metadata
license: mit
tags:
- code
pipeline_tag: text-generation
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
| EvalPlus | pass@1 |
|---|---|
| HumanEval | 86.0 |
| HumanEval+ | 81.1 |
We use a simple template to generate the solution for evalplus:
"Complete the following Python function:\n{prompt}"
| 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.You should use transformer version 4.39 to avoid loading tokenizer errors.
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 = """Complete the following Python function:
from typing import List
def has_close_elements(numbers: List[float], threshold: float) -> bool:
""" Check if in given list of numbers, are any two numbers closer to each other than
given threshold.
>>> has_close_elements([1.0, 2.0, 3.0], 0.5)
False
>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)
True
"""
"""
messages = [
{"role": "user", "content": prompt}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
res = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
Contact information
For personal communication related to this project, please contact Nha Nguyen Van (nha.nguyen@ntq-solution.com.vn).