Create README.md

README.md

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+---
+language:
+- en
+base_model:
+- google/functiongemma-270m-it
+---
+## FunctionGemma-270M-IT RAG 
+
+This is a fine-tuned derivative of `google/functiongemma-270m-it`, optimized for **lightweight Retrieval-Augmented Generation (RAG)** on **mobile / edge / low-power devices**. The fine-tune specializes the model to **consistently emit a tool call to `vector_search`**—with a well-formed, high-recall search query—when the user asks a natural-language question that should be answered from a document store.
+
+It’s intended to be used as the **“retrieval controller”** in a local-first RAG pipeline:  
+**User question → model generates `vector_search(query=…)` → system retrieves passages → (optional) downstream answer model composes final response**.
+
+### Base model
+
+- **Base:** `google/functiongemma-270m-it` (Gemma 3 270M family), a small model tuned specifically for function calling. ([Google AI for Developers](https://ai.google.dev/gemma/docs/functiongemma "FunctionGemma model overview  |  Google AI for Developers"))
+    
+- **Interface & formatting:** Uses FunctionGemma’s special control tokens for tool use (e.g., `<start_function_call>…<end_function_call>`) and the `<escape>` delimiter for string fields. ([Google AI for Developers](https://ai.google.dev/gemma/docs/functiongemma/formatting-and-best-practices "FunctionGemma formatting and best practices  |  Google AI for Developers"))
+    
+- **Context length (base):** 32K total input context (and up to 32K output context per request, budget permitting). ([Hugging Face](https://huggingface.co/google/functiongemma-270m-it "google/functiongemma-270m-it · Hugging Face"))
+    
+
+### What’s new in this fine-tune
+
+**Primary behavioral change:** When asked questions in natural language, the model reliably chooses to call:
+
+- `vector_search`
+    
+- with a **single string argument**: a retrieval query designed to maximize recall and relevance for downstream passage ranking.
+    
+
+**Example behavior (from your eval set):**
+
+- **Prompt:** “Can you compare the political systems of the Roman Republic and the Aztec Empire… succession and social mobility?”  
+    **Output:** `<start_function_call>call:vector_search{query:<escape>Roman Republic vs Aztec Empire political systems succession social mobility ...<escape>}<end_function_call>` ✅
+    
+
+(Additional examples include VAR vs VAR review, journalism ethics across platforms, intrinsic vs extrinsic motivation, bench vs jury trial, Rodin image sources.)
+
+### Intended use
+
+**Designed for:**
+
+- On-device or constrained deployments (mobile apps, embedded, low-cost CPU boxes) that need **fast, local routing to retrieval**. FunctionGemma is explicitly positioned as a lightweight base for local-first agents and edge workflows. ([Google AI for Developers](https://ai.google.dev/gemma/docs/functiongemma "FunctionGemma model overview  |  Google AI for Developers"))
+    
+- RAG systems where **the most important skill is producing the right search query**, not writing the final answer.
+    
+
+**Not designed for:**
+
+- Being the sole “answer model” for complex, high-stakes, or deeply reasoned tasks (it’s small; use it to retrieve, then answer with a stronger model if needed).
+    
+- Multi-step tool plans out of the box (FunctionGemma’s training is strongest for single-turn / parallel calls; multi-step chaining isn’t its primary trained workflow). ([Google AI for Developers](https://ai.google.dev/gemma/docs/functiongemma/formatting-and-best-practices "FunctionGemma formatting and best practices  |  Google AI for Developers"))
+    
+
+### Tool contract
+
+This fine-tune assumes a tool with the following conceptual signature:
+
+- **Tool name:** `vector_search`
+    
+- **Arguments:**
+    
+    - `query` (string): a search query describing the user’s information need
+        
+- **Returns:** passages/snippets (top-k) with metadata (titles/urls/ids), which are then fed into a downstream step.
+    
+
+**Important formatting note:** String values in tool blocks must be wrapped in `<escape>…<escape>` to avoid parsing ambiguity. ([Google AI for Developers](https://ai.google.dev/gemma/docs/functiongemma/formatting-and-best-practices "FunctionGemma formatting and best practices  |  Google AI for Developers"))
+
+### How to use (recommended pattern)
+
+1. **Run the model** on the user question.
+    
+2. If the output contains a `vector_search` call, execute retrieval.
+    
+3. Feed retrieved passages to:
+    
+    - either the same model (if you accept lower-quality synthesis), or
+        
+    - a larger model for final answer generation.
+        
+
+If you are using the Hugging Face tooling, FunctionGemma models are typically used via chat templates that support tool definitions and function-call decoding. ([Hugging Face](https://huggingface.co/google/functiongemma-270m-it "google/functiongemma-270m-it · Hugging Face"))