FireFunction V2: Fireworks Function Calling Model
Try on Fireworks | API Docs | Demo App | Discord
FireFunction is a state-of-the-art function calling model with a commercially viable license. View detailed info in our announcement blog. Key info and highlights:
Comparison with other models:
- Competitive with GPT-4o at function-calling, scoring 0.81 vs 0.80 on a medley of public evaluations
- Trained on Llama 3 and retains Llama 3โs conversation and instruction-following capabilities, scoring 0.84 vs Llama 3โs 0.89 on MT bench
- Significant quality improvements over FireFunction v1 across the broad range of metrics
General info:
๐พ Successor of the FireFunction model
๐ Support of parallel function calling (unlike FireFunction v1) and good instruction following
๐ก Hosted on the Fireworks platform at < 10% of the cost of GPT 4o and 2x the speed
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
The model has an 8k context window, like Llama 3
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])
Resources
- Downloads last month
- 185
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.