Instructions to use LinkSoul/Chinese-Llama-2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinkSoul/Chinese-Llama-2-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LinkSoul/Chinese-Llama-2-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LinkSoul/Chinese-Llama-2-7b") model = AutoModelForCausalLM.from_pretrained("LinkSoul/Chinese-Llama-2-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LinkSoul/Chinese-Llama-2-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LinkSoul/Chinese-Llama-2-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LinkSoul/Chinese-Llama-2-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LinkSoul/Chinese-Llama-2-7b
- SGLang
How to use LinkSoul/Chinese-Llama-2-7b 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 "LinkSoul/Chinese-Llama-2-7b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LinkSoul/Chinese-Llama-2-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "LinkSoul/Chinese-Llama-2-7b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LinkSoul/Chinese-Llama-2-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LinkSoul/Chinese-Llama-2-7b with Docker Model Runner:
docker model run hf.co/LinkSoul/Chinese-Llama-2-7b
Download .github/demo.gif from LinkSoul/Chinese-Llama-2-7b: direct link, hf CLI and curl.
- Browser
- Download file 2.9 MB
-
https://huggingface.co/LinkSoul/Chinese-Llama-2-7b/resolve/main/.github/demo.gif
- Command line
-
hf download hf://LinkSoul/Chinese-Llama-2-7b/.github/demo.gif
-
curl -L -o demo.gif https://huggingface.co/LinkSoul/Chinese-Llama-2-7b/resolve/main/.github/demo.gif
2.9 MB

- Xet hash:
- d7343e8a962f9cfcd0d9ac9b70f2a89d0d3f81576661a5a04dc5eb5151e3839e
- Size of remote file:
- 2.9 MB
- SHA256:
- 8884579d0e75f6f46d3931794bddacab57f8faa9dfafcaa6f28b8ca36c1ab39e
·
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