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--- |
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license: apache-2.0 |
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tags: |
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- function-calling |
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--- |
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# Fireworks Function Calling (FireFunction) Model V1 |
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FireFunction is a state-of-the-art function calling model with a commercially viable license. |
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💡 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). |
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```sh |
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OPENAI_API_BASE=https://api.fireworks.ai/inference/v1 |
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OPENAI_API_KEY=<YOUR_FIREWORKS_API_KEY> |
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MODEL=accounts/fireworks/models/firefunction-v1 |
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``` |
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## Intended Use and Limitations |
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### Primary Use |
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Although the model was trained on a variety of tasks, it performs best on: |
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* single-turn request routing to a function picked from a pool of up to 20 function specs. |
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* structured information extraction. |
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### Out-of-Scope Use |
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The model was not optimized for the following use cases: |
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* general multi-turn chat, |
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* parallel and nested function calls in a single response. These can be broken into multiple messages. |
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## How to use the model |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import json |
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device = "cuda" # the device to load the model onto |
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model = AutoModelForCausalLM.from_pretrained("fireworks-ai/firefunction-v1") |
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tokenizer = AutoTokenizer.from_pretrained("fireworks-ai/firefunction-v1") |
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function_spec = [ |
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{ |
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"name": "get_stock_price", |
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"description": "Get the current stock price", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"symbol": { |
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"type": "string", |
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"description": "The stock symbol, e.g. AAPL, GOOG" |
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} |
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}, |
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"required": [ |
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"symbol" |
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] |
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} |
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}, |
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{ |
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"name": "check_word_anagram", |
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"description": "Check if two words are anagrams of each other", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"word1": { |
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"type": "string", |
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"description": "The first word" |
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}, |
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"word2": { |
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"type": "string", |
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"description": "The second word" |
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} |
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}, |
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"required": [ |
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"word1", |
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"word2" |
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] |
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} |
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} |
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] |
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functions = json.dumps(functions, indent=4) |
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messages = [ |
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{'role': 'functions', 'content': functions}, |
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{'role': 'system', 'content': 'You are a helpful assistant with access to functions. Use them if required.'}, |
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{'role': 'user', 'content': 'Hi, can you tell me the current stock price of AAPL?'} |
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] |
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encoded = tokenizer.apply_chat_template(messages, return_tensors="pt") |
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model_inputs = encodeds.to(device) |
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model.to(device) |
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generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) |
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decoded = tokenizer.batch_decode(generated_ids) |
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print(decoded[0]) |
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``` |