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

Libraries

How to use ValiantLabs/Llama3-70B-Fireplace with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ValiantLabs/Llama3-70B-Fireplace")
model = AutoModelForCausalLM.from_pretrained("ValiantLabs/Llama3-70B-Fireplace")
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 ValiantLabs/Llama3-70B-Fireplace with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ValiantLabs/Llama3-70B-Fireplace"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ValiantLabs/Llama3-70B-Fireplace",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ValiantLabs/Llama3-70B-Fireplace

SGLang

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

Docker Model Runner
How to use ValiantLabs/Llama3-70B-Fireplace with Docker Model Runner:
```
docker model run hf.co/ValiantLabs/Llama3-70B-Fireplace
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Click here to support our open-source dataset and model releases!

Fireplace is a function-calling model for Llama 3 70b Instruct.

combines function-calling abilities with a high-performance, versatile chat model
system message function-calling utilizing the Llama 3 Instruct format

This version of Fireplace focuses on combining chat-instruct and system-message function-calling only.

We've just released Fireplace 2 for Llama 3.1 8b, which includes inline function calls as one of several technical skills (including JSON, SQL, and more!) Try it today!

Version

This is the 2024-05-09 release of Fireplace for Llama 3 70b.

We've also released Fireplace 2 for Llama 3.1 8b and we're working on more Fireplace releases to come :)

Prompting Guide

Fireplace uses the Llama 3 Instruct prompt format:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>{{ user_msg_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>{{ model_answer_1 }}<|eot_id|>

Example input for function calling:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n You are Fireplace, an expert code assistant with access to the following functions. Use them if required - { "name": "calculate_tip", "description": "Calculate the tip amount for a bill", "parameters": { "type": "object", "properties": { "bill_amount": { "type": "number", "description": "The total amount of the bill" }, "tip_percentage": { "type": "number", "description": "The percentage of tip to be given" } }, "required": [ "bill_amount", "tip_percentage" ] } } { "name": "check_website_availability", "description": "Check the availability of a website", "parameters": { "type": "object", "properties": { "url": { "type": "string", "description": "The URL of the website" } }, "required": [ "url" ] } } <|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHi, I need help with calculating a tip. My bill is $100 and I want to leave a 30% tip. <|eot_id|><|start_header_id|>assistant<|end_header_id|>

For assistant handling of function responses, deliver them in a new user message:

<|start_header_id|>user<|end_header_id|>\n\n FUNCTION RESPONSE: {"status": "success", "message": "Email has been sent successfully"} <|eot_id|>

WARNING: text-generation-webui

When using Llama 3 Instruct models (including Fireplace) with text-generation-webui note that a current bug in webui can result in incorrect reading of the model's ending tokens, causing unfinished outputs and incorrect structure.

For a temporary workaround if you encounter this issue, edit Fireplace's tokenizer_config file as indicated:

from "eos_token": "<|end_of_text|>",

to "eos_token": "<|eot_id|>",