初始化项目，由ModelHub XC社区提供模型

Model: DALabCommunity/Haidass-143M-v1
Source: Original Platform

README.md

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+---
+language:
+- en
+- zh
+license: apache-2.0
+datasets:
+- openbmb/Ultra-FineWeb
+- mlfoundations/dclm-baseline-1.0
+- HuggingFaceTB/finemath
+tags:
+- haidass
+- npu
+- bilingual
+- mindspeed-llm
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+<div align="center">
+  <img src="logo.png" width="400"/>
+</div>
+
+# Haidass-143M
+
+<p align="center">
+English |
+<a href="https://huggingface.co/DALabCommunity/Haidass-143M-v1/blob/main/README_ZH.md">中文</a>
+</p>
+
+A bilingual (English/Chinese) small language model trained entirely on **Huawei Ascend** NPU ecosystem.
+
+## Model Overview
+
+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.
+
+## Model Architecture
+
+| 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 |
+
+## Training Data
+
+The model was trained on approximately 100B tokens of mixed English and Chinese data. Primary data sources:
+
+- [openbmb/Ultra-FineWeb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb) (ultrafineweb-en + ultrafineweb-zh)
+- [mlfoundations/dclm-baseline-1.0-parquet](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0-parquet) (dclm)
+- [HuggingFaceTB/finemath](https://huggingface.co/datasets/HuggingFaceTB/finemath) (finemath-4plus)
+
+
+## Training Configuration
+
+| 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 |
+
+
+## Optimizer
+
+| Parameter | Value |
+|------|------|
+| Optimizer | AdamW |
+| Peak learning rate | 3e-4 |
+| Min learning rate | 3e-5 |
+
+
+## Tokenizer
+
+| Property | Value |
+|------|------|
+| Type | SentencePiece BPE |
+| Vocabulary size | 64,000 |
+| Language coverage | English + Chinese |
+
+## Evaluation
+
+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 |
+
+## Key Features
+
+- **Fully Ascend-native**: Trained entirely on Huawei Ascend 910B NPUs using the MindSpeed-LLM framework
+- **Bilingual**: Trained on a mixture of English and Chinese data
+
+
+## Intended Use
+
+This is a research model, suitable for:
+- Studying training dynamics of small models on Ascend NPUs
+- English/Chinese language modeling research
+- Serving as a base model for fine-tuning or annealing experiments
+
+## Limitations
+
+- Small model scale; reasoning and generation capabilities are limited
+- raw pretrained model only
+
+## Citation
+
+```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}
+}
+```
+
+## License
+
+Apache 2.0