Instructions to use fireworks-ai/firefunction-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fireworks-ai/firefunction-v1 with Transformers:

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

pipe = pipeline("text-generation", model="fireworks-ai/firefunction-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fireworks-ai/firefunction-v1")
model = AutoModelForCausalLM.from_pretrained("fireworks-ai/firefunction-v1")
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 fireworks-ai/firefunction-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/fireworks-ai/firefunction-v1

SGLang

How to use fireworks-ai/firefunction-v1 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 "fireworks-ai/firefunction-v1" \
    --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": "fireworks-ai/firefunction-v1",
		"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 "fireworks-ai/firefunction-v1" \
        --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": "fireworks-ai/firefunction-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fireworks-ai/firefunction-v1 with Docker Model Runner:
```
docker model run hf.co/fireworks-ai/firefunction-v1
```

raymond513 commited on Feb 20, 2024

Commit

ea65e15

verified ·

1 Parent(s): 2076494

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -1

README.md CHANGED Viewed

@@ -17,6 +17,14 @@ OPENAI_API_KEY=<YOUR_FIREWORKS_API_KEY>
 MODEL=accounts/fireworks/models/firefunction-v1
 ```
 ## Intended Use and Limitations
 ### Key Highlights
@@ -31,7 +39,6 @@ Although the model was trained on a variety of tasks, it performs best on:
  * structured information extraction.
 ### Out-of-Scope Use
 The model was not optimized for the following use cases:
   * general multi-turn chat,

 MODEL=accounts/fireworks/models/firefunction-v1
 ```
+## Resources
+* [FireFunction-v1 Blog Post] (https://fireworks.ai/blog)
+* [Fireworks discord with function calling channel] (https://discord.gg/mMqQxvFD9A)
+* [Documentation] (https://readme.fireworks.ai/docs/function-calling)
+* [Demo app] (https://functional-chat.vercel.app/)
+* [Try in Fireworks prompt playground UI] (https://fireworks.ai/models/fireworks/firefunction-v1)
 ## Intended Use and Limitations
 ### Key Highlights
  * structured information extraction.
 ### Out-of-Scope Use
 The model was not optimized for the following use cases:
   * general multi-turn chat,