Instructions to use 2imi9/Qwen3-1.7b-gptq-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 2imi9/Qwen3-1.7b-gptq-int4 with Transformers:

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

pipe = pipeline("text-generation", model="2imi9/Qwen3-1.7b-gptq-int4")
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("2imi9/Qwen3-1.7b-gptq-int4")
model = AutoModelForCausalLM.from_pretrained("2imi9/Qwen3-1.7b-gptq-int4", 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 2imi9/Qwen3-1.7b-gptq-int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "2imi9/Qwen3-1.7b-gptq-int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "2imi9/Qwen3-1.7b-gptq-int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/2imi9/Qwen3-1.7b-gptq-int4

SGLang

How to use 2imi9/Qwen3-1.7b-gptq-int4 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 "2imi9/Qwen3-1.7b-gptq-int4" \
    --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": "2imi9/Qwen3-1.7b-gptq-int4",
		"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 "2imi9/Qwen3-1.7b-gptq-int4" \
        --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": "2imi9/Qwen3-1.7b-gptq-int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 2imi9/Qwen3-1.7b-gptq-int4 with Docker Model Runner:
```
docker model run hf.co/2imi9/Qwen3-1.7b-gptq-int4
```

Qwen3 1.7B GPTQ INT4

GPTQ 4-bit quantized version of Qwen/Qwen3-1.7B with group size 16.

Model Details

Quantization: GPTQ INT4 with group size 16
Size: ~1GB (4x compression from original)
Format: W4A16 (4-bit weights, 16-bit activations)
Compatible: Native transformers library support

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "2imi9/qwen3-1.7b-gptq-int4",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("2imi9/qwen3-1.7b-gptq-int4")

# Generate text
inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Gradio Demo

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("2imi9/qwen3-1.7b-gptq-int4", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("2imi9/qwen3-1.7b-gptq-int4")

def chat(message, history):
    inputs = tokenizer(message, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7)
    response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
    return response

gr.ChatInterface(chat).launch()

Perfect for Gradio demos due to small size and fast inference.

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Safetensors

Model size

2B params

Tensor type

I64

I32

BF16

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Qwen3-1.7b-gptq-int4

Qwen3 1.7B GPTQ INT4

Model Details

Usage

Gradio Demo

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