Instructions to use DALabCommunity/Haidass-143M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DALabCommunity/Haidass-143M-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DALabCommunity/Haidass-143M-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DALabCommunity/Haidass-143M-v1")
model = AutoModelForCausalLM.from_pretrained("DALabCommunity/Haidass-143M-v1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DALabCommunity/Haidass-143M-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DALabCommunity/Haidass-143M-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DALabCommunity/Haidass-143M-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DALabCommunity/Haidass-143M-v1

SGLang

How to use DALabCommunity/Haidass-143M-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "DALabCommunity/Haidass-143M-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DALabCommunity/Haidass-143M-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "DALabCommunity/Haidass-143M-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DALabCommunity/Haidass-143M-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DALabCommunity/Haidass-143M-v1 with Docker Model Runner:
```
docker model run hf.co/DALabCommunity/Haidass-143M-v1
```

Haidass-143M-v1 / README_ZH.md

waynelm

Update README_ZH.md

ebd28a7 verified 12 days ago

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Haidass-143M

English | 中文

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 (ultrafineweb-en + ultrafineweb-zh)
mlfoundations/dclm-baseline-1.0-parquet (dclm)
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

@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