Instructions to use Trelis/Meta-Llama-3-8B-Instruct-function-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Trelis/Meta-Llama-3-8B-Instruct-function-calling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Trelis/Meta-Llama-3-8B-Instruct-function-calling") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Trelis/Meta-Llama-3-8B-Instruct-function-calling") model = AutoModelForCausalLM.from_pretrained("Trelis/Meta-Llama-3-8B-Instruct-function-calling") 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 Trelis/Meta-Llama-3-8B-Instruct-function-calling with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Trelis/Meta-Llama-3-8B-Instruct-function-calling" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Trelis/Meta-Llama-3-8B-Instruct-function-calling", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Trelis/Meta-Llama-3-8B-Instruct-function-calling
- SGLang
How to use Trelis/Meta-Llama-3-8B-Instruct-function-calling 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 "Trelis/Meta-Llama-3-8B-Instruct-function-calling" \ --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": "Trelis/Meta-Llama-3-8B-Instruct-function-calling", "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 "Trelis/Meta-Llama-3-8B-Instruct-function-calling" \ --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": "Trelis/Meta-Llama-3-8B-Instruct-function-calling", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use Trelis/Meta-Llama-3-8B-Instruct-function-calling with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Trelis/Meta-Llama-3-8B-Instruct-function-calling to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Trelis/Meta-Llama-3-8B-Instruct-function-calling to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Trelis/Meta-Llama-3-8B-Instruct-function-calling to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Trelis/Meta-Llama-3-8B-Instruct-function-calling", max_seq_length=2048, ) - Docker Model Runner
How to use Trelis/Meta-Llama-3-8B-Instruct-function-calling with Docker Model Runner:
docker model run hf.co/Trelis/Meta-Llama-3-8B-Instruct-function-calling
not working well when function calling
Thanks for testing this out.
Curl Commands
This is tricky to do because you need to have the prompt correctly formatted. One option is to copy-paste the sample formatted prompt on the model card and use that.OpenAI Style requests
The problem you're having there is that the end token for Llama 3 is not the same as the eos_token in the tokenizer_config.json file. I've swapped the eos_token now, so if you re-run with an openai style endpoint (whether tgi or vllm) it should produce clean results.
Note still the following caveats:
- The model correctly calls functions, but does not do a good job making use of function responses.
- Unlike openchat 3.5 (the function calling fine-tune), the model is weak at chaining function calls.
Thanks a lot, now it works!


