This is the 0.6B 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: 1024
Layers: 28
Attention heads: 16
KV heads: 8
FFN intermediate size: 3072
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	41.83
C-Eval Acc	0.2533
CMMLU Acc	0.2656

Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "你的用户名/attention-residuals-0.6B-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))

Ethangou/attention-residuals-0.6B-baseline

Attention Residuals 0.6B Baseline

Model Details

Intended Use

Evaluation

Usage

Ethangou/
attention-residuals-0.6B-baseline