Instructions to use Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit with Transformers:

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

pipe = pipeline("text-generation", model="Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit")
model = AutoModelForCausalLM.from_pretrained("Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit

SGLang

How to use Yhyu13/Xwin-Math-7B-V1.0-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 "Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit with Docker Model Runner:
```
docker model run hf.co/Yhyu13/Xwin-Math-7B-V1.0-GPTQ-4bit
```

GPTQ 4-bit actor order version that works in textgen-webui with exllamav2

Generated by using scripts from https://gitee.com/yhyu13/llama_-tools

Original weight : https://huggingface.co/Xwin-LM/Xwin-Math-7B-V1.0

Branch	Bits	GS	Act Order	Damp %	GPTQ Dataset	Seq Len	Size	ExLlama	Desc
main	4	128	Yes	0.1	code	4096	4.4GB	Yes	4-bit, with Act Order. 128 group size

Here is some testing done in textgen-webui, I was using Q&A from this dataset https://huggingface.co/datasets/TIGER-Lab/MathInstruct

Basic arithmatic, the answer (A) is correct $Alt text$

Does not follow the instruction to write python code. And hulluciate an answer not in exists in options $Alt text$

Answer is correct, but hulluciate with a not existing option $Alt text$

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