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Haidass-143M is a 143M-parameter bilingual language model trained on approximately 100B tokens of English and Chinese data. The entire training pipeline runs on the Huawei Ascend ecosystem, using the MindSpeed-LLM framework on Atlas A2 servers (910B). A custom 64,000-token bilingual vocabulary (SentencePiece BPE) was trained alongside the model. This model is competitive among multilingual models under 150M parameters and ranks favorably across multiple evaluation benchmarks.
| Parameter | Value |
|---|---|
| Architecture | Qwen3 |
| Layers | 30 |
| Hidden size | 576 |
| Attention heads | 9 |
| KV heads (GQA) | 3 |
| Head dim | 64 |
| FFN intermediate size | 1,536 |
| Vocabulary size | 64,000 |
| Max sequence length | 4,096 |
| Tie word embeddings | Yes |
| Position encoding | RoPE (θ=100,000) |
| Attention bias | None |
| Precision | BF16 |
| Total parameters | ~143M |
The model was trained on approximately 100B tokens of mixed English and Chinese data. Primary data sources:
| Parameter | Value |
|---|---|
| Framework | MindSpeed-LLM (v2.3.0) |
| Hardware | 8 × Atlas A2 servers (8 NPUs per node, 256 cores) |
| NPU model | Huawei Ascend 910B |
| Total NPUs | 64 (8 nodes × 8 cards) |
| Sequence length | 4,096 |
| Parameter | Value |
|---|---|
| Optimizer | AdamW |
| Peak learning rate | 3e-4 |
| Min learning rate | 3e-5 |
| Property | Value |
|---|---|
| Type | SentencePiece BPE |
| Vocabulary size | 64,000 |
| Language coverage | English + Chinese |
Evaluated at checkpoint (~98B tokens) using the lighteval framework (v0.9.2).
| Benchmark | Score |
|---|---|
| ARC-Easy | 60.44 |
| ARC-Challenge | 27.13 |
| PIQA | 67.25 |
| HellaSwag | 37.91 |
| OpenBookQA | 31.8 |
| Winogrande | 52.17 |
| agi_eval | 23.78 |
This is a research model, suitable for:
bibtex
@misc{haidass-143m,title={haidass-143M: A Bilingual Small Language Model Trained on Ascend 910B},year={2026},note={Based on Qwen3 architecture, trained from scratch on 100B tokens using MindSpeed-LLM on 64× Ascend 910B NPUs}}
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