7.62 GB
110 files
Updated 9 days ago
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MANIFEST.json337 Bytes
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README.md2.26 kB
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RELEASE_NOTES.md832 Bytes
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generation_config.json202 Bytes
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merges.txt3.35 MB
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preprocessor_config.json390 Bytes
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tokenizer.json12.8 MB
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tokenizer_config.json16.7 kB
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video_preprocessor_config.json385 Bytes
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vocab.json6.72 MB
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README.md

Screenshot 2026-08-03 at 10.32.36 AM

Benchmarks

Mach-1 Small is compared against its own BF16 teacher, as is every other model here. Retention = 100 × student score ÷ that student's own BF16 teacher score — a compression-fidelity measure, not an absolute capability score across model families.

Mean retention, 12 benchmarks
Mach-1 Small 95.0%
Ternary Bonsai 27B (PrismML) 94.4%
Gemma 4 Q2_K_XL (Unsloth) 85.6%

Per-benchmark

Benchmark Mach-1 Small Ternary Bonsai 27B Gemma 4 Q2_K_XL
AIME26 99.5% 93.8% 67.7%
MATH-500 99.4% 98.2% 95.6%
AIME25 99.1% 97.4% 67.2%
GSM8K 98.4% 100.2% 97.3%
MBPP+ 98.3% 98.4% 92.2%
HumanEval+ 97.4% 98.7% 94.1%
MMLU-Redux 96.2% 94.0% 96.9%
IFEval 94.0% 89.8% 95.5%
MuSR 92.7% 91.6% 91.1%
BFCL-v3 92.0% 98.9% 95.7%
τ²-bench 90.0% 93.5% 73.1%
IFBench 83.2% 77.7% 61.3%
Mean 95.0% 94.4% 85.6%

Mach-1 Small's own scores and teacher scores:

Benchmark Score Teacher (Qwen3.6-35B-A3B BF16) Retention
AIME26 89.58 90.00 99.5%
MATH-500 98.00 98.60 99.4%
AIME25 87.50 88.33 99.1%
GSM8K 94.69 96.21 98.4%
MBPP+ 94.44 96.03 98.3%
HumanEval+ 92.68 95.12 97.4%
MMLU-Redux 89.18 92.68 96.2%
IFEval 83.75 89.05 94.0%
MuSR 61.77 66.66 92.7%
BFCL-v3 68.97 74.98 92.0%
τ²-bench 71.58 79.51 90.0%
IFBench 54.08 64.97 83.2%

Speed

Screenshot 2026-08-03 at 11.55.47 AM

Total size
7.62 GB
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Last updated
Aug 4
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