Update model card with v12b benchmark results

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-1.7B
+tags:
+  - gguf
+  - speech-to-text
+  - text-cleanup
+  - dictation
+  - lora
+  - qwen3
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# TurboSpeak Correction Model
+
+Fine-tuned Qwen3-1.7B (Q4_K_M quantization) for cleaning up speech transcription output.
+
+## What it does
+
+- Removes filler words (um, uh, like, you know, basically)
+- Fixes stutters (w-w-want → want)
+- Resolves mid-sentence self-corrections (speaker says X then corrects to Y → keeps only Y)
+- Preserves all content words — never adds words the speaker didn't say
+
+## Performance
+
+| Metric | Score |
+|--------|-------|
+| Correction accuracy | 87.5% (35/40 P+G) |
+| Filler/stutter handling | 100% |
+| Avg latency | ~100ms on Apple Silicon |
+| Model size | 1.0 GB (Q4_K_M) |
+
+## Training
+
+- Base model: Qwen/Qwen3-1.7B
+- Fine-tuning: LoRA (rank=8, lr=5e-5, 500 iterations)
+- Training data: 2,390 examples (1,710 base + 680 hard corrections)
+- Quantization: Q4_K_M via llama.cpp
+
+## Usage
+
+Used by [TurboSpeak](https://github.com/kavanaghpatrick/gpu-forge) macOS dictation app.
+Runs locally via llama.cpp / llama-cpp-2 Rust bindings.
+
+### System prompt (ChatML format)
+
+```
+Clean up the transcribed text. Remove filler words, fix stutters, and resolve mid-sentence corrections. Output only the cleaned text.
+```
+
+## License
+
+Apache 2.0 (same as base Qwen3 model)