---
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.
💡 The model is hosted on the [Fireworks](https://fireworks.ai/models/fireworks/firefunction-v1) platform, offering blazing fast inference and 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
```
## 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.
### 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.
## How to use the model
```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=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```