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README.md

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+---
+license: apache-2.0
+base_model: google/functiongemma-270m-it
+tags:
+  - function-calling
+  - tool-use
+  - dispatcher
+  - delia
+  - gemma
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# FunctionGemma 270M - Delia Dispatcher
+
+A fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it) for **Delia LLM orchestration**.
+
+This tiny model (270M params) acts as a fast dispatcher, routing user requests to the appropriate backend:
+- `call_coder` - Code generation tasks
+- `call_reviewer` - Code review and analysis
+- `call_planner` - Architecture and planning (also handles ambiguous requests)
+- `call_executor` - Running commands and scripts
+
+## Key Features
+
+- **Minimalist schema**: Single `reasoning` parameter per tool
+- **Thought tokens**: Brief CoT scratchpad before tool calls
+- **EOS hardening**: Explicit stop tokens prevent infinite loops
+- **Negative samples**: 13% ambiguous examples → planner (graceful handling)
+- **GBNF grammar**: Constrained decoding for 100% valid output
+
+## Usage
+
+### With llama.cpp (recommended for speed)
+
+```bash
+# Download the GGUF
+wget https://huggingface.co/devopsforflops/functiongemma-270m-delia-dispatcher/resolve/main/functiongemma-270m-delia-dispatcher-f16.gguf
+
+# Download the grammar
+wget https://huggingface.co/devopsforflops/functiongemma-270m-delia-dispatcher/resolve/main/dispatcher.gbnf
+
+# Run with grammar constraint
+./llama-cli -m functiongemma-270m-delia-dispatcher-f16.gguf \
+  --grammar-file dispatcher.gbnf \
+  -p "<start_of_turn>user
+Write a fibonacci function<end_of_turn>
+<start_of_turn>model"
+```
+
+### With Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("devopsforflops/functiongemma-270m-delia-dispatcher")
+tokenizer = AutoTokenizer.from_pretrained("devopsforflops/functiongemma-270m-delia-dispatcher")
+
+prompt = """<start_of_turn>user
+Review this code for bugs<end_of_turn>
+<start_of_turn>model"""
+
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Output Format
+
+```
+<start_of_turn>user
+{request}<end_of_turn>
+<start_of_turn>model
+thought
+{brief reasoning}
+<tool_call>{"name": "call_X", "arguments": {"reasoning": "..."}}</tool_call><end_of_turn>
+```
+
+## Training
+
+Fine-tuned with [Unsloth](https://github.com/unslothai/unsloth) using LoRA:
+- **Epochs**: 3
+- **LoRA rank**: 32
+- **Training examples**: 92 (balanced across 4 tools + 13% ambiguous)
+- **Final loss**: 0.46
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `functiongemma-270m-delia-dispatcher-f16.gguf` | GGUF model (F16, 518MB) |
+| `model.safetensors` | Transformers model |
+| `dispatcher.gbnf` | GBNF grammar for constrained decoding |
+| `dispatcher_tools.json` | Tool schema (4 tools) |
+| `train.jsonl` | Training data |
+
+## License
+
+Apache 2.0 (same as base model)
+
+## Part of Delia
+
+This model is designed for use with [Delia](https://github.com/zbrdc/delia), an LLM orchestration system that routes requests to optimal backends.