transformers safetensors llama text-generation orpo llama3-8B Supervised_Training conversational en dataset:mlabonne/orpo-dpo-mix-40k base_model:meta-llama/Llama-3.1-8B base_model:finetune:meta-llama/Llama-3.1-8B license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model:
- meta-llama/Llama-3.1-8B
datasets:
- mlabonne/orpo-dpo-mix-40k
language:
- en
library_name: transformers
license: apache-2.0
tags:
- orpo
- llama3-8B
- Supervised_Training
model-index:
- name: LLAMA_Harsha_8_B_ORDP_10k
  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: 34.64
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=asharsha30/LLAMA_Harsha_8_B_ORDP_10k
      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: 25.73
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=asharsha30/LLAMA_Harsha_8_B_ORDP_10k
      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: 5.21
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=asharsha30/LLAMA_Harsha_8_B_ORDP_10k
      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: 3.13
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=asharsha30/LLAMA_Harsha_8_B_ORDP_10k
      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: 7.07
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=asharsha30/LLAMA_Harsha_8_B_ORDP_10k
      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: 20.11
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=asharsha30/LLAMA_Harsha_8_B_ORDP_10k
      name: Open LLM Leaderboard
---

# asharsha30/LLAMA_Harsha_8_B_ORDP_10k

This model is the fine tune of NousResearch/Meta-Llama-3-8B using the 12,000 steps of [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k).



## 💻 Usage

```python
# Use a pipeline as a high-level helper
from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="asharsha30/LLAMA_Harsha_8_B_ORDP_10k")
pipe(messages)
```
## 📈Training And Evaluation Report:

Reports from Wandb

https://wandb.ai/asharshavardhana96-texas-a-m-university/huggingface/runs/gky6j4vn?nw=nwuserasharshavardhana96

## Acknowledgment:

Huge thanks to Maxime Labonne for his brilliant blog post covering about the techniques related to finetuning the llama models using SFT and ORPO

## Evaluated Using: 

The model is evaluated using the https://github.com/mlabonne/llm-autoeval and the results are summarized from the generated gist https://gist.github.com/asharsha30-1996/4162fc98d9669aab3080645c54905bd0

## Accuracy measure on Neous Benchmarks:

|                                         Model                                          |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|----------------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[LLAMA_Harsha_8_B_ORDP_10k](https://huggingface.co/asharsha30/LLAMA_Harsha_8_B_ORDP_10k)|  35.54|  71.15|     55.39|   37.96|  50.01|

### AGIEval
|             Task             |Version| Metric |Value|   |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |26.77|±  |  2.78|
|                              |       |acc_norm|27.17|±  |  2.80|
|agieval_logiqa_en             |      0|acc     |31.34|±  |  1.82|
|                              |       |acc_norm|33.03|±  |  1.84|
|agieval_lsat_ar               |      0|acc     |18.70|±  |  2.58|
|                              |       |acc_norm|19.57|±  |  2.62|
|agieval_lsat_lr               |      0|acc     |42.94|±  |  2.19|
|                              |       |acc_norm|35.10|±  |  2.12|
|agieval_lsat_rc               |      0|acc     |52.42|±  |  3.05|
|                              |       |acc_norm|43.87|±  |  3.03|
|agieval_sat_en                |      0|acc     |65.53|±  |  3.32|
|                              |       |acc_norm|54.37|±  |  3.48|
|agieval_sat_en_without_passage|      0|acc     |41.75|±  |  3.44|
|                              |       |acc_norm|33.98|±  |  3.31|
|agieval_sat_math              |      0|acc     |42.27|±  |  3.34|
|                              |       |acc_norm|37.27|±  |  3.27|

Average: 35.54%

### GPT4All
|    Task     |Version| Metric |Value|   |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge|      0|acc     |49.91|±  |  1.46|
|             |       |acc_norm|54.10|±  |  1.46|
|arc_easy     |      0|acc     |80.47|±  |  0.81|
|             |       |acc_norm|80.05|±  |  0.82|
|boolq        |      1|acc     |82.08|±  |  0.67|
|hellaswag    |      0|acc     |61.08|±  |  0.49|
|             |       |acc_norm|80.26|±  |  0.40|
|openbookqa   |      0|acc     |34.00|±  |  2.12|
|             |       |acc_norm|45.00|±  |  2.23|
|piqa         |      0|acc     |79.71|±  |  0.94|
|             |       |acc_norm|81.61|±  |  0.90|
|winogrande   |      0|acc     |74.98|±  |  1.22|

Average: 71.15%

### TruthfulQA
|    Task     |Version|Metric|Value|   |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc|      1|mc1   |37.45|±  |  1.69|
|             |       |mc2   |55.39|±  |  1.50|

Average: 55.39%

### Bigbench
|                      Task                      |Version|       Metric        |Value|   |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|57.37|±  |  3.60|
|bigbench_date_understanding                     |      0|multiple_choice_grade|68.02|±  |  2.43|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|31.01|±  |  2.89|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|20.89|±  |  2.15|
|                                                |       |exact_str_match      | 0.00|±  |  0.00|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|28.40|±  |  2.02|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|20.71|±  |  1.53|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|48.67|±  |  2.89|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|31.60|±  |  2.08|
|bigbench_navigate                               |      0|multiple_choice_grade|50.60|±  |  1.58|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|63.25|±  |  1.08|
|bigbench_ruin_names                             |      0|multiple_choice_grade|34.38|±  |  2.25|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|21.84|±  |  1.31|
|bigbench_snarks                                 |      0|multiple_choice_grade|44.20|±  |  3.70|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|50.30|±  |  1.59|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|26.30|±  |  1.39|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|21.36|±  |  1.16|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|15.77|±  |  0.87|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|48.67|±  |  2.89|

Average: 37.96%

Average score: 50.01%

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