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README.md
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---
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license: apache-2.0
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base_model: google/functiongemma-270m-it
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tags:
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- gemma
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- function-calling
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- tool-use
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- gguf
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- ollama
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model-index:
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- name: delia-functiongemma-270m-gguf
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results: []
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---
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# Delia FunctionGemma 270M - GGUF
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This is the GGUF version of [delia-functiongemma-270m](https://huggingface.co/devopsforflops/delia-functiongemma-270m), fine-tuned for Delia MCP tool orchestration.
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## Quick Start with Ollama
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```bash
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# Download the GGUF file
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wget https://huggingface.co/devopsforflops/delia-functiongemma-270m-gguf/resolve/main/functiongemma-delia-f16.gguf
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# Create Modelfile
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cat > Modelfile << 'MODELFILE'
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FROM ./functiongemma-delia-f16.gguf
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TEMPLATE """{{ if .System }}<start_of_turn>developer
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{{ .System }}
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<end_of_turn>
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{{ end }}<start_of_turn>user
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{{ .Prompt }}
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<end_of_turn>
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<start_of_turn>model
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"""
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PARAMETER stop <end_of_turn>
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PARAMETER stop <start_of_turn>
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PARAMETER temperature 0.1
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PARAMETER num_ctx 2048
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MODELFILE
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# Import to Ollama
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ollama create functiongemma-delia -f Modelfile
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# Test it
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ollama run functiongemma-delia "Hello!"
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```
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## Model Details
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| Property | Value |
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|----------|-------|
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| Base Model | google/functiongemma-270m-it |
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| Architecture | Gemma3 |
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| Parameters | 268M |
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| Quantization | F16 (full precision) |
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| File Size | ~518 MB |
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| Context Length | 2048 tokens |
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## Training
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Fine-tuned using LoRA on Delia MCP tool calling examples:
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- LoRA rank: 16
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- LoRA alpha: 64
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- Epochs: 20
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- Dataset: 27 training examples from Delia test suite
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## Use with Delia
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Add to your Delia `settings.json`:
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```json
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{
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"model_dispatcher": {
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"name": "functiongemma-delia",
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"num_ctx": 2048
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}
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}
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```
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**Important:** The model name must contain "functiongemma" for Delia to apply the correct prompt formatting.
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## Related Models
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- [delia-functiongemma-270m](https://huggingface.co/devopsforflops/delia-functiongemma-270m) - Full merged HuggingFace model
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- [delia-functiongemma-270m-lora](https://huggingface.co/devopsforflops/delia-functiongemma-270m-lora) - LoRA adapter only
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## License
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Apache 2.0
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