Create README.md

README.md

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+---
+license: llama3.2
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+base_model:
+- meta-llama/Llama-3.2-1B-Instruct
+pipeline_tag: text-generation
+tags:
+- gptqmodel
+- modelcloud
+- llama3.2
+- instruct
+- int4
+---
+This model has been quantized using [GPTQModel](https://github.com/ModelCloud/GPTQModel).
+
+- **bits**: 4
+- **dynamic**: null
+- **group_size**: 32
+- **desc_act**: true
+- **static_groups**: false
+- **sym**: true
+- **lm_head**: false
+- **true_sequential**: true
+- **quant_method**: "gptq"
+- **checkpoint_format**: "gptq"
+- **meta**：
+  - **quantizer**: gptqmodel:1.1.0
+  - **uri**: https://github.com/modelcloud/gptqmodel
+  - **damp_percent**: 0.1
+  - **damp_auto_increment**: 0.0015
+
+
+## Example:
+```python
+from transformers import AutoTokenizer
+from gptqmodel import GPTQModel
+
+model_name = "ModelCloud/Llama-3.2-1B-Instruct-gptqmodel-4bit-vortext-v1"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = GPTQModel.from_quantized(model_name)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+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)
+```