gnumanth
/

xkcd-functiongemma

@@ -1,200 +1,122 @@
 ---
 library_name: transformers
 tags:
 - unsloth
 ---
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 ---
+language:
+- en
+license: apache-2.0
 library_name: transformers
 tags:
+- function-calling
+- xkcd
+- gemma
 - unsloth
+- peft
+- lora
+base_model: google/functiongemma-270m-it
+datasets:
+- olivierdehaene/xkcd
+pipeline_tag: text-generation
 ---
+# XKCD FunctionGemma
+A fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it) for XKCD comic search function calling.
+## Model Description
+This model was fine-tuned to generate structured function calls for searching XKCD comics. Given a natural language query about comics, it outputs a properly formatted tool call that can be parsed and executed.
+**Base model:** `google/functiongemma-270m-it`
+**Fine-tuning method:** LoRA via Unsloth (1.4% trainable parameters)
+**Training data:** 2,630 examples from [olivierdehaene/xkcd](https://huggingface.co/datasets/olivierdehaene/xkcd)
+**Training time:** ~8 minutes on T4 GPU
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import json
+import re
+# Load model
+model = AutoModelForCausalLM.from_pretrained(
+    "gnumanth/xkcd-functiongemma",
+    device_map="auto",
+    torch_dtype="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("gnumanth/xkcd-functiongemma")
+# Define tools
+TOOLS = [{
+    "type": "function",
+    "function": {
+        "name": "search_xkcd",
+        "description": "Search XKCD comics by topic",
+        "parameters": {
+            "type": "object",
+            "properties": {"query": {"type": "string"}},
+            "required": ["query"]
+        }
+    }
+}]
+# Generate function call
+messages = [{"role": "user", "content": "Find xkcd about programming"}]
+text = tokenizer.apply_chat_template(messages, tools=TOOLS, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=128, do_sample=False)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+# Output: <start_function_call>call:search_xkcd{"query": "programming"}<end_function_call>
+```
+## Parsing Function Calls
+```python
+def parse_function_call(output: str) -> dict | None:
+    """Extract function name and arguments from model output."""
+    match = re.search(r'call:(\w+)\s*\{(.+)\}', output, re.DOTALL)
+    if not match:
+        return None
+    func_name = match.group(1)
+    args_raw = match.group(2).strip()
+    # Handle double braces from training format
+    args_raw = re.sub(r'^\s*\{', '', args_raw)
+    if args_raw.endswith('}'):
+        args_raw = args_raw[:-1]
+    try:
+        return {"function": func_name, "arguments": json.loads('{' + args_raw + '}')}
+    except json.JSONDecodeError:
+        return None
+# Usage
+call = parse_function_call(response)
+# {'function': 'search_xkcd', 'arguments': {'query': 'programming'}}
+```
 ## Training Details
+- **Epochs:** 1
+- **Batch size:** 2 (with 4 gradient accumulation steps)
+- **Learning rate:** 2e-4
+- **LoRA rank:** 16
+- **Target modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Final loss:** 0.281
+## Limitations
+- Only trained for XKCD search queries
+- May produce double braces in output (handled by parser above)
+- Small model (270M params) - limited reasoning capability
+## License
+Apache 2.0 (same as base model)
+## Links
+- [Training notebook](https://github.com/hemanth/notebooks/blob/main/notebooks/functiongemma_xkcd_finetune.ipynb)
+- [Base model](https://huggingface.co/google/functiongemma-270m-it)
+- [XKCD dataset](https://huggingface.co/datasets/olivierdehaene/xkcd)