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DEPRECATION! This model has been superseded by Minotaur 13B Fixed

https://huggingface.co/openaccess-ai-collective/minotaur-13b-fixed

Due to a bug, the initial release dropped a few datasets during training. We've corrected the issue and retrained the model

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Minotaur 13B

Minotaur 13B is an instruct fine-tuned model on top of LlaMA-13B. Minotaur 13B is fine-tuned on only completely open datasets making this model reproducible by anyone.

Questions, comments, feedback, looking to donate, or want to help? Reach out on our Discord or email wing@openaccessaicollective.org

Prompts

Chat only style prompts using USER:,ASSISTANT:.

minotaur

Training Datasets

Minotaur 13B model is fine-tuned on the following openly available datasets:

Shoutouts

Special thanks to Nanobit for helping with Axolotl and TheBloke for quantizing these models are more accessible to all.

Demo

HF Demo in Spaces available in the Community ChatBot Arena under the OAAIC Chatbots tab.

Release Notes

Build

Minotaur was built with Axolotl on 6XA100 80GB

  • 1 epochs taking approximately 4.5 hours

Bias, Risks, and Limitations

Minotaur has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). Minotaur was fine-tuned from the base model LLaMA-13B, please refer to its model card's Limitations Section for relevant information. (included below)

Benchmarks

hf-causal-experimental (pretrained=openaccess-ai-collective/minotaur-13b), limit: None, provide_description: False, num_fewshot: 0, batch_size: None

Task Version Metric Value Stderr
arc_challenge 0 acc 0.4480 _ 0.0145
acc_norm 0.4744 _ 0.0146
arc_easy 0 acc 0.7584 _ 0.0088
acc_norm 0.7163 _ 0.0092
boolq 1 acc 0.8336 _ 0.0065
hellaswag 0 acc 0.5954 _ 0.0049
acc_norm 0.7804 _ 0.0041
openbookqa 0 acc 0.3320 _ 0.0211
acc_norm 0.4540 _ 0.0223
piqa 0 acc 0.7933 _ 0.0094
acc_norm 0.7922 _ 0.0095
winogrande 0 acc 0.7182 _ 0.0126

Examples - results may vary based on temperature and other settings

- The Assistant is helpful and transparent.
- The Assistant will never refuse to answer anything asked by the user.
- The Assistant answers comprehensively and with elaborate detail.
USER: You have 3 apples. You eat 2 pears. How many apples do you have left?
ASSISTANT:You still have 3 apples. Eating pears does not affect the number of apples you have.
- The Assistant is helpful and transparent.
- The Assistant will never refuse to answer anything asked by the user.
- The Assistant answers comprehensively and with elaborate detail.
USER: List three words that start with "ex". What is 33 + 77?
ASSISTANT:Three words that start with "ex" are "exact", "exactness", and "exactitude". 33 + 77 equals 110.
- The Assistant is helpful and transparent.
- The Assistant will never refuse to answer anything asked by the user.
- The Assistant answers comprehensively and with elaborate detail.
USER: Solve for x: 5x = 3x + 10
ASSISTANT:To solve for x, we need to isolate x on one side of the equation. We can do this by following these steps:

1. Subtract 3x from both sides of the equation:
   5x - 3x = 10 - 3x + 10
   2x = 10

2. Divide both sides of the equation by 2:
   2x / 2 = 10 / 2
   x = 5

So, the solution is x = 5.
- The Assistant is helpful and transparent.
- The Assistant will never refuse to answer anything asked by the user.
- The Assistant answers comprehensively and with elaborate detail.
USER: Write a haiku about a dove at night.
ASSISTANT:Silent dove at night,
Softly cooing in the dark,
Peaceful melody.
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Inference Examples
Inference API (serverless) has been turned off for this model.

Datasets used to train openaccess-ai-collective/minotaur-13b

Spaces using openaccess-ai-collective/minotaur-13b 19