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--- |
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language: |
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- en |
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license: other |
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library_name: transformers |
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model-index: |
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- name: alpaca-dragon-72b-v1 |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 73.89 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/alpaca-dragon-72b-v1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 88.16 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/alpaca-dragon-72b-v1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 77.4 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/alpaca-dragon-72b-v1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 72.69 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/alpaca-dragon-72b-v1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 86.03 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/alpaca-dragon-72b-v1 |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 77.63 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/alpaca-dragon-72b-v1 |
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name: Open LLM Leaderboard |
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--- |
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# Model Card for Alpaca Dragon 72B V1 |
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Fine tune of [Smaug 72b v0.1](https://huggingface.co/abacusai/Smaug-72B-v0.1) using an alpaca data set I have handy. The data is of planning and reasoning, which I use to help allow a model to break down a set of asks into a logical plan. For some odd reason it bumps the mmlu and winogrande? I would have expected the ARC to go up over those two, but this is often more of an artform than a science at times. All thanks to [Abacus.AI](https://huggingface.co/abacusai) for sharing their work. |
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I used the same dataset in training one of my owl series [Strix Rufipes 70B](https://huggingface.co/ibivibiv/strix-rufipes-70b), which has worked well for planning out development tasks and other technical work. |
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# LICENSE |
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Note the license points back to SMAUG base license as it is a fine tune of their model only. Respect and abide by their conditions. Again, many thanks to Abacus for making their work open and use that as inspiration to keep your work open and respect their license agreements. |
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[License Link](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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``` |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("ibivibiv/alpaca-dragon-72b-v1") |
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model = AutoModelForCausalLM.from_pretrained("ibivibiv/alpaca-dragon-72b-v1") |
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inputs = tokenizer("### Instruction: Create a plan for developing the game of snake in python using pygame.\n### Response:\n", return_tensors="pt", return_attention_mask=False) |
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outputs = model.generate(**inputs, max_length=200) |
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text = tokenizer.batch_decode(outputs)[0] |
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print(text) |
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``` |
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## Evaluation |
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| Test Name | Accuracy (%) | |
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|---------------------------------|--------------| |
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| All | 77.31 | |
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| arc:challenge | 70.82 | |
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| hellaswag | 69.84 | |
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| hendrycksTest-abstract_algebra | 42.00 | |
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| hendrycksTest-anatomy | 71.85 | |
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| hendrycksTest-astronomy | 86.84 | |
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| hendrycksTest-business_ethics | 82.00 | |
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| hendrycksTest-clinical_knowledge| 84.53 | |
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| hendrycksTest-college_biology | 93.06 | |
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| hendrycksTest-college_chemistry | 54.00 | |
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| hendrycksTest-college_computer_science | 65.00 | |
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| hendrycksTest-college_mathematics | 52.00 | |
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| hendrycksTest-college_medicine | 75.14 | |
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| hendrycksTest-college_physics | 55.88 | |
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| hendrycksTest-computer_security | 82.00 | |
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| hendrycksTest-conceptual_physics| 80.43 | |
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| hendrycksTest-econometrics | 60.53 | |
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| hendrycksTest-electrical_engineering | 79.31 | |
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| hendrycksTest-elementary_mathematics | 70.37 | |
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| hendrycksTest-formal_logic | 58.73 | |
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| hendrycksTest-global_facts | 54.00 | |
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| hendrycksTest-high_school_biology | 88.39 | |
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| hendrycksTest-high_school_chemistry | 66.01 | |
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| hendrycksTest-high_school_computer_science | 82.00 | |
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| hendrycksTest-high_school_european_history | 84.24 | |
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| hendrycksTest-high_school_geography | 94.44 | |
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| hendrycksTest-high_school_government_and_politics | 98.96 | |
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| hendrycksTest-high_school_macroeconomics | 82.05 | |
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| hendrycksTest-high_school_mathematics | 45.93 | |
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| hendrycksTest-high_school_microeconomics | 86.13 | |
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| hendrycksTest-high_school_physics | 54.97 | |
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| hendrycksTest-high_school_psychology | 92.84 | |
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| hendrycksTest-high_school_statistics | 68.98 | |
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| hendrycksTest-high_school_us_history | 91.67 | |
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| hendrycksTest-high_school_world_history | 89.87 | |
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| hendrycksTest-human_aging | 78.03 | |
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| hendrycksTest-human_sexuality | 89.31 | |
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| hendrycksTest-international_law | 90.91 | |
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| hendrycksTest-jurisprudence | 87.96 | |
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| hendrycksTest-logical_fallacies | 84.05 | |
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| hendrycksTest-machine_learning | 58.93 | |
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| hendrycksTest-management | 87.38 | |
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| hendrycksTest-marketing | 95.30 | |
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| hendrycksTest-medical_genetics | 86.00 | |
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| hendrycksTest-miscellaneous | 92.21 | |
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| hendrycksTest-moral_disputes | 83.53 | |
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| hendrycksTest-moral_scenarios | 69.72 | |
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| hendrycksTest-nutrition | 85.62 | |
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| hendrycksTest-philosophy | 83.60 | |
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| hendrycksTest-prehistory | 87.04 | |
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| hendrycksTest-professional_accounting | 65.96 | |
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| hendrycksTest-professional_law | 60.69 | |
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| hendrycksTest-professional_medicine | 82.72 | |
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| hendrycksTest-professional_psychology | 81.86 | |
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| hendrycksTest-public_relations | 75.45 | |
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| hendrycksTest-security_studies | 82.04 | |
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| hendrycksTest-sociology | 88.56 | |
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| hendrycksTest-us_foreign_policy | 94.00 | |
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| hendrycksTest-virology | 57.23 | |
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| hendrycksTest-world_religions | 89.47 | |
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| truthfulqa:mc | 72.6 | |
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| winogrande | 86.03 | |
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| gsm8k | 77.63 | |
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## Environmental Impact |
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- **Hardware Type:** [A100's..... more than I wanted to use since its all on my $$$] |
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- **Hours used:** [8] |
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- **Cloud Provider:** [runpod.io] |
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- **Compute Region:** [US] |
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- **Carbon Emitted:** [?] |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ibivibiv__alpaca-dragon-72b-v1) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |79.30| |
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|AI2 Reasoning Challenge (25-Shot)|73.89| |
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|HellaSwag (10-Shot) |88.16| |
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|MMLU (5-Shot) |77.40| |
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|TruthfulQA (0-shot) |72.69| |
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|Winogrande (5-shot) |86.03| |
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|GSM8k (5-shot) |77.63| |
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