Create README.md

README.md

@@ -0,0 +1,47 @@
+---
+tags:
+- gptq
+- 4bit
+- int4
+- gptqmodel
+- modelcloud
+- llama-3.1
+- 70b
+- instruct
+---
+This model has been quantized using [GPTQModel](https://github.com/ModelCloud/GPTQModel).
+
+- **bits**: 4
+- **group_size**: 128
+- **desc_act**: true
+- **static_groups**: false
+- **sym**: true
+- **lm_head**: false
+- **damp_percent**: 0.01
+- **true_sequential**: true
+- **model_name_or_path**: ""
+- **model_file_base_name**: "model"
+- **quant_method**: "gptq"
+- **checkpoint_format**: "gptq"
+- **meta**：
+  - **quantizer**: "gptqmodel:0.9.9-dev0"
+
+**Here is an example:**
+```python
+from transformers import AutoTokenizer
+from gptqmodel import GPTQModel
+
+model_name = "ModelCloud/Meta-Llama-3.1-70B-Instruct-gptq-4bit"
+
+prompt = [{"role": "user", "content": "I am in Shanghai, preparing to visit the natural history museum. Can you tell me the best way to"}]
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+model = GPTQModel.from_quantized(model_name)
+
+input_tensor = tokenizer.apply_chat_template(prompt, add_generation_prompt=True, return_tensors="pt")
+outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=100)
+result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
+
+print(result)
+```