metadata
license: apache-2.0
tags:
- function-calling
Fireworks Function Calling (FireFunction) Model V2
![firefunction](https://cdn-uploads.huggingface.co/production/uploads/64b6f3a72f5a966b9722de88/nJNtxLzWswBDKK1iOZblb.png)
FireFunction is a state-of-the-art function calling model with a commercially viable license. Key info and highlights:
๐พ Successor of the FireFunction model
๐ Signifficant quality improvements over FireFunction v1 across the broad range of metrics
๐ Support of parallel function calling (unlike FireFunction v1) and good instruction following
๐ก Hosted on the Fireworks platform
Intended Use and Limitations
Supported usecases
The model was tuned to perfom well on a range of usecases including:
- general instruction following
- multi-turn chat mixing vanilla messages with function calls
- single- and parallel function calling
- up to 20 function specs supported at once
- structured information extraction
Out-of-Scope Use
The model was not optimized for the following use cases:
- 100+ function specs
- nested function calling
Metrics
Benchmark | Firefunction v1 | Firefunction v2 | Llama 3 70b Instruct | Gpt-4o |
---|---|---|---|---|
Gorilla simple | 0.91 | 0.94 | 0.925 | 0.88 |
Gorilla multiple_function | 0.92 | 0.91 | 0.86 | 0.91 |
Gorilla parallel_function | 0 | 0.9 | 0.86 | 0.89 |
Gorilla parallel_multiple_function | 0 | 0.8 | 0.615 | 0.72 |
Nexus parallel | 0.38 | 0.53 | 0.3 | 0.47 |
Mtbench | 0.73 | 0.84 | 0.89 | 0.93 |
Average | 0.49 | 0.82 | 0.74 | 0.8 |
Example Usage
See documentation for more detail.
from transformers import AutoModelForCausalLM, AutoTokenizer
import json
from datetime import datetime
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("fireworks-ai/firefunction-v2", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("fireworks-ai/firefunction-v2")
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': '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 google and netflix?'}
]
now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
model_inputs = tokenizer.apply_chat_template(messages, functions=functions, datetime=now, 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])