Instructions to use shareAI/llama3.1-8b-instruct-dpo-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shareAI/llama3.1-8b-instruct-dpo-zh with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="shareAI/llama3.1-8b-instruct-dpo-zh",
	filename="llama3.1_8b_chinese_chat_q4_k_m-shareAI.gguf",
)

llm.create_chat_completion(
	messages = "{\n    \"question\": \"What is my name?\",\n    \"context\": \"My name is Clara and I live in Berkeley.\"\n}"
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use shareAI/llama3.1-8b-instruct-dpo-zh with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shareAI/llama3.1-8b-instruct-dpo-zh:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf shareAI/llama3.1-8b-instruct-dpo-zh:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shareAI/llama3.1-8b-instruct-dpo-zh:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf shareAI/llama3.1-8b-instruct-dpo-zh:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf shareAI/llama3.1-8b-instruct-dpo-zh:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf shareAI/llama3.1-8b-instruct-dpo-zh:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf shareAI/llama3.1-8b-instruct-dpo-zh:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shareAI/llama3.1-8b-instruct-dpo-zh:Q4_K_M

Use Docker

docker model run hf.co/shareAI/llama3.1-8b-instruct-dpo-zh:Q4_K_M

LM Studio
Jan
Ollama
How to use shareAI/llama3.1-8b-instruct-dpo-zh with Ollama:
```
ollama run hf.co/shareAI/llama3.1-8b-instruct-dpo-zh:Q4_K_M
```

Unsloth Studio new

How to use shareAI/llama3.1-8b-instruct-dpo-zh with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for shareAI/llama3.1-8b-instruct-dpo-zh to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for shareAI/llama3.1-8b-instruct-dpo-zh to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for shareAI/llama3.1-8b-instruct-dpo-zh to start chatting

Docker Model Runner
How to use shareAI/llama3.1-8b-instruct-dpo-zh with Docker Model Runner:
```
docker model run hf.co/shareAI/llama3.1-8b-instruct-dpo-zh:Q4_K_M
```

Lemonade

How to use shareAI/llama3.1-8b-instruct-dpo-zh with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull shareAI/llama3.1-8b-instruct-dpo-zh:Q4_K_M

Run and chat with the model

lemonade run user.llama3.1-8b-instruct-dpo-zh-Q4_K_M

List all available models

lemonade list

llama3.1-8b-instruct 中文DPO版

模型介绍

像原版instruct一样，喜欢用有趣中文和表情符号回答问题。
Github：https://github.com/CrazyBoyM/llama3-Chinese-chat

特点：偏好中文和emoji表情，且不损伤原instruct版模型能力。实测中文DPO版问答性能体验超过现在市面上任何llama3.1中文微调版（微调会大面积破坏llama3.1原版能力，导致遗忘）

训练细节

DPO(beta 0.5) + lora rank128, alpha256 + 打开"lm_head", "input_layernorm", "post_attention_layernorm", "norm"层训练.

模型部署

网页脚本文件：https://github.com/CrazyBoyM/llama3-Chinese-chat/blob/main/deploy/web_streamlit_for_instruct_v2.py
已经在模型文件夹里内置了一份web.py，可以直接使用。

pip install streamlit
pip install transformers==4.40.1
streamlit run web.py ./llama3.1-8b-instruct-dpo-zh

模型下载

SDK下载

#安装ModelScope
pip install modelscope

#SDK模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('shareAI/llama3.1-8b-instruct-dpo-zh')

Git下载

#Git模型下载
git clone https://www.modelscope.cn/shareAI/llama3.1-8b-instruct-dpo-zh.git

目前已经上传 Q4 K_M 的GGUF量化模型，后续将进一步进行手机端或边缘算力端展示

Downloads last month: 60

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

shareAI
/

llama3.1-8b-instruct-dpo-zh

llama3.1-8b-instruct 中文DPO版

模型介绍

训练细节

模型部署

模型下载

Dataset used to train shareAI/llama3.1-8b-instruct-dpo-zh