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---
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](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.
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.
# 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](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT)
## 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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ibivibiv__alpaca-dragon-72b-v1)
| 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|
|