[!IMPORTANT] Independently computed using dedicated cloud compute clusters, funded 100% out-of-pocket. If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies.
Handcrafted Importance Matrix (I-Matrix) computed for InternScience/Sage-A1.5 to power high-fidelity downstream quantization (IQ4_XS, IQ3_M, IQ3_S, IQ2_XS, etc.).
Calculated using official llama.cpp b11512 (commit a11f57ba93797579a5d1855ee216a31f10242676) with full layer offloading (-ngl 99).
| Parameter | Specification |
|---|---|
| Model Repository | InternScience/Sage-A1.5 |
| Model Revision | 3e6b02c5e6afbc481fd4556baca8e6009f26d696 |
| Model Architecture | qwen35moe (40 layers, 256 experts per layer, top-k=8, intermediate=512) |
| Target Tokens | 1,000,000 tokens (489 chunks x 2048 context = 1,001,472 positions) |
| Execution Flags | --process-output --no-ppl --no-escape -c 2048 -b 2048 -ub 2048 --chunks 489 -ngl 99 |
The calibration dataset was assembled with a deterministic 35/25/25/15 ratio spanning 11 natural languages and 10 programming languages, fully verified against the native tokenizer:
| Component | Target Share | Measured Share | Effective Tokens | Sub-categories / Scope |
|---|---|---|---|---|
| Code | 35.0% | 34.99% | 350,419 | 10 Programming Languages (Python, JS, TS, Java, C++, C, Go, Rust, SQL, Shell) |
| Reasoning | 25.0% | 24.83% | 248,641 | 11 Natural Languages (Mathematical, logical, and step-by-step reasoning) |
| Prose / Text | 25.0% | 25.06% | 251,005 | 11 Natural Languages (High-density prose, encyclopedic text, technical documentation) |
| Tools / Agentic | 15.0% | 15.12% | 151,407 | 11 Natural Languages (Multi-turn tool transcripts, function calling, API schemas) |
| Total Positions | 100.0% | 100.0% | 1,001,472 | 489 chunks of 2048 tokens |
| Programming Language | Effective Tokens | Category Share | Corpus Share |
|---|---|---|---|
| Python | 92,660 | 26.44% | 9.25% |
| JavaScript | 57,988 | 16.55% | 5.79% |
| C++ | 35,351 | 10.09% | 3.53% |
| TypeScript | 35,216 | 10.05% | 3.52% |
| Java | 34,763 | 9.92% | 3.47% |
| Rust | 23,558 | 6.72% | 2.35% |
| Go | 23,556 | 6.72% | 2.35% |
| C | 23,319 | 6.65% | 2.33% |
| Shell | 12,066 | 3.44% | 1.20% |
| SQL | 11,942 | 3.41% | 1.19% |
| Total Code | 350,419 | 100.00% | 34.99% |
| Language | Reasoning Tokens | Prose / Text Tokens | Tools Tokens | Total Language Tokens | Corpus Share |
|---|---|---|---|---|---|
| English (en) | 51,930 | 51,907 | 31,123 | 134,960 | 13.48% |
| Spanish (es) | 37,052 | 37,350 | 22,744 | 97,146 | 9.70% |
| Chinese (zh) | 37,033 | 37,037 | 22,252 | 96,322 | 9.62% |
| French (fr) | 29,649 | 30,150 | 17,779 | 77,578 | 7.75% |
| Portuguese (pt) | 18,524 | 19,055 | 11,372 | 48,951 | 4.89% |
| German (de) | 18,612 | 18,733 | 11,215 | 48,560 | 4.85% |
| Russian (ru) | 14,903 | 15,129 | 9,270 | 39,302 | 3.92% |
| Japanese (ja) | 14,882 | 14,931 | 9,171 | 38,984 | 3.89% |
| Arabic (ar) | 11,148 | 11,224 | 6,809 | 29,181 | 2.91% |
| Italian (it) | 7,467 | 7,770 | 4,860 | 20,097 | 2.01% |
| Korean (ko) | 7,441 | 7,719 | 4,812 | 19,972 | 1.99% |
| Subtotal | 248,641 | 251,005 | 151,407 | 651,053 | 65.01% |
[!NOTE] All document boundaries and token allocations were verified against the official native tokenizer to guarantee strict zero-deviation quotas across all language partitions.
In an MoE architecture with 256 experts per layer and top-k=8, token observations are distributed across 10,240 independent routing experts (30,720 distinct projection matrices):
| Metric | Measured Value | Standard Required |
|---|---|---|
| Total Expert Projection Matrices | 30,720 (40 layers x 256 experts x 3 FFN projections) | 30,720 |
| Observed Expert Matrices | 30,720 (100.0%) | 100.0% |
| Unobserved Experts | 0 | 0 |
| Minimum Observations per Expert | 440 rows | Greater than 0 |
| Maximum Observations per Expert | 361,813 rows | Finite F32 |
| Average Observations per Expert | 31,296.0 rows | Finite F32 |
| Validation Status | VALIDATED_OBSERVATIONS |
Validated |
| File Name | File Size | SHA256 Checksum | Format |
|---|---|---|---|
Sage-A1.5-VAL-1M-I-Matrix.gguf |
384,359,808 bytes | 634336253dedbebb63643dc6cd1b51821c2132187c45c5a044a5d43d6c8b813d |
GGUF v3 |
All compute hours and hardware allocations are funded 100% out-of-pocket to bring high-quality open-source quantizations and calibration assets to the community.
If these builds help you in your day-to-day workflow, research, or development, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and supports my university studies.
InternScience/Sage-A1.5