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.
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 ([email protected]).