Create README.md

README.md

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+---
+license: other
+license_name: tongyi-qianwen
+license_link: >-
+  https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- chat
+---
+
+# Nxcode-CQ-7B-orpo
+
+
+## Introduction
+
+Nxcode-CQ-7B-orpo is the Code-Specific version of Qwen1.5. It is a transformer-based decoder-only language model pretrained on a large amount of data of codes. 
+
+* Strong code generation capabilities and competitve performance across a series of benchmarks;
+* Supporting long context understanding and generation with the context length of 64K tokens;
+* Supporting 92 coding languages
+* Excellent performance in text-to-SQL, bug fix, etc.
+
+## Quickstart
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/CodeQwen1.5-7B-Chat",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("NTQAI/Nxcode-CQ-7B-orpo")
+
+prompt = "Write a quicksort algorithm in python."
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```