metadata
language:
- en
- fr
license: apache-2.0
base_model: unsloth/functiongemma-270m-it
library_name: gguf
pipeline_tag: text-generation
tags:
- gguf
- llama.cpp
- function-calling
- tool-calling
- gemma
- bilingual
- en
- fr
- finetuned
model_name: function-gemma-finetuned-tool-call
function-gemma-finetuned-tool-call
Fine-tuned Function-Gemma 270M model for bilingual (English/French) tool-calling.
Files
function-gemma-finetuned-tool-call.gguf(F16 merged GGUF)
Base Model
unsloth/functiongemma-270m-it
Training Summary
- Method: SFT + LoRA, then merged into full weights
- Dataset: custom bilingual EN/FR tool-calling set (
dataset_80tools_en_fr.json) - Target behavior: structured function/tool calls with argument extraction and no-tool abstention when appropriate
Local Evaluation (checkpoint benchmark)
From outputs/eval_checkpoint_report.json:
- Total cases: 16
- Pass rate: 0.8125
- Decision accuracy: 0.8125
- Tool name accuracy: 0.8125
- Argument presence accuracy: 1.0
- Tool-call recall: 1.0
- No-tool precision: 0.5
Usage (llama.cpp)
llama.cpp/build/bin/llama-cli \
--model function-gemma-finetuned-tool-call.gguf \
--ctx-size 32768 \
--n-gpu-layers 99 \
--seed 3407 \
--top-k 64 \
--top-p 0.95 \
--temp 1.0 \
--jinja
For one-shot test:
llama.cpp/build/bin/llama-cli \
--model function-gemma-finetuned-tool-call.gguf \
--ctx-size 32768 \
--n-gpu-layers 99 \
--seed 3407 \
--top-k 64 \
--top-p 0.95 \
--temp 1.0 \
--jinja \
--single-turn \
--simple-io \
--prompt "What is the weather in Paris?"
Prompt / Output Format
This model was fine-tuned for Function-Gemma style tool tags (e.g. <start_function_call>...).
When used with --jinja, llama.cpp applies the chat template stored in GGUF metadata.
Limitations
- Small model (270M): can still over-call tools in ambiguous no-tool prompts.
- Best results require strong tool schema prompts and clear user intent.
Intended Use
- Lightweight local assistant prototypes
- Tool-routing and structured argument extraction tasks
- EN/FR bilingual demos and experimentation