Ethangou/attention-residuals-0.6B-baseline

Attention Residuals 0.6B Baseline

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

Usage

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))