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# Minotaur 7B

Minotaur 7B is an instruct fine-tuned model on top of LlaMA-13B. Minotaur 7B 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](https://discord.gg/PugNNHAF5r) or email [wing@openaccessaicollective.org](mailto:wing@openaccessaicollective.org)

# Prompts
Chat only style prompts using `USER:`,`ASSISTANT:`.

<img src="https://huggingface.co/openaccess-ai-collective/minotaur-7b/resolve/main/minotaur.png" alt="minotaur" width="600" height="500"/>

# Training Datasets

Minotaur 7B model is fine-tuned on the following openly available datasets:

- [WizardLM](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered)
- [subset of QingyiSi/Alpaca-CoT for roleplay and CoT](https://huggingface.co/QingyiSi/Alpaca-CoT)
- [GPTeacher-General-Instruct](https://huggingface.co/datasets/teknium/GPTeacher-General-Instruct)
- [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - instruct for concise responses
- [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - instruct augmented tl;dr summarization
- [camel-ai/math](https://huggingface.co/datasets/camel-ai/math)
- [camel-ai/physics](https://huggingface.co/datasets/camel-ai/physics)
- [camel-ai/chemistry](https://huggingface.co/datasets/camel-ai/chemistry)
- [camel-ai/biology](https://huggingface.co/datasets/camel-ai/biology)
- [winglian/evals](https://huggingface.co/datasets/winglian/evals) - instruct augmented datasets
  - custom sysnthetic datasets around misconceptions, in-context qa, jokes, N-tasks problems, and context-insensitivity
  - ARC-Easy & ARC-Challenge - instruct augmented for detailed responses, derived from the `train` split
  - [hellaswag](https://huggingface.co/datasets/hellaswag) - 30K+ rows of instruct augmented for detailed explanations w 30K+ rows, derived from the `train` split
  - [riddle_sense](https://huggingface.co/datasets/riddle_sense) - instruct augmented, derived from the `train` split
  - [gsm8k](https://huggingface.co/datasets/gsm8k) - instruct augmented, derived from the `train` split
  - prose generation

# 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](https://huggingface.co/spaces/openaccess-ai-collective/rlhf-arena) under the OAAIC Chatbots tab.

## Release Notes

- https://wandb.ai/wing-lian/minotaur-13b/runs/ucgd0x6n

## Build

Minotaur was built with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) on 8x RTXA6000
 - 1 epochs taking approximately 1.5 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-7b), limit: None, provide_description: False, num_fewshot: 0, batch_size: None

|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.4078|_  |0.0144|
|             |       |acc_norm|0.4292|_  |0.0145|
|arc_easy     |      0|acc     |0.7424|_  |0.0090|
|             |       |acc_norm|0.6877|_  |0.0095|
|boolq        |      1|acc     |0.7679|_  |0.0074|
|hellaswag    |      0|acc     |0.5636|_  |0.0049|
|             |       |acc_norm|0.7377|_  |0.0044|
|openbookqa   |      0|acc     |0.3540|_  |0.0214|
|             |       |acc_norm|0.4420|_  |0.0222|
|piqa         |      0|acc     |0.7878|_  |0.0095|
|             |       |acc_norm|0.7851|_  |0.0096|
|winogrande   |      0|acc     |0.7072|_  |0.0128|


## Examples - results may vary based on temperature and other settings


```
The following is a chat between a USER and a friendly and helpful ASSISTANT.                                                                                                                            
USER: You have 5 apples. You eat 3 pears. How many apples do you have left?                                                                                                                                 
ASSISTANT:You still have 5 apples because eating pears does not affect the number of apples you have.
```

```
The following is a chat between a USER and a friendly and helpful ASSISTANT.
USER: List three words that start with "ex". What is 33 + 77?
ASSISTANT:Three words starting with the letter 'e' are excited, eatable, and expensive. 33+77 equals 106.
```

```
The following is a chat between a USER and a friendly and helpful ASSISTANT.
USER: Solve for x: 5x = 3x + 10
ASSISTANT:To solve the equation 5x = 3x + 10, we can follow these steps:

Step 1: Add both sides of the equation to get a common denominator.
(1/5)x = 1 (common)

Subtract the term with "x" on both sides.
-2/5 x = 10

Step 2: To solve the resulting equation, isolate the variable "x".
First, multiply all terms by 5 to eliminate the fractions.
5x = 15

Now, divide the entire right side by 5.
x = 15/5

Finally, simplify to get the solution in its simplest form.
x = 1

So, the solution is x = 1.
```
Yup, 7B still sucks at math

{"license": "apache-2.0", "tags": ["OpenAccess AI Collective", "MPT", "axolotl"], "datasets": ["ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered", "QingyiSi/Alpaca-CoT", "teknium/GPTeacher-General-Instruct", "metaeval/ScienceQA_text_only", "hellaswag", "openai/summarize_from_feedback", "riddle_sense", "gsm8k", "camel-ai/math", "camel-ai/biology", "camel-ai/physics", "camel-ai/chemistry", "winglian/evals"], "inference": false}
transformers pytorch llama text-generation OpenAccess AI Collective MPT axolotl dataset:ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered dataset:QingyiSi/Alpaca-CoT dataset:teknium/GPTeacher-General-Instruct dataset:metaeval/ScienceQA_text_only dataset:hellaswag dataset:openai/summarize_from_feedback dataset:riddle_sense dataset:gsm8k dataset:camel-ai/math dataset:camel-ai/biology dataset:camel-ai/physics dataset:camel-ai/chemistry dataset:winglian/evals license:apache-2.0 autotrain_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #llama #text-generation #OpenAccess AI Collective #MPT #axolotl #dataset-ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered #dataset-QingyiSi/Alpaca-CoT #dataset-teknium/GPTeacher-General-Instruct #dataset-metaeval/ScienceQA_text_only #dataset-hellaswag #dataset-openai/summarize_from_feedback #dataset-riddle_sense #dataset-gsm8k #dataset-camel-ai/math #dataset-camel-ai/biology #dataset-camel-ai/physics #dataset-camel-ai/chemistry #dataset-winglian/evals #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us 

<img src="URL alt="Built with Axolotl" width="200" height="32"/>
Donate to OpenAccess AI Collective to help us keep building great tools and models!


Minotaur 7B
===========


Minotaur 7B is an instruct fine-tuned model on top of LlaMA-13B. Minotaur 7B 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@URL


Prompts
=======


Chat only style prompts using 'USER:','ASSISTANT:'.


<img src="URL alt="minotaur" width="600" height="500"/>


Training Datasets
=================


Minotaur 7B model is fine-tuned on the following openly available datasets:


* WizardLM
* subset of QingyiSi/Alpaca-CoT for roleplay and CoT
* GPTeacher-General-Instruct
* metaeval/ScienceQA\_text\_only - instruct for concise responses
* openai/summarize\_from\_feedback - instruct augmented tl;dr summarization
* camel-ai/math
* camel-ai/physics
* camel-ai/chemistry
* camel-ai/biology
* winglian/evals - instruct augmented datasets
	+ custom sysnthetic datasets around misconceptions, in-context qa, jokes, N-tasks problems, and context-insensitivity
	+ ARC-Easy & ARC-Challenge - instruct augmented for detailed responses, derived from the 'train' split
	+ hellaswag - 30K+ rows of instruct augmented for detailed explanations w 30K+ rows, derived from the 'train' split
	+ riddle\_sense - instruct augmented, derived from the 'train' split
	+ gsm8k - instruct augmented, derived from the 'train' split
	+ prose generation


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
-------------


* URL


Build
-----


Minotaur was built with Axolotl on 8x RTXA6000


* 1 epochs taking approximately 1.5 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-7b), limit: None, provide\_description: False, num\_fewshot: 0, batch\_size: None



Examples - results may vary based on temperature and other settings
-------------------------------------------------------------------


Yup, 7B still sucks at math



TAGS
#transformers #pytorch #llama #text-generation #OpenAccess AI Collective #MPT #axolotl #dataset-ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered #dataset-QingyiSi/Alpaca-CoT #dataset-teknium/GPTeacher-General-Instruct #dataset-metaeval/ScienceQA_text_only #dataset-hellaswag #dataset-openai/summarize_from_feedback #dataset-riddle_sense #dataset-gsm8k #dataset-camel-ai/math #dataset-camel-ai/biology #dataset-camel-ai/physics #dataset-camel-ai/chemistry #dataset-winglian/evals #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us 
