kingabzpro
/

functiongemma-hermes-3k-ft

@@ -3,199 +3,163 @@ library_name: transformers
 tags:
 - trl
 - sft
 ---
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 tags:
 - trl
 - sft
+license: apache-2.0
+datasets:
+- interstellarninja/hermes_reasoning_tool_use
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- google/functiongemma-270m-it
+pipeline_tag: text-generation
 ---
+# FunctionGemma Hermes Tool-Use (3K Fine-tuned)
+This model is a **fine-tuned version of Google’s FunctionGemma (270M)**, trained on a curated subset of the **Hermes Tool-Use** dataset to improve **structured function calling**.
+The goal of this fine-tuning is **higher accuracy and reliability** when selecting the correct tool and emitting a valid function call in the expected format.
+## 🚀 What’s Improved
+Evaluation was run on a held-out validation set (50 examples):
+| Metric | Before FT | After FT |
+|------|-----------|----------|
+| Tool Selection Accuracy | **92.0%** | **98.0%** |
+| Absolute Gain | – | **+6.0%** |
+This shows the model learns **better tool selection and call consistency**, even though the base model already performs strongly.
+## 🧠 Supported Output Format
+The model emits function calls in **FunctionGemma-style** tags:
+```text
+<start_function_call>
+call:tool_name{args:<escape>{...}<escape>}
+<end_function_call>
+````
+This is compatible with downstream tool execution pipelines.
+## 📦 Installation
+```bash
+pip install transformers accelerate sentencepiece
+```
+## 🔧 Usage Example (Function Calling)
+```python
+from transformers import AutoProcessor, AutoModelForCausalLM
+repo_id = "kingabzpro/functiongemma-hermes-3k-ft"
+processor = AutoProcessor.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(repo_id)
+# Tool definition (HF function schema)
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "billboard_global_200",
+            "description": "Fetch Billboard Global 200 chart information for a specific date.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "date": {
+                        "type": "string",
+                        "description": "Date in YYYY-MM-DD format",
+                        "default": "2020-09-19",
+                    }
+                },
+                "required": ["date"],
+            },
+        },
+    }
+]
+messages = [
+    {
+        "role": "developer",
+        "content": (
+            "You are a function calling AI model. "
+            "Each function call must be enclosed in <tool_call> XML tags."
+        ),
+    },
+    {
+        "role": "user",
+        "content": (
+            "Which songs were at positions 1, 11, 21, 31, and 41 "
+            "on the Billboard Global 200 chart, and who sang them?"
+        ),
+    },
+]
+inputs = processor.apply_chat_template(
+    messages,
+    tools=tools,
+    add_generation_prompt=True,
+    return_dict=True,
+    return_tensors="pt",
+)
+inputs = {k: v.to(model.device) for k, v in inputs.items()}
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    pad_token_id=processor.eos_token_id,
+)
+gen = processor.decode(
+    outputs[0][inputs["input_ids"].shape[-1]:],
+    skip_special_tokens=True,
+)
+print(gen)
+```
+### ✅ Example Output
+```text
+<start_function_call>
+call:billboard_global_200{args:<escape>{"date": "2006-03-20"}<escape>}
+<end_function_call>
+```
+## 🎯 Intended Use
+* Tool / function calling research
+* Agent systems and planners
+* Structured API invocation
+* Evaluation of tool-selection accuracy
+* Lightweight function-calling demos (CPU / small GPU friendly)
+## ⚠️ Limitations
+* Trained on a **subset (3K)** of Hermes Tool-Use data
+* Focused on **tool selection**, not long-form reasoning
+* Not instruction-tuned for general chat beyond tool use
+## 📜 Attribution
+* Base model: **Google FunctionGemma**
+* Dataset: **Hermes Tool-Use**
+* Fine-tuning & evaluation: **kingabzpro**