hfl/llama-3-chinese-8b-instruct · Hugging Face (original) (raw)
Instructions to use hfl/llama-3-chinese-8b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/llama-3-chinese-8b-instruct with Transformers:
Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="hfl/llama-3-chinese-8b-instruct")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages)
Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("hfl/llama-3-chinese-8b-instruct")
model = AutoModelForMultimodalLM.from_pretrained("hfl/llama-3-chinese-8b-instruct")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
- Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hfl/llama-3-chinese-8b-instruct with vLLM:
Install from pip and serve model
Install vLLM from pip:
pip install vllm
Start the vLLM server:
vllm serve "hfl/llama-3-chinese-8b-instruct"
Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "hfl/llama-3-chinese-8b-instruct",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'
Use Docker
docker model run hf.co/hfl/llama-3-chinese-8b-instruct
- SGLang
How to use hfl/llama-3-chinese-8b-instruct 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 "hfl/llama-3-chinese-8b-instruct" \
--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": "hfl/llama-3-chinese-8b-instruct",
"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=" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "hfl/llama-3-chinese-8b-instruct" \
--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": "hfl/llama-3-chinese-8b-instruct",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'
- Docker Model Runner
How to use hfl/llama-3-chinese-8b-instruct with Docker Model Runner:
docker model run hf.co/hfl/llama-3-chinese-8b-instruct - Browse Quantizations
to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.
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