README.md

---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
datasets:
- LimYeri/LeetCode_Python_Solutions
pipeline_tag: text-generation
model-index:
- name: CodeMind-Llama3-8B-unsloth_v2-merged
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 69.46
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LimYeri/CodeMind-Llama3-8B-unsloth_v2-merged
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 26.66
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LimYeri/CodeMind-Llama3-8B-unsloth_v2-merged
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 5.74
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LimYeri/CodeMind-Llama3-8B-unsloth_v2-merged
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 2.01
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LimYeri/CodeMind-Llama3-8B-unsloth_v2-merged
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 2.22
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LimYeri/CodeMind-Llama3-8B-unsloth_v2-merged
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 27.84
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LimYeri/CodeMind-Llama3-8B-unsloth_v2-merged
      name: Open LLM Leaderboard
---

# Uploaded  model

- **Developed by:** LimYeri
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

### Training Setting
The following hyperparameters are used during SFT:
- num_epochs: 3
- learning_rate: 1e-4
- max_seq_length: None
- optimizer: adamw_8bit
- lr_scheduler_type: linear
- warmup_ratio: 0.03
- weight_decay: 0.01
- lora_rank: 16
- lora_alpha: 16
- lora_dropout: 0
- gradient_checkpointing: true
- fp16: not is_bfloat16_supported()
- bf16: is_bfloat16_supported()
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_LimYeri__CodeMind-Llama3-8B-unsloth_v2-merged)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |22.32|
|IFEval (0-Shot)    |69.46|
|BBH (3-Shot)       |26.66|
|MATH Lvl 5 (4-Shot)| 5.74|
|GPQA (0-shot)      | 2.01|
|MuSR (0-shot)      | 2.22|
|MMLU-PRO (5-shot)  |27.84|