Instructions to use meta-llama/Meta-Llama-3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meta-llama/Meta-Llama-3-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="meta-llama/Meta-Llama-3-8B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B") model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use meta-llama/Meta-Llama-3-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "meta-llama/Meta-Llama-3-8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meta-llama/Meta-Llama-3-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/meta-llama/Meta-Llama-3-8B
- SGLang
How to use meta-llama/Meta-Llama-3-8B 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 "meta-llama/Meta-Llama-3-8B" \ --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": "meta-llama/Meta-Llama-3-8B", "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 "meta-llama/Meta-Llama-3-8B" \ --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": "meta-llama/Meta-Llama-3-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use meta-llama/Meta-Llama-3-8B with Docker Model Runner:
docker model run hf.co/meta-llama/Meta-Llama-3-8B
Update post-processor to add bos
@pcuenq It looks like this might be missing the end of turn token:
Edit: I'm dumb, this comment should be for the instruct model https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/discussions/42/files Sorry!
https://github.com/meta-llama/llama3/blob/main/llama/generation.py#L307
->
https://github.com/meta-llama/llama3/blob/main/llama/tokenizer.py#L222
def encode_message(self, message: Message) -> List[int]:
tokens = self.encode_header(message)
tokens.extend(
self.tokenizer.encode(message["content"].strip(), bos=False, eos=False)
)
tokens.append(self.tokenizer.special_tokens["<|eot_id|>"])
return tokens
It looks like at the end of each message, the eot should be appended if I'm reading this right.
I tested this change, and it fixes fine-tuning of the base model. Without it the grad norm is inf and the loss is high.
I also tried just using add_bos_token: true and that did not actually add the token, at least with Axolotl.
Thanks for the confirmations, merging now!

