---
license: apache-2.0
tags:
- function-calling
---
# Fireworks Function Calling (FireFunction) Model V1
FireFunction is a state-of-the-art function calling model with a commercially viable license. Key info and highlights:
💡 The model is also hosted on the [Fireworks](https://fireworks.ai/models/fireworks/firefunction-v1) platform. Offered for free during a limited beta period
⭐️ Near GPT-4 level quality for real-world use cases of structured information generation and routing decision-making
💨 Blazing fast speed. Inference speeds are roughly 4x that of GPT-4 when using FireFunction hosted on the Fireworks platform
🔄 Support for "any" parameter in tool_choice. Firefunction is the only model that we're aware that supports an option for the model to always choose a function - particularly helpful for routing use cases
✅ The model is also API compatible with [OpenAI function calling](https://platform.openai.com/docs/guides/function-calling).
```sh
OPENAI_API_BASE=https://api.fireworks.ai/inference/v1
OPENAI_API_KEY=
MODEL=accounts/fireworks/models/firefunction-v1
```
## Resources
* [FireFunction-v1 Blog Post](https://fireworks.ai/blog/firefunction-v1-gpt-4-level-function-calling)
* [Fireworks discord with function calling channel](https://discord.gg/mMqQxvFD9A)
* [Documentation](https://readme.fireworks.ai/docs/function-calling)
* [UI Demo app](https://functional-chat.vercel.app/)
* [Try in Fireworks prompt playground UI](https://fireworks.ai/models/fireworks/firefunction-v1)
# Intended Use and Limitations
### Primary Use
Although the model was trained on a variety of tasks, it performs best on:
* single-turn request routing to a function picked from a pool of up to 20 function specs.
* structured information extraction.
See [blog post](https://fireworks.ai/blog) for more info on FireFunction.
### Out-of-Scope Use
The model was not optimized for the following use cases:
* general multi-turn chat,
* parallel and nested function calls in a single response. These can be broken into multiple messages.
## Example Usage
See [documentation](https://readme.fireworks.ai/docs/function-calling) for more detail.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import json
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("fireworks-ai/firefunction-v1", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("fireworks-ai/firefunction-v1")
function_spec = [
{
"name": "get_stock_price",
"description": "Get the current stock price",
"parameters": {
"type": "object",
"properties": {
"symbol": {
"type": "string",
"description": "The stock symbol, e.g. AAPL, GOOG"
}
},
"required": [
"symbol"
]
}
},
{
"name": "check_word_anagram",
"description": "Check if two words are anagrams of each other",
"parameters": {
"type": "object",
"properties": {
"word1": {
"type": "string",
"description": "The first word"
},
"word2": {
"type": "string",
"description": "The second word"
}
},
"required": [
"word1",
"word2"
]
}
}
]
functions = json.dumps(function_spec, indent=4)
messages = [
{'role': 'functions', 'content': functions},
{'role': 'system', 'content': 'You are a helpful assistant with access to functions. Use them if required.'},
{'role': 'user', 'content': 'Hi, can you tell me the current stock price of AAPL?'}
]
model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
generated_ids = model.generate(model_inputs, max_new_tokens=128)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Demo App
Check our easy-to-extend [demo chat app](https://github.com/fw-ai/forge/tree/main/apps/functional_chat) with function calling capabilities built on Firefunction model.
![demo app](function_calling_demo.gif)