Instructions to use RedHatAI/QwQ-32B-Preview-quantized.w8a8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/QwQ-32B-Preview-quantized.w8a8 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/QwQ-32B-Preview-quantized.w8a8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/QwQ-32B-Preview-quantized.w8a8")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/QwQ-32B-Preview-quantized.w8a8", 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 RedHatAI/QwQ-32B-Preview-quantized.w8a8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/QwQ-32B-Preview-quantized.w8a8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/QwQ-32B-Preview-quantized.w8a8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/QwQ-32B-Preview-quantized.w8a8

SGLang

How to use RedHatAI/QwQ-32B-Preview-quantized.w8a8 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 "RedHatAI/QwQ-32B-Preview-quantized.w8a8" \
    --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": "RedHatAI/QwQ-32B-Preview-quantized.w8a8",
		"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 "RedHatAI/QwQ-32B-Preview-quantized.w8a8" \
        --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": "RedHatAI/QwQ-32B-Preview-quantized.w8a8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/QwQ-32B-Preview-quantized.w8a8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/QwQ-32B-Preview-quantized.w8a8
```

QwQ-32B-Preview-quantized.w8a8 / quant_w8a8.sh

Eldar Kurtic

add model

a7050c3 over 1 year ago

Raw

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610 Bytes

	#!/bin/bash

	# for DAMPENING_FRAC 0.01 0.1;
	# do
	# for OBSERVER minmax mse;
	# do
	# for ACTORDER False group;
	# do

	# export DAMPENING_FRAC=0.01
	# export OBSERVER="minmax"

	export CUDA_VISIBLE_DEVICES=${1}
	export MDL=${2}
	export OBSERVER=${3} # minmax mse
	export DAMPENING_FRAC=${4} # 0.01 0.1

	for CALIB_SIZE in 128 512 1024;
	do
	python w8a8.py --model_path ${MDL} --quant_path "output_dir_w8a8/${MDL}/calib${CALIB_SIZE}_eosFalse_damp${DAMPENING_FRAC}_obs${OBSERVER}" --calib_size ${CALIB_SIZE} --dampening_frac ${DAMPENING_FRAC} --observer ${OBSERVER}
	done