Create README.md

README.md

@@ -0,0 +1,42 @@
+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"
+
+
+You can use [GPTQModel](https://github.com/ModelCloud/GPTQModel) for model inference.
+```python
+import torch
+from transformers import AutoTokenizer, GenerationConfig
+from gptqmodel import GPTQModel
+model_name = "/monster/data/model/DeepSeek-V2-Chat-0628/gptq_gptq_4_0719/"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+# `max_memory` should be set based on your devices
+max_memory = {i: "75GB" for i in range(2)}
+# `device_map` cannot be set to `auto`
+model = GPTQModel.from_quantized(model_name, trust_remote_code=True, device_map="sequential", max_memory=max_memory, torch_dtype=torch.float16, attn_implementation="eager")
+
+model.generation_config = GenerationConfig.from_pretrained(model_name)
+model.generation_config.pad_token_id = model.generation_config.eos_token_id
+
+messages = [
+    {"role": "user", "content": "Write a piece of quicksort code in C++"}
+]
+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=100)
+result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
+print(result)
+ 
+```