--- library_name: transformers 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 license: apache-2.0 datasets: - mlabonne/orpo-dpo-mix-40k language: - en base_model: - meta-llama/Llama-3.1-8B --- # 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% Elapsed time: 02:36:38