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README.md

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+---
+base_model: mlx-community/gemma-4-12B-it-qat-4bit
+license: gemma
+library_name: mlx-vlm
+tags:
+  - lora
+  - qlora
+  - gemma4
+  - agentic
+  - coding
+  - tool-use
+  - mlx
+datasets:
+  - greghavens/fable-5-coding-and-debugging-traces
+  - greghavens/gpt-5.6-sol-coding-and-debugging-traces
+  - greghavens/kimi-k3-coding-and-debugging-traces
+---
+
+# gemma-4-12b-it-qat-4bit-frontierdistill
+
+A rank-8 LoRA adapter for [mlx-community/gemma-4-12B-it-qat-4bit](https://huggingface.co/mlx-community/gemma-4-12B-it-qat-4bit), fine-tuned for agentic coding and tool use. Trained on Apple Silicon (MLX) via [mlx-vlm](https://github.com/Blaizzy/mlx-vlm).
+
+## Training data
+
+16,730 examples combined from three frontier-model distillation trace datasets (all CC-BY-4.0):
+
+- [greghavens/fable-5-coding-and-debugging-traces](https://huggingface.co/datasets/greghavens/fable-5-coding-and-debugging-traces)
+- [greghavens/gpt-5.6-sol-coding-and-debugging-traces](https://huggingface.co/datasets/greghavens/gpt-5.6-sol-coding-and-debugging-traces)
+- [greghavens/kimi-k3-coding-and-debugging-traces](https://huggingface.co/datasets/greghavens/kimi-k3-coding-and-debugging-traces)
+
+Split by trajectory (not row) so no trajectory leaks across train/valid/test.
+
+## Training config
+
+| | |
+|---|---|
+| Method | LoRA, all `language_model` linear layers |
+| Rank / alpha | 8 / 16 |
+| Iterations | 1000 (~1.1 epochs) |
+| Effective batch | 16 (grad accumulation) |
+| Learning rate | 2e-5, constant |
+| Max sequence length | 8192 |
+| Loss | response-only (prompt masked) |
+
+## Evaluation
+
+Held-out test split (never seen in training), base vs. this adapter:
+
+| Metric | Base | +LoRA | Δ |
+|---|---|---|---|
+| Perplexity (held-out NLL) | 4.23 | 2.15 | −49% |
+| HumanEval pass@1 (n=164) | 92.1% | 89.0% | −3.1pp |
+| Tool-name accuracy (n=150) | 68.0% | 74.0% | +6.0pp |
+| Tool-argument overlap (n=150) | 23.7% | 41.3% | +17.7pp |
+
+The adapter meaningfully improves tool selection and argument correctness — the actual training objective — at a small cost to isolated, out-of-distribution coding-puzzle accuracy (HumanEval). Perplexity alone overstates the improvement; treat it alongside the functional numbers, not in place of them.
+
+## Usage
+
+```bash
+pip install mlx-vlm
+python -m mlx_vlm.generate \
+    --model mlx-community/gemma-4-12B-it-qat-4bit \
+    --adapter-path gemma-4-12b-it-qat-4bit-frontierdistill \
+    --prompt "Your prompt here"
+```