Create README.md

README.md

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+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE
+language:
+- en
+base_model: Qwen/Qwen2.5-0.5B
+pipeline_tag: text-generation
+tags:
+- gptqmodel
+- modelcloud
+- chat
+- qwen2
+- instruct
+- int4
+- gptq
+- 4bit
+- W4A16
+---
+
+This model has been quantized using [GPTQModel](https://github.com/ModelCloud/GPTQModel).
+
+- **bits**: 4
+- **dynamic**: null
+- **group_size**: 128
+- **desc_act**: true
+- **static_groups**: false
+- **sym**: true
+- **lm_head**: false
+- **true_sequential**: true
+- **quant_method**: "gptq"
+- **checkpoint_format**: "gptq"
+- **meta**：
+  - **quantizer**: gptqmodel:1.7.0
+  - **uri**: https://github.com/modelcloud/gptqmodel
+  - **damp_percent**: 0.1
+  - **damp_auto_increment**: 0.0025
+
+
+## Example:
+```python
+from transformers import AutoTokenizer
+from gptqmodel import GPTQModel
+
+tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Qwen2.5-0.5B-Instruct-gptqmodel-4bit")
+model = GPTQModel.load("ModelCloud/Qwen2.5-0.5B-Instruct-gptqmodel-4bit")
+
+messages = [
+    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
+    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
+]
+input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
+
+outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
+result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
+
+print(result)
+```