Instructions to use Crusadersk/qwen2.5-1.5b-gptq-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Crusadersk/qwen2.5-1.5b-gptq-4bit with Transformers:

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

pipe = pipeline("text-generation", model="Crusadersk/qwen2.5-1.5b-gptq-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Crusadersk/qwen2.5-1.5b-gptq-4bit")
model = AutoModelForCausalLM.from_pretrained("Crusadersk/qwen2.5-1.5b-gptq-4bit", 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 Crusadersk/qwen2.5-1.5b-gptq-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Crusadersk/qwen2.5-1.5b-gptq-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Crusadersk/qwen2.5-1.5b-gptq-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Crusadersk/qwen2.5-1.5b-gptq-4bit

SGLang

How to use Crusadersk/qwen2.5-1.5b-gptq-4bit 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 "Crusadersk/qwen2.5-1.5b-gptq-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": "Crusadersk/qwen2.5-1.5b-gptq-4bit",
		"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 "Crusadersk/qwen2.5-1.5b-gptq-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": "Crusadersk/qwen2.5-1.5b-gptq-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Crusadersk/qwen2.5-1.5b-gptq-4bit with Docker Model Runner:
```
docker model run hf.co/Crusadersk/qwen2.5-1.5b-gptq-4bit
```

qwen2.5-1.5b-gptq-4bit / quantize_manifest.json

Crusadersk

Self-quantized qwen2.5-1.5b-gptq 4-bit (group_size=128, seed=42)

fd1b594 verified 4 months ago

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269 Bytes

	{
	"model_name": "qwen2.5-1.5b",
	"hf_id": "Qwen/Qwen2.5-1.5B-Instruct",
	"method": "gptq",
	"bits": 4,
	"group_size": 128,
	"calibration_dataset": "allenai/c4",
	"calibration_samples": 128,
	"seed": 42,
	"elapsed_s": 554.7059471607208,
	"tool": "gptqmodel"
	}