Create README.md

README.md

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+---
+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=<YOUR_FIREWORKS_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")
+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(functions, 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?'}
+]
+
+encoded = tokenizer.apply_chat_template(messages, return_tensors="pt")
+
+model_inputs = encodeds.to(device)
+model.to(device)
+
+generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
+decoded = tokenizer.batch_decode(generated_ids)
+print(decoded[0])
+```