metadata

base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
language:
  - en
license: mit
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - trl
  - sft
datasets:
  - LimYeri/LeetCode_Python_Solutions_Data

CodeMind-Llama3.1-8B-unsloth

Codemind Project is a language model developed to assist in solving and learning coding test problems. This model is fine-tuned using posts written by LeetCode users as training data, aiming to provide answers specialized for coding tests.

Model Information

Base Model: meta-llama/Meta-Llama-3.1-8B-Instruct
Fine-Tuning: Fine-tuned using the unsloth library based on the unsloth/Meta-Llama-3.1-8B-Instruct model
Fine-Tuning Process: Conducted with reference to the Llama 3.1 Conversational_notebook

Dataset Used

LeetCode Python Solutions Dataset: LeetCode_Python_Solutions_Data

How to Use the Model

This model is accessible through HuggingFace's model hub and can be integrated into applications using the API. It is designed to generate explanations, code snippets, or guides for coding problems or programming-related questions.

# 자세한 사항은 demo-Llama3.1.ipynb 확인
from unsloth import FastLanguageModel
from unsloth.chat_templates import get_chat_template
from IPython.display import display, Markdown

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "LimYeri/CodeMind-Llama3.1-8B-unsloth", # YOUR MODEL YOU USED FOR TRAINING
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

tokenizer = get_chat_template(
    tokenizer,
    chat_template = "llama-3.1",
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference

messages = [
    {"role": "system", "content": "You are a kind coding test teacher."},
    {"role": "user", "content": "Enter your coding problem or question here."},
]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True, # Must add for generation
    return_tensors = "pt",
).to("cuda")

outputs = model.generate(input_ids = inputs, max_new_tokens = 3000, use_cache = True,
                         temperature = 0.5, min_p = 0.3) # Feel free to adjust the temperature and min_p
text = (tokenizer.batch_decode(outputs))[0].split('assistant<|end_header_id|>\n\n')[1].strip()
display(Markdown(text))

LoRA Configuration

r: 16
lora_alpha: 16
lora_dropout: 0
bias: "none"
use_gradient_checkpointing: "unsloth"

Training Settings

Per Device Train Batch Size: 8
Gradient Accumulation Steps: 2
Warmup Steps: 200
Number of Training Epochs: 5
Learning Rate: 2e-4
fp16: not is_bfloat16_supported()
bf16: is_bfloat16_supported()
Logging Steps: 20
Optimizer: "adamw_8bit"
Weight Decay: 0.01
LR Scheduler Type: "linear"

Evaluation Results Open LLM Leaderboard

Metric	Value
Average	22.17
IFEval	64.9
BBH	24.19
MATH Lvl 5	9.97
GPQA	1.9
MUSR	6.04
MMLU-PRO	26

Fine-Tuning Code

Detailed fine-tuning code and settings can be found in the CodeMind-Extended GitHub repository.