Instructions to use ScaleGenAI/Llama3-70B-Function-Calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ScaleGenAI/Llama3-70B-Function-Calling with Transformers:

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

pipe = pipeline("text-generation", model="ScaleGenAI/Llama3-70B-Function-Calling")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ScaleGenAI/Llama3-70B-Function-Calling")
model = AutoModelForCausalLM.from_pretrained("ScaleGenAI/Llama3-70B-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/Llama3-70B-Function-Calling with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ScaleGenAI/Llama3-70B-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/Llama3-70B-Function-Calling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ScaleGenAI/Llama3-70B-Function-Calling

SGLang

How to use ScaleGenAI/Llama3-70B-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/Llama3-70B-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/Llama3-70B-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/Llama3-70B-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/Llama3-70B-Function-Calling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ScaleGenAI/Llama3-70B-Function-Calling with Docker Model Runner:
```
docker model run hf.co/ScaleGenAI/Llama3-70B-Function-Calling
```

Usage From Our SDK

pip install scalegen-function-calling

from  scalegen_function_calling import CustomOpenAIClient
from openai import OpenAI

tools = [
   {
      "type":"function",
      "function":{
         "name":"Expense",
         "description":"",
         "parameters":{
            "type":"object",
            "properties":{
               "description":{
                  "type":"string"
               },
               "net_amount":{
                  "type":"number"
               },
               "gross_amount":{
                  "type":"number"
               },
               "tax_rate":{
                  "type":"number"
               },
               "date":{
                  "type":"string",
                  "format":"date-time"
               }
            },
            "required":[
               "description",
               "net_amount",
               "gross_amount",
               "tax_rate",
               "date"
            ]
         }
      }
   },
   {
      "type":"function",
      "function":{
         "name":"ReportTool",
         "description":"",
         "parameters":{
            "type":"object",
            "properties":{
               "report":{
                  "type":"string"
               }
            },
            "required":[
               "report"
            ]
         }
      }
   }
]

model_name = "ScaleGenAI/Llama3-70B-Function-Calling"
api_key = "<YOUR_API_KEY>"
api_endpint = "<YOUR_API_ENDPOINT>"

messages = [
    {"role":"user", "content": 'I have spend 5$ on a coffee today please track my expense. The tax rate is 0.2. plz add to expense'}
]

client = OpenAI(
    api_key=api_key,
    base_url=api_endpoint,
)

custom_client = CustomOpenAIClient(client) #patch the client

response = custom_client.chat.completions.create(
            model=model_name,
            messages=messages,
            tools=tools,
            stream=False
        )

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Safetensors

Model size

71B params

Tensor type

BF16