Instructions to use hfl/llama-3-chinese-8b-instruct-v3 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-v3 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-v3") 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-v3") model = AutoModelForMultimodalLM.from_pretrained("hfl/llama-3-chinese-8b-instruct-v3") 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-v3 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-v3" # 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-v3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/hfl/llama-3-chinese-8b-instruct-v3
- SGLang
How to use hfl/llama-3-chinese-8b-instruct-v3 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-v3" \ --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-v3", "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 "hfl/llama-3-chinese-8b-instruct-v3" \ --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-v3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use hfl/llama-3-chinese-8b-instruct-v3 with Docker Model Runner:
docker model run hf.co/hfl/llama-3-chinese-8b-instruct-v3
## License incompatibility
Hi,I'd like to report a license conflict in hfl/llama-3-chinese-8b-instruct-v3. I noticed that this model was fine-tuned from meta-llama/Meta-Llama-3-8B-Instruct, but it's currently published under the Apache-2.0 license. After taking a look at the META LLAMA 3 COMMUNITY LICENSE AGREEMENT, I found there can be a mismatch between different licensing terms. This inconsistency can make it confusing for people to understand what rules to follow when they use or share the model."
⚠️ Key violations of META LLAMA 3 COMMUNITY LICENSE AGREEMENT:
Clause 1.b.i – Redistribution and Use:
• ⚠️ No license file included (should contain the META LLAMA 3 COMMUNITY LICENSE AGREEMENT)
• ⚠️ "Built with Meta Llama 3" is not prominently displayed
• ⚠️ Model name does not begin with “Llama 3”, which is required for any derivative
Clause 1.b.iii – Required Notice:
• ⚠️ Missing the following required text in a "NOTICE" file:
“Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.”
Clause 1.iv – Acceptable Use Policy:
• ⚠️ No mention of Meta’s Acceptable Use Policy, which must be passed on to downstream users
Clause 2 – Additional Commercial Terms:
• ⚠️ No clarification about the 700M MAU (monthly active users) threshold — making commercial usage ambiguous
On the flip side, Apache-2.0 lets you:
• Use it commercially without asking for extra permission
• Sublicense and redistribute it under more flexible terms
• You don’t have to pass along any non-permissive terms or use restrictions from upstream
This creates a bit of a conflict because the LLaMA 3 license specifically says you can’t sublicense it under more flexible terms and requires downstream users to follow certain use restrictions, which Apache-2.0 doesn’t enforce.
So I'm thinking there might be a licensing conflict here that needs to be sorted out.
🔹 Suggestion:
1. To make sure everything aligns with the LLaMA 3 terms, you might want to tweak the licensing setup a bit, like:
• Maybe include a copy of the LLaMA 3 Community License in the repo or model card
• Include this notice in a “NOTICE” file or the docs:
> “Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.”
• A “Built with Meta Llama 3” note somewhere in the model card could be helpful too
• Maybe a quick note about usage restrictions, especially for folks using it in commercial settings
• A statement clarifying that use of the model must comply with Meta’s Acceptable Use Policy
**2.**Or, we could just drop the Apache-2.0 tag and go with the LLaMA 3 Community License. This could clear up any confusion about redistribution rights and how people can use it downstream.
Hope this helps! 😊 Let me know if you have any questions or need more info.
Thanks for your attention!
Looking forward to your response!
We have included Llama-3 license statement in our github repo.
Nonetheless, thanks for your advice and we will add them here thereafter.