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Model: DALabCommunity/Haidass-143M-v1 Source: Original Platform
2026-09-10 10:44:16 +08:00
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--- a/README.md
+++ b/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
--- a/README_ZH.md
+++ b/README_ZH.md
@@ -0,0 +1,120 @@
+
+
+<div align="center">
+  <img src="logo.png" width="400"/>
+</div>
+
+# Haidass-143M
+
+<p align="center">
+<a href="https://huggingface.co/DALabCommunity/Haidass-143M-v1/blob/main/README.md">English</a> |
+中文
+</p>
+
+中英双语小语言模型，在**华为昇腾**生态上进行全流程训练。
+
+## 模型简介
+
+Haidass-143M 是一个 143M 参数的中英双语语言模型，在约 100B token 的中英文数据上训练完成。模型在华为昇腾生态上进行全流程训练，整体流程基于 **MindSpeed-LLM** 框架和 Atlas A2 服务器（910B）。同时配套训练了大小为 64,000 的中英双语词表。该模型在 150M 以下参数规模的多语言模型中具有较强竞争力，并在多个评测指标中排名靠前。
+
+## 模型架构
+
+| 参数 | 值 |
+|------|-----|
+| 架构 | Qwen3 |
+| 层数 | 30 |
+| 隐层维度 | 576 |
+| 注意力头数 | 9 |
+| KV 头数 (GQA) | 3 |
+| 头维度 | 64 |
+| FFN 中间维度 | 1,536 |
+| 词表大小 | 64,000 |
+| 最大序列长度 | 4,096 |
+| 绑定嵌入 | 是 |
+| 位置编码 | RoPE (θ=100,000) |
+| 注意力偏置 | 无 |
+| 精度 | BF16 |
+| 总参数量 | ~143M |
+
+## 训练数据
+
+模型在约 100B token 的中英文混合数据上训练。主要数据来源为：
+
+- [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)
+
+
+## 训练配置
+
+| 参数 | 值 |
+|------|------|
+| 框架 | MindSpeed-LLM (v2.3.0) |
+| 硬件 | 8 台 Atlas A2 服务器 (每台 8 卡 NPU，256 核) |
+| NPU 型号 | 华为昇腾 910B |
+| 总 NPU 数 | 64 (8 节点 × 8 卡) |
+| 序列长度 | 4,096 |
+
+
+## 优化器
+
+| 参数 | 值 |
+|------|------|
+| 优化器 | AdamW |
+| 峰值学习率 | 3e-4 |
+| 最低学习率 | 3e-5 |
+
+
+## 词表
+
+| 属性 | 值 |
+|------|------|
+| 类型 | SentencePiece BPE |
+| 词表大小 | 64,000 |
+| 语言覆盖 | 英文 + 中文 |
+
+## 测评
+
+在 checkpoint (~98B tokens) 上基于 lighteval 框架（v0.9.2）测评。
+
+| 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 |
+
+## 核心特点
+
+- **全昇腾原生**: 完全在华为昇腾 910B NPU 上训练，使用 MindSpeed-LLM 框架
+- **中英双语**: 模型基于中英混合数据集训练
+
+
+## 预期用途
+
+本模型为研究型模型，适用于:
+- 研究小模型在昇腾 NPU 上的训练动态
+- 中英文语言建模研究
+- 作为后续微调或退火实验的基础模型
+
+## 局限性
+
+- 模型规模较小，推理和生成能力有限
+- 仅为原始预训练模型
+
+## 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
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+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 64000
+}
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+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|im_end|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": null,
+  "legacy": true,
+  "add_eos_token": false,
+  "sp_model_kwargs": {}
+}