Instructions to use SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates with Transformers:

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

pipe = pipeline("text-generation", model="SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates")
model = AutoModelForMultimodalLM.from_pretrained("SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates

SGLang

How to use SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates 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 "SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates with Docker Model Runner:
```
docker model run hf.co/SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates
```

SeerAttention-R

This repo contains the decode stage AttnGate weights from paper SeerAttention-R. The current support models are:

Results of Reasoning Tasks

Results of reasoning task with different token budgets. All the results are the averaged pass@1 results with 64 sample per query for AIME, 16 samples for GPQA, and 8 samples for MATH-500.

AIME24

Model	2k	4k	6k	8k	Full Attention
Qwen3-4B	55.42	68.75	70.94	72.50	71.25
Qwen3-8B	56.56	72.29	74.22	75.05	74.48
Qwen3-14B	62.24	75.78	78.02	78.65	78.91
DeepSeek-R1-Distill-Qwen-14B	55.78	66.35	67.50	66.82	67.50

AIME25

Model	2k	4k	6k	8k	Full Attention
Qwen3-4B	45.73	57.60	60.20	62.90	66.41
Qwen3-8B	42.60	56.77	60.31	64.17	67.86
Qwen3-14B	46.67	62.66	67.19	69.01	70.21
DeepSeek-R1-Distill-Qwen-14B	38.44	47.19	52.25	50.05	50.00

MATH500

Model	1k	2k	4k	6k	Full Attention
Qwen3-4B	84.80	92.20	93.60	93.60	93.93
Qwen3-8B	82.82	91.53	94.17	94.53	94.43
Qwen3-14B	85.13	93.20	94.77	94.80	95.22
DeepSeek-R1-Distill-Qwen-14B	87.65	92.10	93.05	93.12	93.30

GPQA Diamond

Model	1k	2k	4k	6k	Full Attention
Qwen3-4B	39.61	51.20	55.20	55.90	56.19
Qwen3-8B	37.59	54.32	59.60	60.48	60.54
Qwen3-14B	44.54	59.72	63.76	64.20	65.25
DeepSeek-R1-Distill-Qwen-14B	51.26	56.79	56.41	57.48	57.80

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Model tree for SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates

Base model

Qwen/Qwen3-14B-Base

Finetuned

Qwen/Qwen3-14B

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Collection including SeerAttention/SeerAttention-Decode-Qwen3-14B-AttnGates

SeerAttention-R

Collection

Decode AttnGate for Reasoning Models • 4 items • Updated Jun 9, 2025