Instructions to use HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text with Transformers:

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

pipe = pipeline("text-generation", model="HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text")
model = AutoModelForCausalLM.from_pretrained("HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text

SGLang

How to use HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text 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 "HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text" \
    --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": "HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text",
		"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 "HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text" \
        --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": "HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text with Docker Model Runner:
```
docker model run hf.co/HarimxChoi/WarpQuant-Qwen3.8-27B-R16E4H4-Text
```

WarpQuant-Qwen3.8-27B-R16E4H4-Text / experiment /text-only-export-validation.json

Harim Choi

Add files using upload-large-folder tool

c52e5d2 verified 8 days ago

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	{
	"source": "/workspace/warpquant-qwen38-27b-20260815/checkpoints/r16e4h4",
	"text_only": "/workspace/warpquant-qwen38-27b-20260815/checkpoints/r16e4h4-text-only.tmp",
	"prompt": "Quantization makes language models",
	"max_abs_logit_error": 0.0,
	"mean_abs_logit_error": 0.0,
	"top1_equal": true,
	"generated_text": "Quantization makes language models smaller and faster, but it can also make them less reliable. In this article"
	}