Instructions to use Ethangou/attention-residuals-0.6B-baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ethangou/attention-residuals-0.6B-baseline with Transformers:

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

pipe = pipeline("text-generation", model="Ethangou/attention-residuals-0.6B-baseline")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ethangou/attention-residuals-0.6B-baseline")
model = AutoModelForCausalLM.from_pretrained("Ethangou/attention-residuals-0.6B-baseline", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ethangou/attention-residuals-0.6B-baseline with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ethangou/attention-residuals-0.6B-baseline"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ethangou/attention-residuals-0.6B-baseline",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ethangou/attention-residuals-0.6B-baseline

SGLang

How to use Ethangou/attention-residuals-0.6B-baseline 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 "Ethangou/attention-residuals-0.6B-baseline" \
    --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": "Ethangou/attention-residuals-0.6B-baseline",
		"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 "Ethangou/attention-residuals-0.6B-baseline" \
        --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": "Ethangou/attention-residuals-0.6B-baseline",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ethangou/attention-residuals-0.6B-baseline with Docker Model Runner:
```
docker model run hf.co/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

Metric	Result
Chinese Held-out PPL	41.83
C-Eval Acc	0.2533
CMMLU Acc	0.2656

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))

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Safetensors

Model size

0.5B params

Tensor type

BF16

Ethangou
/

attention-residuals-0.6B-baseline

Attention Residuals 0.6B Baseline

Model Details

Intended Use

Evaluation

Usage

Dataset used to train Ethangou/attention-residuals-0.6B-baseline