attention-residuals-100M-baseline/README.md

license, language, library_name, pipeline_tag, datasets, metrics, tags

license	language	library_name	pipeline_tag	datasets	metrics	tags
mit	zh	transformers	text-generation	opencsg/Fineweb-Edu-Chinese-V2.2	perplexity accuracy	pytorch transformers causal-lm qwen3 chinese from-scratch attention-residuals baseline standard-residual

Attention Residuals 100M Baseline

This is the 100M baseline checkpoint for the attention-residuals-reproduction project. It uses a standard Qwen3-style decoder-only Transformer with standard residual connections, trained from scratch on Chinese data.

Model Details

• Mode: baseline
• Architecture: Qwen3-style causal language model
• Residual type: standard residual connection
• Hidden size: 512
• Layers: 12
• Attention heads: 8
• KV heads: 4
• FFN intermediate size: 1536
• Sequence length: 2048
• Training steps: 20,000
• Training data: opencsg/Fineweb-Edu-Chinese-V2.2

Intended Use

This checkpoint is mainly intended for research comparison with Attention Residuals variants. It is not instruction-tuned and should not be used as a chat model.

Evaluation

Metric	Result
Chinese Held-out PPL	128.58
C-Eval Acc	0.2664
CMMLU Acc	0.2594

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "你的用户名/attention-residuals-100M-baseline"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

prompt = "人工智能的发展"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=100,
    do_sample=True,
    temperature=0.8,
    top_p=0.95,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))