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alpaca-dragon-72b-v1 - GGUF
- Model creator: https://huggingface.co/ibivibiv/
- Original model: https://huggingface.co/ibivibiv/alpaca-dragon-72b-v1/
Original model description:
language: - en license: other library_name: transformers model-index: - name: alpaca-dragon-72b-v1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 73.89 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/alpaca-dragon-72b-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.16 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/alpaca-dragon-72b-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 77.4 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/alpaca-dragon-72b-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 72.69 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/alpaca-dragon-72b-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 86.03 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/alpaca-dragon-72b-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 77.63 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/alpaca-dragon-72b-v1 name: Open LLM Leaderboard
Model Card for Alpaca Dragon 72B V1
Fine tune of 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 for sharing their work.
I used the same dataset in training one of my owl series Strix Rufipes 70B, which has worked well for planning out development tasks and other technical work.
LICENSE
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. License Link
How to Get Started with the Model
Use the code below to get started with the model.
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("ibivibiv/alpaca-dragon-72b-v1")
model = AutoModelForCausalLM.from_pretrained("ibivibiv/alpaca-dragon-72b-v1")
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)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
Evaluation
| Test Name | Accuracy (%) |
|---|---|
| All | 77.31 |
| arc:challenge | 70.82 |
| hellaswag | 69.84 |
| hendrycksTest-abstract_algebra | 42.00 |
| hendrycksTest-anatomy | 71.85 |
| hendrycksTest-astronomy | 86.84 |
| hendrycksTest-business_ethics | 82.00 |
| hendrycksTest-clinical_knowledge | 84.53 |
| hendrycksTest-college_biology | 93.06 |
| hendrycksTest-college_chemistry | 54.00 |
| hendrycksTest-college_computer_science | 65.00 |
| hendrycksTest-college_mathematics | 52.00 |
| hendrycksTest-college_medicine | 75.14 |
| hendrycksTest-college_physics | 55.88 |
| hendrycksTest-computer_security | 82.00 |
| hendrycksTest-conceptual_physics | 80.43 |
| hendrycksTest-econometrics | 60.53 |
| hendrycksTest-electrical_engineering | 79.31 |
| hendrycksTest-elementary_mathematics | 70.37 |
| hendrycksTest-formal_logic | 58.73 |
| hendrycksTest-global_facts | 54.00 |
| hendrycksTest-high_school_biology | 88.39 |
| hendrycksTest-high_school_chemistry | 66.01 |
| hendrycksTest-high_school_computer_science | 82.00 |
| hendrycksTest-high_school_european_history | 84.24 |
| hendrycksTest-high_school_geography | 94.44 |
| hendrycksTest-high_school_government_and_politics | 98.96 |
| hendrycksTest-high_school_macroeconomics | 82.05 |
| hendrycksTest-high_school_mathematics | 45.93 |
| hendrycksTest-high_school_microeconomics | 86.13 |
| hendrycksTest-high_school_physics | 54.97 |
| hendrycksTest-high_school_psychology | 92.84 |
| hendrycksTest-high_school_statistics | 68.98 |
| hendrycksTest-high_school_us_history | 91.67 |
| hendrycksTest-high_school_world_history | 89.87 |
| hendrycksTest-human_aging | 78.03 |
| hendrycksTest-human_sexuality | 89.31 |
| hendrycksTest-international_law | 90.91 |
| hendrycksTest-jurisprudence | 87.96 |
| hendrycksTest-logical_fallacies | 84.05 |
| hendrycksTest-machine_learning | 58.93 |
| hendrycksTest-management | 87.38 |
| hendrycksTest-marketing | 95.30 |
| hendrycksTest-medical_genetics | 86.00 |
| hendrycksTest-miscellaneous | 92.21 |
| hendrycksTest-moral_disputes | 83.53 |
| hendrycksTest-moral_scenarios | 69.72 |
| hendrycksTest-nutrition | 85.62 |
| hendrycksTest-philosophy | 83.60 |
| hendrycksTest-prehistory | 87.04 |
| hendrycksTest-professional_accounting | 65.96 |
| hendrycksTest-professional_law | 60.69 |
| hendrycksTest-professional_medicine | 82.72 |
| hendrycksTest-professional_psychology | 81.86 |
| hendrycksTest-public_relations | 75.45 |
| hendrycksTest-security_studies | 82.04 |
| hendrycksTest-sociology | 88.56 |
| hendrycksTest-us_foreign_policy | 94.00 |
| hendrycksTest-virology | 57.23 |
| hendrycksTest-world_religions | 89.47 |
| truthfulqa:mc | 72.6 |
| winogrande | 86.03 |
| gsm8k | 77.63 |
Environmental Impact
- Hardware Type: [A100's..... more than I wanted to use since its all on my $$$]
- Hours used: [8]
- Cloud Provider: [runpod.io]
- Compute Region: [US]
- Carbon Emitted: [?]
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 79.30 |
| AI2 Reasoning Challenge (25-Shot) | 73.89 |
| HellaSwag (10-Shot) | 88.16 |
| MMLU (5-Shot) | 77.40 |
| TruthfulQA (0-shot) | 72.69 |
| Winogrande (5-shot) | 86.03 |
| GSM8k (5-shot) | 77.63 |
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