Text Generation
Safetensors
English
qwen3
quantized
awq
4-bit precision
compression
icpr2026
conversational
How to use from
SGLangUse 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 "shantipriya/Qwen3-8B-AWQ-4bit" \
--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": "shantipriya/Qwen3-8B-AWQ-4bit",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Quick Links
Configuration Parsing Warning:In config.json: "quantization_config.modules_to_not_convert" must be an array
Qwen3-8B AWQ 4-bit
4-bit AWQ (Activation-aware Weight Quantization) of Qwen/Qwen3-8B.
Quantization details
| Parameter | Value |
|---|---|
| Method | AWQ (GEMM kernel) |
| Bits | 4 |
| Group size | 128 |
| Calibration | WikiText-2 (512 samples) |
Benchmark results (200-sample subset)
| Benchmark | BF16 Baseline | AWQ 4-bit | Drop |
|---|---|---|---|
| GSM8K accuracy | 19.0% | pending | — |
| BoolQ accuracy | 87.5% | pending | — |
| MS MARCO ROUGE-L | 0.0616 | pending | — |
AWQ benchmark evaluation in progress. Results will be updated once complete.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"shantipriya/Qwen3-8B-AWQ-4bit",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("shantipriya/Qwen3-8B-AWQ-4bit")
inputs = tokenizer("Hello, how are you?", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Citation
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "shantipriya/Qwen3-8B-AWQ-4bit" \ --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": "shantipriya/Qwen3-8B-AWQ-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'