Update README.md

README.md

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----
-base_model: unsloth/functiongemma-270m-it
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- gemma3_text
-license: apache-2.0
-language:
-- en
----
-
-# Uploaded finetuned  model
-
-- **Developed by:** emredeveloper
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/functiongemma-270m-it
-
-This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+# Finetuned FunctionGemma Model: `emredeveloper/functiongemma-tools`
+
+This repository hosts a finetuned version of Google's `functiongemma-270m-it` model, specifically adapted for advanced tool-calling capabilities with integrated reasoning. This model has been enhanced using the `LLM360/TxT360-3efforts` dataset, which focuses on providing detailed thinking processes alongside tool calls.
+
+## Model Overview
+
+- **Base Model**: `unsloth/functiongemma-270m-it`
+- **Finetuning Method**: LoRA (Low-Rank Adaptation) using Unsloth library.
+- **Dataset**: `LLM360/TxT360-3efforts` (agent split, 50,000 examples streamed).
+- **Key Enhancement**: Improved tool-calling with explicit `<think>...</think>` blocks for internal reasoning, crucial for complex multi-step tasks.
+
+## Finetuning Details
+
+This model was finetuned in Google Colab using the Unsloth library, which significantly speeds up the finetuning process and reduces VRAM usage.
+
+### Training Configuration:
+
+- **Max Sequence Length**: 4096
+- **LoRA Rank (r)**: 128
+- **LoRA Alpha**: 256
+- **LoRA Dropout**: 0
+- **Gradient Checkpointing**: Enabled with "unsloth" optimization
+- **Batch Size**: 4 (per device), with gradient accumulation steps = 2
+- **Learning Rate**: 2e-4
+- **Optimizer**: `adamw_8bit`
+- **Training Steps**: 100
+
+Special attention was given to applying a custom chat template that incorporates `<think>` tags for explicit reasoning and aligns with the `functiongemma`'s tool-calling format. The training focused on responses only, masking out the instruction part to enhance the model's generation quality.
+
+## How to Use
+
+This model can be loaded and used for inference with the Hugging Face `transformers` library, especially when combined with Unsloth for optimized performance.
+
+### Installation
+
+First, ensure you have the necessary libraries installed:
+
+```bash
+pip install transformers unsloth[cuda] torch
+```
+
+### Loading the Model
+
+```python
+from unsloth import FastLanguageModel
+import torch
+from transformers import TextStreamer
+
+max_seq_length = 4096
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "emredeveloper/functiongemma-tools", # Your finetuned model on Hugging Face Hub
+    max_seq_length = max_seq_length,
+    load_in_4bit = False,
+    load_in_8bit = False,
+    load_in_16bit = True, # Or False if you saved merged_4bit
+)
+```
+
+### Inference Example
+
+To perform inference, you'll need to construct your messages and tools according to the `functiongemma` chat template. Here's an example demonstrating basic tool calling with thinking:
+
+```python
+import json
+
+tools_example = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_amazon_product_details",
+            "description": (
+                "Retrieves comprehensive product information from Amazon, "
+                "including title, price, description, specifications, and availability."
+            ),
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "asin": {
+                        "type": "string",
+                        "description": "The Amazon ASIN of the product.",
+                    }
+                },
+                "required": ["asin"],
+            },
+        },
+    }
+]
+
+messages_example = [
+    {
+        "role": "system",
+        "content": (
+            "You are a shopping assistant. Use tools when you need detailed "
+            "Amazon product data such as price and specifications."
+        ),
+    },
+    {
+        "role": "user",
+        "content": "Is the espresso machine with ASIN B0XYZ12345 any good for home use?"
+    },
+]
+
+# Apply the chat template for generation
+text = tokenizer.apply_chat_template(
+    messages_example,
+    tools = tools_example,
+    tokenize = False,
+    add_generation_prompt = True,
+).removeprefix('<bos>') # Remove <bos> token if present
+
+# Generate a response
+_ = model.generate(
+    **tokenizer(text, return_tensors = "pt").to("cuda"),
+    max_new_tokens = 1024,
+    streamer = TextStreamer(tokenizer, skip_prompt = False),
+    top_p = 0.95, top_k = 64, temperature = 1.0,
+)
+```
+
+This will produce an output similar to:
+
+```
+<start_of_turn>model
+<think>User is asking for an opinion, but I need factual product details first such as price, features, and reviews. I should call the Amazon product details tool with the provided ASIN.</think><start_function_call>call:get_amazon_product_details{asin:<escape>B0XYZ12345<escape>}<end_function_call>
+```
+
+## Applications
+
+This finetuned `functiongemma` model is ideal for:
+
+- **Advanced AI Assistants**: Building intelligent agents that can reason about complex tasks and use external tools effectively.
+- **Tool-Augmented LLMs**: Enhancing LLMs with the ability to dynamically call functions and interpret their results.
+- **Complex Workflow Automation**: Automating multi-step processes that require logical reasoning and interaction with external systems.
+- **Research in Tool Learning**: Studying and developing more sophisticated tool-learning mechanisms for LLMs.
+
+## Feedback and Issues
+
+For any questions, issues, or contributions, please refer to the Unsloth Discord channel or open an issue on the Hugging Face repository.