---
license: llama3.1
datasets:
- agentlans/crash-course
base_model:
- agentlans/Llama3.1-SuperDeepFuse
model-index:
- name: Llama3.1-SuperDeepFuse-CrashCourse12K
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: wis-k/instruction-following-eval
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 71.87
      name: averaged accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=agentlans%2FLlama3.1-SuperDeepFuse-CrashCourse12K
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: SaylorTwift/bbh
      split: test
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 31.83
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=agentlans%2FLlama3.1-SuperDeepFuse-CrashCourse12K
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: lighteval/MATH-Hard
      split: test
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 17.67
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=agentlans%2FLlama3.1-SuperDeepFuse-CrashCourse12K
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 8.39
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=agentlans%2FLlama3.1-SuperDeepFuse-CrashCourse12K
      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: 8.6
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=agentlans%2FLlama3.1-SuperDeepFuse-CrashCourse12K
      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: 29.24
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=agentlans%2FLlama3.1-SuperDeepFuse-CrashCourse12K
      name: Open LLM Leaderboard
---
# Llama3.1-SuperDeepFuse-CrashCourse12K

Llama3.1-SuperDeepFuse-CrashCourse12K is an 8B parameter language model based on [Llama3.1-SuperDeepFuse](https://huggingface.co/agentlans/Llama3.1-SuperDeepFuse)
and further fine-tuned on [agentlans/crash-course](https://huggingface.co/datasets/agentlans/crash-course). 

## Model Details

- **Base Model**: Llama3.1-SuperDeepFuse (8B parameters)
- **Fine-tuning Dataset**: 12 000 samples from agentlans/crash-course (containing samples from 10 high-quality instruct datasets)
- **Model Type**: Instruction-tuned language model
- **Language(s)**: Multilingual
- **License**: Follows standard Llama 3.1 usage terms

## Training Procedure

### Fine-tuning

- **Method**: LoRA (Low-Rank Adaptation)
- **Optimizer**: AdamW
- **Learning Rate**: 5e-5
- **Batch Size**: 2 per device
- **Gradient Accumulation Steps**: 8
- **Training Epochs**: 1
- **Max Sequence Length**: 2048
- **LoRA Configuration**:
  - Rank: 8
  - Alpha: 16
  - Dropout: 0.5
  - Target: all layers
- **Quantization**: 4-bit (bitsandbytes)
- **Precision**: BF16
- **Other Techniques**: NEFTune (noise alpha: 5), RS-LoRA

## Performance and Limitations

This model potentially offers:

- Enhanced multi-task reasoning
- Improved performance in mathematics and coding tasks
- Better instruction-following abilities

However:

- Performance may be limited compared to larger model variants
- Can produce misleading or incorrect outputs
- Outputs should be independently verified for critical applications

## Additional Information

- For the original model, see [agentlans/Llama3.1-SuperDeepFuse](https://huggingface.co/agentlans/Llama3.1-SuperDeepFuse)
- For the base Llama 3.1 model, including training data and model architecture, refer to the original [Llama 3.1](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) model card.
# [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/agentlans__Llama3.1-SuperDeepFuse-CrashCourse12K-details)!
Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=agentlans%2FLlama3.1-SuperDeepFuse-CrashCourse12K&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!

|      Metric       |Value (%)|
|-------------------|--------:|
|**Average**        |    27.93|
|IFEval (0-Shot)    |    71.87|
|BBH (3-Shot)       |    31.83|
|MATH Lvl 5 (4-Shot)|    17.67|
|GPQA (0-shot)      |     8.39|
|MuSR (0-shot)      |     8.60|
|MMLU-PRO (5-shot)  |    29.24|