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 Settings

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

85ff8c7

verified ·

1 Parent(s): 2057304

Update README.md

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README.md +7 -3

README.md CHANGED Viewed

@@ -1,11 +1,14 @@
 ---
 license: llama3
 ---
-This model is intended for use in environments where automated function calling capabilities are required to enhance data manipulation and retrieval tasks. It is particularly useful in scenarios involving complex data analysis, where users can query data interactively through natural language commands.
-The model has a specific format for tool calling that is :
 <|begin_of_text|>
 <|start_header_id|>system<|end_header_id|>
         Only use these tools while answering
@@ -34,9 +37,10 @@ function_call: {"name": "python_repl_tool", "arguments": {"code": "df.loc[('2021
 function_response: {"answer": "80000"}
 <|eot_id|>
 <|start_header_id|>assistant<|end_header_id|>
-Ai will respond with function_call if it needs to call a function
 User will respond with either error or the response if it was a tool like : function_response {json response}

 ---
 license: llama3
 ---
+### Function Calling Llama by ScaleGenAI
+## This model is intended for use in environments where automated function calling capabilities are required to enhance data manipulation and retrieval tasks. It is particularly useful in scenarios involving complex data analysis, where users can query data interactively through natural language commands.
+### The model has a specific format for tool calling that is :
+```
 <|begin_of_text|>
 <|start_header_id|>system<|end_header_id|>
         Only use these tools while answering
 function_response: {"answer": "80000"}
 <|eot_id|>
 <|start_header_id|>assistant<|end_header_id|>
+```
+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}