---
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](https://huggingface.co/datasets/LimYeri/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.

```python
# 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](https://huggingface.co/spaces/open-llm-leaderboard/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](https://github.com/LimYeri/CodeMind-Extended).