NTQ AI LM
Collection
A collection of finely tuned Language Models (LLMs) across diverse datasets.
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3 items
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Updated
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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 | 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}"
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 |
09/05/2024
Top 1 average score.
Top 2 winrate.
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)
For personal communication related to this project, please contact Nha Nguyen Van ([email protected]).