Instructions to use mzbac/llama-3-8B-Instruct-function-calling-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mzbac/llama-3-8B-Instruct-function-calling-v0.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mzbac/llama-3-8B-Instruct-function-calling-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mzbac/llama-3-8B-Instruct-function-calling-v0.2")
model = AutoModelForCausalLM.from_pretrained("mzbac/llama-3-8B-Instruct-function-calling-v0.2")
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 mzbac/llama-3-8B-Instruct-function-calling-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mzbac/llama-3-8B-Instruct-function-calling-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mzbac/llama-3-8B-Instruct-function-calling-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mzbac/llama-3-8B-Instruct-function-calling-v0.2

SGLang

How to use mzbac/llama-3-8B-Instruct-function-calling-v0.2 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 "mzbac/llama-3-8B-Instruct-function-calling-v0.2" \
    --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": "mzbac/llama-3-8B-Instruct-function-calling-v0.2",
		"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 "mzbac/llama-3-8B-Instruct-function-calling-v0.2" \
        --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": "mzbac/llama-3-8B-Instruct-function-calling-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mzbac/llama-3-8B-Instruct-function-calling-v0.2 with Docker Model Runner:
```
docker model run hf.co/mzbac/llama-3-8B-Instruct-function-calling-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model

This model has been fine-tuned based on Meta-Llama/Meta-Llama-3-8B-Instruct using the mlx-lm with a cleaned-up function calling dataset that removed invalid JSON data and single quotes around argument values.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "mzbac/llama-3-8B-Instruct-function-calling-v0.2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    
)

tool = {
            "name": "search_web",
            "description": "Perform a web search for a given search terms.",
            "parameter": {
                "type": "object", 
                "properties": {
                    "search_terms": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "The search queries for which the search is performed.",
                    "required": True,
                    }
                }
            },
        }

messages = [
            {
                "role": "system",
                "content": f"You are a helpful assistant with access to the following functions. Use them if required - {str(tool)}",
            },
            {"role": "user", "content": "Today's news in Melbourne, just for your information, today is April 27, 2014."},
        ]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.1,
)
response = outputs[0]
print(tokenizer.decode(response))

# <|begin_of_text|><|start_header_id|>system<|end_header_id|>

# You are a helpful assistant with access to the following functions. Use them if required - {'name':'search_web', 'description': 'Perform a web search for a given search terms.', 'parameter': {'type': 'object', 'properties': {'search_terms': {'type': 'array', 'items': {'type':'string'}, 'description': 'The search queries for which the search is performed.','required': True}}}}<|eot_id|><|start_header_id|>user<|end_header_id|>

# Today's news in Melbourne, just for your information, today is April 27, 2014.<|eot_id|><|start_header_id|>assistant<|end_header_id|>

# <functioncall> {"name": "search_web", "arguments": {"search_terms": ["Melbourne news", "April 27, 2014"]}}<|eot_id|>

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BF16

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