Apel-sin/nxcode-CQ-7B-orpo-exl2

Exllama v2 NTQAI/Nxcode-CQ-7B-orpo

Using turboderp's ExLlamaV2 v0.0.21 for quantization.

The "main" branch only contains the measurement.json, download one of the other branches for the model

Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.

Original model: NTQAI/Nxcode-CQ-7B-orpo
Calibration dataset is based on: m-a-p/CodeFeedback-Filtered-Instruction

Available sizes

Branch	Bits	lm_head bits	Description
8_0	8.0	8.0	Maximum quality that ExLlamaV2 can produce, near unquantized performance.
6_5	6.5	8.0	Very similar to 8.0, good tradeoff of size vs performance, recommended.
5_5	5.5	8.0	Slightly lower quality vs 6.5, but usable on 8GB cards.

Introduction

Nxcode-CQ-7B-orpo is an Monolithic Preference Optimization without Reference Model fine-tune of Qwen/CodeQwen1.5-7B on 100k samples of high-quality ranking data.

Evalplus

EvalPlus	pass@1
HumanEval	86.6
HumanEval+	83.5
MBPP(v0.2.0)	82.3
MBPP+(v0.2.0)	70.4

We use a simple template to generate the solution for evalplus:

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

Evalplus Leaderboard

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

Bigcode Leaderboard

Bigcode Leaderboard

09/05/2024

Top 1 average score.

Top 2 winrate.

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 upgrade the transformers if you receive an error when loading the tokenizer

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).