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| Benchmark | VibeThinker-1.5B | DeepSeek R1 (671B) |
|---|---|---|
| AIME24 | 80.3 | 79.8 |
| AIME25 | 74.4 | 70.0 |
| HMMT25 | 50.4 | 41.7 |
| Parameter | Value |
|---|---|
| Architecture | Qwen 2.5 |
| Context Length | 2048 |
| Batch Size | 64 |
| Chunks | 4 (hybrid) |
| FFN Quantization | LUT6 (Apple Neural Engine), per-channel group size 4 |
| LM Head Quantization | LUT6 (Apple Neural Engine), per-channel group size 4 |
| Embeddings | Unquantized |
| Argmax in Model | No |
qwen25_FFN_PF_lut6_chunk_01of04.mlmodelc) runs the first transformer layer's attention in FP32 (unquantized), while chunks 2-4 use standard LUT6 quantization.+inf) before masking. This leads to softmax saturation and catastrophic output divergence. Running first-layer attention at full precision eliminates the overflow and recovers near-exact parity with the original Hugging Face model.1temperature: 0.6
2top_p: 0.95
3top_k: 0
4do_sample: true1pip install coremltools transformers
2
3# Basic chat
4python chat.py --meta ./meta.yaml
5
6# Full conversation mode with history
7python chat_full.py --meta ./meta.yamlNote: First load takes time as macOS places the model on the Neural Engine. Subsequent loads are fast.