Text Generation
Transformers
Safetensors
Chinese
English
baihu_ssa
sparse-attention
subq
ssa
long-context
commercial-license-required
conversational
Instructions to use ZichenAI/BaiHu-V1-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZichenAI/BaiHu-V1-Flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ZichenAI/BaiHu-V1-Flash") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ZichenAI/BaiHu-V1-Flash", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ZichenAI/BaiHu-V1-Flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ZichenAI/BaiHu-V1-Flash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZichenAI/BaiHu-V1-Flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ZichenAI/BaiHu-V1-Flash
- SGLang
How to use ZichenAI/BaiHu-V1-Flash with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ZichenAI/BaiHu-V1-Flash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZichenAI/BaiHu-V1-Flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ZichenAI/BaiHu-V1-Flash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZichenAI/BaiHu-V1-Flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ZichenAI/BaiHu-V1-Flash with Docker Model Runner:
docker model run hf.co/ZichenAI/BaiHu-V1-Flash
Download .gitattributes from ZichenAI/BaiHu-V1-Flash: direct link, hf CLI and curl.
- Browser
- Download file 1.52 kB
-
https://huggingface.co/ZichenAI/BaiHu-V1-Flash/resolve/main/.gitattributes
- Command line
-
hf download hf://ZichenAI/BaiHu-V1-Flash/.gitattributes
-
curl -L -o .gitattributes https://huggingface.co/ZichenAI/BaiHu-V1-Flash/resolve/main/.gitattributes
1.52 kB
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