Instructions to use ocisd4/openllama-zh-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ocisd4/openllama-zh-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ocisd4/openllama-zh-7B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ocisd4/openllama-zh-7B") model = AutoModelForCausalLM.from_pretrained("ocisd4/openllama-zh-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ocisd4/openllama-zh-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ocisd4/openllama-zh-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ocisd4/openllama-zh-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ocisd4/openllama-zh-7B
- SGLang
How to use ocisd4/openllama-zh-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 "ocisd4/openllama-zh-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": "ocisd4/openllama-zh-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 "ocisd4/openllama-zh-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": "ocisd4/openllama-zh-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ocisd4/openllama-zh-7B with Docker Model Runner:
docker model run hf.co/ocisd4/openllama-zh-7B
Download tokenizer.model from ocisd4/openllama-zh-7B: direct link, hf CLI and curl.
- Browser
- Download file 828 kB
-
https://huggingface.co/ocisd4/openllama-zh-7B/resolve/main/tokenizer.model
- Command line
-
hf download hf://ocisd4/openllama-zh-7B/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/ocisd4/openllama-zh-7B/resolve/main/tokenizer.model
828 kB
- Xet hash:
- fbab9ef5d4e4fb2fe4fea4b917f04e6fe507226111d32fad8e899e7edf794f42
- Size of remote file:
- 828 kB
- SHA256:
- 92a4b32f17afc26c60e42accff53f43c3ce9b137b06fdd198e4ca7d3a129930f
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