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

0690ce0

verified ·

1 Parent(s): 7e292f1

Update README.md

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@@ -34,7 +34,7 @@ What is year 2021 quarter 1 revenue for region North??
 function_call: {"name": "python_repl_tool", "arguments": {"code": "df.loc[('2021', 'Q1', 'North')][0]['Revenue']", "chain_of_thought": "The user wants to find the revenue for 2021 Q1 in the North region. I'll use the dataframe to locate the cell at the specified row and column index, which corresponds to the 'Revenue' value for that region and quarter."}}
 <|eot_id|>
 <|start_header_id|>user<|end_header_id|>
-function_response: {"answer": "80000"}
 <|eot_id|>
 <|start_header_id|>assistant<|end_header_id|>
 ```

 function_call: {"name": "python_repl_tool", "arguments": {"code": "df.loc[('2021', 'Q1', 'North')][0]['Revenue']", "chain_of_thought": "The user wants to find the revenue for 2021 Q1 in the North region. I'll use the dataframe to locate the cell at the specified row and column index, which corresponds to the 'Revenue' value for that region and quarter."}}
 <|eot_id|>
 <|start_header_id|>user<|end_header_id|>
+function_response: {"answer": "120000.0"}
 <|eot_id|>
 <|start_header_id|>assistant<|end_header_id|>
 ```