Create README.md

README.md

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+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE
+language:
+- en
+base_model:
+- Qwen/QwQ-32B-Preview
+pipeline_tag: text-generation
+tags:
+- gptqmodel
+- modelcloud
+- chat
+- qwen2
+- qwq
+- instruct
+- gptq
+- gguf
+---
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/641c13e7999935676ec7bc03/F7pXCPgPKmXdW_jWFQQ6L.png)
+
+## Example with transformers:
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "ModelCloud/QwQ-32B-Preview-gguf-vortex-v1"
+filename = "QwQ-32B-Preview-Q4_K_M.gguf"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
+model = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename, device_map="cuda", torch_dtype=torch.float16)
+
+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)
+```