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, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ValiantLabs/Llama3-70B-Fireplace")
model = AutoModelForMultimodalLM.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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

sequelbox commited on Mar 12, 2025

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@@ -118,6 +118,9 @@ model-index:
 ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64f267a8a4f79a118e0fcc89/qg49GOlx8zogDOrMTnb89.jpeg)
 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

 ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64f267a8a4f79a118e0fcc89/qg49GOlx8zogDOrMTnb89.jpeg)
+**[Click here to support our open-source dataset and model releases!](https://huggingface.co/spaces/sequelbox/SupportOpenSource)**
 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