Multi-task Language Understanding on MMLU (ET, ES, E*S, AR%)
61.2E*SRecover-LoRA
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| Recover-LoRAModel=Qwen3-4B, Quantization=W2-GateUp, Training Data Source=OpenHermes-10k, Training Samples=10k2026.06 | 61.2 | — | — | 83.7 | |
| Recover-LoRAModel=Qwen3-4B, Quantization=W2-GateUp, Training Data Source=Synthetic-10k, Training Samples=10k2026.06 | 60.5 | — | — | 82.2 | |
| Quantized Student (W2-GateUp)Model=Qwen3-4B, Quantization=W2-GateUp (gate/up at INT2)2026.06 | — | — | 24.5 | — | |
| Teacher (BF16)Model=Qwen3-4B, Precision=BF162026.06 | — | 68.3 | — | — |