Instructions to use ScaleGenAI/Llama3-8B-Function-Calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ScaleGenAI/Llama3-8B-Function-Calling with Transformers:

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

pipe = pipeline("text-generation", model="ScaleGenAI/Llama3-8B-Function-Calling")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ScaleGenAI/Llama3-8B-Function-Calling")
model = AutoModelForCausalLM.from_pretrained("ScaleGenAI/Llama3-8B-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ScaleGenAI/Llama3-8B-Function-Calling with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ScaleGenAI/Llama3-8B-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": "ScaleGenAI/Llama3-8B-Function-Calling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ScaleGenAI/Llama3-8B-Function-Calling

SGLang

How to use ScaleGenAI/Llama3-8B-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 "ScaleGenAI/Llama3-8B-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": "ScaleGenAI/Llama3-8B-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 "ScaleGenAI/Llama3-8B-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": "ScaleGenAI/Llama3-8B-Function-Calling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ScaleGenAI/Llama3-8B-Function-Calling with Docker Model Runner:
```
docker model run hf.co/ScaleGenAI/Llama3-8B-Function-Calling
```

tejeshbhalla commited on Apr 26, 2024

Commit

7e292f1

verified ·

1 Parent(s): 85ff8c7

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -40,7 +40,7 @@ function_response: {"answer": "80000"}
 ```
-Ai will respond with function_call if it needs to call a function lile : function_call {tool args in json}
-User will respond with either error or the response if it was a tool like : function_response {json response}

 ```
+#### Ai will respond with function_call if it needs to call a function lile : function_call {tool args in json}
+#### User will respond with either error or the response if it was a tool like : function_response {json response}