README.md

---
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 an ORPO fine-tune of Qwen/CodeQwen1.5-7B-Chat on 100k samples ours datasets.

* Strong code generation capabilities and competitve performance across a series of benchmarks;
* Supporting 92 coding languages
* Excellent performance in text-to-SQL, bug fix, etc.

## [Evalplus](https://github.com/evalplus/evalplus)

| EvalPlus | pass@1 |
| --- | --- |
| HumanEval | 86.0 |
| HumanEval+ | 81.1 |

We use the simple tempale for generate the solution for evalplus:

```python
"Complete the following Python function:\n{prompt}"
```

[Evalplus Leaderboard](https://evalplus.github.io/leaderboard.html)
| Models | HumanEval | HumanEval+|
|------ | ------  | ------ |
| GPT-4-Turbo (April 2024)|  90.2| 86.6|
| GPT-4 (May 2023)|  88.4| 81.17|
| GPT-4-Turbo (Nov 2023)|  85.4| 79.3| 
| CodeQwen1.5-7B-Chat|  83.5| 78.7| 
| claude-3-opus (Mar 2024)|  82.9| 76.8|
| DeepSeek-Coder-33B-instruct|  81.1| 75.0|
| WizardCoder-33B-V1.1|  79.9| 73.2|
| OpenCodeInterpreter-DS-33B|  79.3| 73.8|
| speechless-codellama-34B-v2.0|  77.4| 72|
| GPT-3.5-Turbo (Nov 2023)| 76.8| 70.7|
| Llama3-70B-instruct| 76.2| 70.7|

## 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(
    "NTQAI/Nxcode-CQ-7B-orpo",
    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]
```

### Contact information
For personal communication related to this project, please contact Nha Nguyen Van ([email protected]).