fireworks-ai
/

llama-3-firefunction-v2

@@ -1,199 +1,112 @@
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 ---
+license: apache-2.0
+tags:
+- function-calling
 ---
+# Fireworks Function Calling (FireFunction) Model V2
+<img src="https://cdn-uploads.huggingface.co/production/uploads/64b6f3a72f5a966b9722de88/12mfdeAJzW1NdKrN_J--L.png" alt="firefunction" width="400"/>
+FireFunction is a state-of-the-art function calling model with a commercially viable license. Key info and highlights:
+🐾 Successor of the [FireFunction](https://fireworks.ai/models/fireworks/firefunction-v2) model
+📏 Signifficant quality improvements over FireFunction v1 across the broad range of metrics
+🔆 Support of parallel function calling (unlike FireFunction v1) and good instruction following
+💡 Hosted on the [Fireworks](https://fireworks.ai/models/fireworks/firefunction-v2) platform
+## Resources
+* [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-v2)
+# 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
+## 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-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': '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 google and netflix?'}
+]
+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.