selected_timestamps) < num_frames:
selected_timestamps.append(selected_timestamps[-1] if selected_timestamps else 0)
else:
image_grid_thw = [None] * len(images)
video_grid_thw = [None] * len(videos)
num_frames = 0
selected_timestamps = [0]
for message in messages:
content = message["content"]
while IMAGE_PLACEHOLDER in content:
image_seqlen = image_grid_thw[num_image_tokens].prod() // merge_length if self.expand_mm_tokens else 1
content = content.replace(
IMAGE_PLACEHOLDER, f"<|begin_of_image|>{self.image_token * image_seqlen}<|end_of_image|>", 1
)
num_image_tokens += 1
while VIDEO_PLACEHOLDER in content:
video_structure = ""
for frame_index in range(num_frames):
video_seqlen = (
video_grid_thw[num_video_tokens][1:].prod() // merge_length if self.expand_mm_tokens else 1
)
timestamp_sec = selected_timestamps[frame_index]
frame_structure = (
f"<|begin_of_image|>{self.image_token * video_seqlen}<|end_of_image|>{timestamp_sec}"
)
video_structure += frame_structure
content = content.replace(VIDEO_PLACEHOLDER, f"<|begin_of_video|>{video_structure}<|end_of_video|>", 1)
num_video_tokens += 1
message["content"] = content
return messages
@override
def get_mm_inputs(
self,
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
imglens: list[int],
vidlens: list[int],
audlens: list[int],
batch_ids: list[list[int]],
processor: Optional["ProcessorMixin"],
) -> dict[str, Union[list[int], "torch.Tensor"]]:
self._validate_input(processor, images, videos, audios)
mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
mm_inputs.pop("timestamps", None)
return mm_inputs
class Qwen2OmniPlugin(Qwen2VLPlugin):
@override
def _get_mm_inputs(
......@@ -1718,6 +1872,8 @@ class VideoLlavaPlugin(BasePlugin):
PLUGINS = {
"base": BasePlugin,
"gemma3": Gemma3Plugin,
"glm4v": GLM4VPlugin,
"gemma3n": Gemma3nPlugin,
"intern_vl": InternVLPlugin,
"kimi_vl": KimiVLPlugin,
"llama4": Llama4Plugin,
......
src/llamafactory/data/template.py
View file @ d1588ee7
......@@ -916,6 +916,18 @@ register_template(
)
# copied from chatml template
register_template(
name="falcon_h1",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<|im_end|>", "<|end_of_text|>"],
)
register_template(
name="fewshot",
format_assistant=StringFormatter(slots=["{{content}}\n\n"]),
......@@ -939,6 +951,22 @@ register_template(
)
# copied from gemma template
register_template(
name="gemma2",
format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
format_system=StringFormatter(slots=["{{content}}\n\n"]),
format_observation=StringFormatter(
slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<eos>", "<end_of_turn>"],
efficient_eos=True,
template_class=Llama2Template,
)
# copied from gemma template
register_template(
name="gemma3",
......@@ -956,6 +984,22 @@ register_template(
)
register_template(
name="gemma3n",
format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
format_assistant=StringFormatter(slots=["{{content}}<end_of_turn>\n"]),
format_system=StringFormatter(slots=["{{content}}\n\n"]),
format_observation=StringFormatter(
slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<end_of_turn>"],
replace_eos=True,
mm_plugin=get_mm_plugin("gemma3n", image_token="<image_soft_token>", audio_token="<audio_soft_token>"),
template_class=Llama2Template,
)
register_template(
name="glm4",
format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),
......@@ -970,6 +1014,23 @@ register_template(
)
# copied from glm4 template
register_template(
name="glm4v",
format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),
format_assistant=StringFormatter(slots=["\n{{content}}"]),
format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
format_function=FunctionFormatter(slots=["{{content}}"], tool_format="glm4"),
format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>"]),
format_tools=ToolFormatter(tool_format="glm4"),
format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
stop_words=["<|user|>", "<|observation|>", "</answer>"],
efficient_eos=True,
mm_plugin=get_mm_plugin(name="glm4v", image_token="<|image|>", video_token="<|video|>"),
template_class=ReasoningTemplate,
)
# copied from glm4 template
register_template(
name="glmz1",
......
src/llamafactory/data/tool_utils.py
View file @ d1588ee7
......@@ -38,8 +38,8 @@ DEFAULT_TOOL_PROMPT = (
)
GLM4_TOOL_PROMPT = (
"你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，"
"你的任务是针对用户的问题和要求提供适当的答复和支持。# 可用工具{tool_text}"
"你是一个名为 ChatGLM 的人工智能助手。你是基于智谱 AI 公司训练的语言模型 GLM-4 模型开发的，"
"你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{tool_text}"
)
LLAMA3_TOOL_PROMPT = (
......
src/llamafactory/extras/constants.py
View file @ d1588ee7
......@@ -589,6 +589,17 @@ register_model_group(
)
register_model_group(
models={
"Devstral-Small-2507-Instruct": {
DownloadSource.DEFAULT: "mistralai/Devstral-Small-2507",
DownloadSource.MODELSCOPE: "mistralai/Devstral-Small-2507",
},
},
template="mistral_small",
)
register_model_group(
models={
"EXAONE-3.0-7.8B-Instruct": {
......@@ -633,6 +644,60 @@ register_model_group(
template="falcon",
)
register_model_group(
models={
"Falcon-H1-0.5B-Base": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-0.5B-Base",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-0.5B-Base",
},
"Falcon-H1-1.5B-Base": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-1.5B-Base",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Base",
},
"Falcon-H1-1.5B-Deep-Base": {
DownloadSource.DEFAULT: "tiuae/Falcon-H1-1.5B-Deep-Base",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Deep-Base",
},
"Falcon-H1-3B-Base": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-3B-Base",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-3B-Base",
},
"Falcon-H1-7B-Base": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-7B-Base",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-7B-Base",
},
"Falcon-H1-34B-Base": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-34B-Base",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-34B-Base",
},
"Falcon-H1-0.5B-Instruct": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-0.5B-Instruct",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-0.5B-Instruct",
},
"Falcon-H1-1.5B-Instruct": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-1.5B-Instruct",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Instruct",
},
"Falcon-H1-1.5B-Deep-Instruct": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-1.5B-Deep-Instruct",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Deep-Instruct",
},
"Falcon-H1-3B-Instruct": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-3B-Instruct",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-3B-Instruct",
},
"Falcon-H1-7B-Instruct": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-7B-Instruct",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-7B-Instruct",
},
"Falcon-H1-34B-Instruct": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-34B-Instruct",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-34B-Instruct",
},
},
template="falcon_h1",
)
register_model_group(
models={
......@@ -658,6 +723,13 @@ register_model_group(
"Gemma-1.1-7B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-1.1-7b-it",
},
},
template="gemma",
)
register_model_group(
models={
"Gemma-2-2B": {
DownloadSource.DEFAULT: "google/gemma-2-2b",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-2b",
......@@ -697,7 +769,7 @@ register_model_group(
DownloadSource.MODELSCOPE: "google/medgemma-27b-text-it",
},
},
template="gemma",
template="gemma2",
)
......@@ -741,6 +813,30 @@ register_model_group(
)
register_model_group(
models={
"Gemma-3n-E2B": {
DownloadSource.DEFAULT: "google/gemma-3n-E2B",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E2B",
},
"Gemma-3n-E4B": {
DownloadSource.DEFAULT: "google/gemma-3n-E4B",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E4B",
},
"Gemma-3n-E2B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-3n-E2B-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E2B-it",
},
"Gemma-3n-E4B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-3n-E4B-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E4B-it",
},
},
template="gemma3n",
multimodal=True,
)
register_model_group(
models={
"GLM-4-9B": {
......@@ -773,6 +869,22 @@ register_model_group(
)
register_model_group(
models={
"GLM-4.1V-9B-Base": {
DownloadSource.DEFAULT: "THUDM/GLM-4.1V-9B-Base",
DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.1V-9B-Base",
},
"GLM-4.1V-9B-Thinking": {
DownloadSource.DEFAULT: "THUDM/GLM-4.1V-9B-Thinking",
DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.1V-9B-Thinking",
},
},
template="glm4v",
multimodal=True,
)
register_model_group(
models={
"GLM-Z1-0414-9B-Chat": {
......@@ -1089,6 +1201,17 @@ register_model_group(
)
register_model_group(
models={
"Kimi-Dev-72B-Instruct": {
DownloadSource.DEFAULT: "moonshotai/Kimi-Dev-72B",
DownloadSource.MODELSCOPE: "moonshotai/Kimi-Dev-72B",
},
},
template="qwen",
)
register_model_group(
models={
"Kimi-VL-A3B-Instruct": {
......@@ -1099,6 +1222,10 @@ register_model_group(
DownloadSource.DEFAULT: "moonshotai/Kimi-VL-A3B-Thinking",
DownloadSource.MODELSCOPE: "moonshotai/Kimi-VL-A3B-Thinking",
},
"Kimi-VL-A3B-Thinking-2506": {
DownloadSource.DEFAULT: "moonshotai/Kimi-VL-A3B-Thinking-2506",
DownloadSource.MODELSCOPE: "moonshotai/Kimi-VL-A3B-Thinking-2506",
},
},
template="kimi_vl",
multimodal=True,
......@@ -1617,6 +1744,10 @@ register_model_group(
DownloadSource.DEFAULT: "mistralai/Mistral-Small-3.1-24B-Instruct-2503",
DownloadSource.MODELSCOPE: "mistralai/Mistral-Small-3.1-24B-Instruct-2503",
},
"Mistral-Small-3.2-24B-Instruct": {
DownloadSource.DEFAULT: "mistralai/Mistral-Small-3.2-24B-Instruct-2506",
DownloadSource.MODELSCOPE: "mistralai/Mistral-Small-3.2-24B-Instruct-2506",
},
},
template="mistral_small",
multimodal=True,
......
src/llamafactory/extras/logging.py
View file @ d1588ee7
......@@ -50,7 +50,7 @@ class LoggerHandler(logging.Handler):
def _write_log(self, log_entry: str) -> None:
with open(self.running_log, "a", encoding="utf-8") as f:
f.write(log_entry + "\n\n")
f.write(log_entry + "\n")
def emit(self, record) -> None:
if record.name == "httpx":
......
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