Instructions to use NovaAI6868/BaiHu-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use NovaAI6868/BaiHu-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="NovaAI6868/BaiHu-gguf", filename="BaiHu-v2.F16-mmproj.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use NovaAI6868/BaiHu-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf NovaAI6868/BaiHu-gguf:F16 # Run inference directly in the terminal: llama cli -hf NovaAI6868/BaiHu-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf NovaAI6868/BaiHu-gguf:F16 # Run inference directly in the terminal: llama cli -hf NovaAI6868/BaiHu-gguf:F16
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 NovaAI6868/BaiHu-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf NovaAI6868/BaiHu-gguf:F16
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 NovaAI6868/BaiHu-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf NovaAI6868/BaiHu-gguf:F16
Use Docker
docker model run hf.co/NovaAI6868/BaiHu-gguf:F16
- LM Studio
- Jan
- vLLM
How to use NovaAI6868/BaiHu-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NovaAI6868/BaiHu-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NovaAI6868/BaiHu-gguf", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/NovaAI6868/BaiHu-gguf:F16
- Ollama
How to use NovaAI6868/BaiHu-gguf with Ollama:
ollama run hf.co/NovaAI6868/BaiHu-gguf:F16
- Unsloth Studio
How to use NovaAI6868/BaiHu-gguf 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 NovaAI6868/BaiHu-gguf 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 NovaAI6868/BaiHu-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for NovaAI6868/BaiHu-gguf to start chatting
- Pi
How to use NovaAI6868/BaiHu-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf NovaAI6868/BaiHu-gguf:F16
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "NovaAI6868/BaiHu-gguf:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use NovaAI6868/BaiHu-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf NovaAI6868/BaiHu-gguf:F16
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default NovaAI6868/BaiHu-gguf:F16
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use NovaAI6868/BaiHu-gguf with Docker Model Runner:
docker model run hf.co/NovaAI6868/BaiHu-gguf:F16
- Lemonade
How to use NovaAI6868/BaiHu-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull NovaAI6868/BaiHu-gguf:F16
Run and chat with the model
lemonade run user.BaiHu-gguf-F16
List all available models
lemonade list
白虎-v2
白虎-v2 是一款多模态大语言模型,支持文本、图像、音频与视频输入,适用于中文场景下的多模态理解与生成任务。
模型简介
- 模型名称: 白虎-v2
- 上下文长度: 131,072 tokens
- 词表大小: 262,144
- 数据类型: float16
- 支持模态: 文本 / 图像 / 音频 / 视频
仓库文件说明
| 文件 | 说明 |
|---|---|
BaiHu-v2.Q4_K_M.gguf |
主模型 GGUF 量化版本(Q4_K_M),适合本地 CPU/GPU 推理 |
BaiHu-v2.F16-mmproj.gguf |
多模态投影层(mmproj)FP16 版本,配合主模型用于图像/音频/视频理解 |
Modelfile |
llama.cpp / Ollama 的模型配置文件示例 |
推荐搭配使用:
BaiHu-v2.Q4_K_M.gguf+BaiHu-v2.F16-mmproj.gguf
模型能力
- 中文多轮对话
- 图像描述与视觉问答
- 音频内容理解
- 视频内容理解
- 工具调用
使用方法
使用 llama.cpp / Ollama 推理
参考仓库中的 Modelfile 创建 Ollama 模型:
ollama create BaiHu-v2 -f Modelfile
ollama run BaiHu-v2
使用 llama.cpp 命令行
./llama-cli \
-m BaiHu-v2.Q4_K_M.gguf \
--mmproj BaiHu-v2.F16-mmproj.gguf \
--image example.jpg \
-p "请描述这张图片:"
使用 transformers(完整模型)
完整 PyTorch/Safetensors 版本请参考配套仓库。本仓库仅提供 GGUF 量化版本。
模型配置
- 文本模型: 35 层,隐藏维度 1536,8 头注意力
- 视觉编码器: 16 层,隐藏维度 768,图像 token 数 280
- 音频编码器: 12 层,隐藏维度 1024
- 视频: 支持 32 帧采样,每帧最大 70 个 soft token
训练信息
- 训练框架: Unsloth
- Unsloth 版本: 2026.6.8
- 优化目标: 在保持多模态能力的同时,提升中文指令跟随与对话质量
免责声明
本模型生成的内容可能受训练数据影响。请勿将模型输出作为专业建议(医疗、法律、金融等)使用。模型可能存在幻觉、偏见或不准确信息,请谨慎使用并自行验证。
授权协议
本模型采用 MIT 协议 开源。使用本模型前请仔细阅读并遵守 MIT 许可协议条款。
致谢
- Downloads last month
- 45
4-bit