An ultra-small (270M) agentic coder. The Q4_K_M GGUF is tiny (~200 MB) and runs comfortably CPU-only (laptops, small VPS), while speaking the deterministic Hermes/ChatML <tool_call> format used by the Tiny-Giant harness.

Files

File	Description
`SteraFunctionGemma-270M-Q4_K_M.gguf`	Q4_K_M quant (~200 MB) — `llama.cpp` / Ollama / LM Studio, CPU-friendly
`SteraFunctionGemma-270M-f16.gguf`	f16 GGUF — re-quantize to any level without retraining
`raw_weights/`	Full bf16 safetensors HF checkpoint
`val_meta.jsonl`	Held-out validation set shipped with the model

Training

Base: google/functiongemma-270m-it (Gemma 3, 270M, gated/Apache-style Gemma license)
Method: full fine-tune (not LoRA), bf16 + gradient checkpointing
Data: ~30k Tiny-Giant agentic tool-use / debugging conversations
Epochs: 2 · LR: 1e-5 (cosine, 3% warmup) · Seq len: 4096

Prompt format

Trained with an explicit ChatML / Hermes renderer (not Gemma's native <start_of_turn> template). Pin ChatML when serving (--chat-template chatml). Tool calls:

<tool_call>
{"name": "<function-name>", "arguments": {...}}
</tool_call>

Inference (llama.cpp, CPU-friendly)

llama-cli -m SteraFunctionGemma-270M-Q4_K_M.gguf --chat-template chatml

License

Inherits the Gemma license from the google/functiongemma-270m-it base model.

strykes/SteraFunctionGemma-270M

SteraFunctionGemma-270M

Files

Training

Prompt format

Inference (llama.cpp, CPU-friendly)

License

strykes/
SteraFunctionGemma-270M