RichardErkhov/Arjun-G-Ravi_-_GPT2-Alpaca-gguf

Quantization made by Richard Erkhov.

Github

Discord

Request more models

GPT2-Alpaca - GGUF

• Model creator: https://huggingface.co/Arjun-G-Ravi/
• Original model: https://huggingface.co/Arjun-G-Ravi/GPT2-Alpaca/

Name	Quant method	Size
GPT2-Alpaca.Q2_K.gguf	Q2_K	0.08GB
GPT2-Alpaca.IQ3_XS.gguf	IQ3_XS	0.08GB
GPT2-Alpaca.IQ3_S.gguf	IQ3_S	0.08GB
GPT2-Alpaca.Q3_K_S.gguf	Q3_K_S	0.08GB
GPT2-Alpaca.IQ3_M.gguf	IQ3_M	0.09GB
GPT2-Alpaca.Q3_K.gguf	Q3_K	0.09GB
GPT2-Alpaca.Q3_K_M.gguf	Q3_K_M	0.09GB
GPT2-Alpaca.Q3_K_L.gguf	Q3_K_L	0.1GB
GPT2-Alpaca.IQ4_XS.gguf	IQ4_XS	0.1GB
GPT2-Alpaca.Q4_0.gguf	Q4_0	0.1GB
GPT2-Alpaca.IQ4_NL.gguf	IQ4_NL	0.1GB
GPT2-Alpaca.Q4_K_S.gguf	Q4_K_S	0.1GB
GPT2-Alpaca.Q4_K.gguf	Q4_K	0.11GB
GPT2-Alpaca.Q4_K_M.gguf	Q4_K_M	0.11GB
GPT2-Alpaca.Q4_1.gguf	Q4_1	0.11GB
GPT2-Alpaca.Q5_0.gguf	Q5_0	0.11GB
GPT2-Alpaca.Q5_K_S.gguf	Q5_K_S	0.11GB
GPT2-Alpaca.Q5_K.gguf	Q5_K	0.12GB
GPT2-Alpaca.Q5_K_M.gguf	Q5_K_M	0.12GB
GPT2-Alpaca.Q5_1.gguf	Q5_1	0.12GB
GPT2-Alpaca.Q6_K.gguf	Q6_K	0.13GB
GPT2-Alpaca.Q8_0.gguf	Q8_0	0.17GB

Original model description:

license: mit datasets: - tatsu-lab/alpaca language: - en metrics: - accuracy library_name: transformers pipeline_tag: text-generation widget: - text: "Below is an instruction that describes a task. Write a response that appropriately completes the request.\nInstruction: What is artificial intelligence?\nResponse: " example_title: "Knowledge-AI" - text: "Below is an instruction that describes a task. Write a response that appropriately completes the request.\nInstruction: Write a poem on cows\nResponse: " example_title: "Poem Generation" - text: "Below is an instruction that describes a task. Write a response that appropriately completes the request.\nInstruction: What is the meaning of life?\nResponse: " example_title: "Philosophy" - text: "Below is an instruction that describes a task. Write a response that appropriately completes the request.\nInstruction: Why is the sky blue?\nResponse: " example_title: "Knowledge-sky" - text: "Below is an instruction that describes a task. Write a response that appropriately completes the request.\nInstruction: Define the term torque.\nResponse: " example_title: "Knowledge-torque" inference: parameters: temperature: 0.7 top_k: 50 top_p: .9 max_length: 200

Model Card for Model ID

This is the fine tuned version of OpenAI's GPT-2 with Alpaca dataset. This has been fine tuned for 20 epochs at batch size of 14.

Model Description

license: mit
dataset: tatsu-lab/alpaca
language: en
library_name: transformers
pipeline_tag: text-generation
base_model: gpt2

Examples

1. Give me tips to be healthy

Here are some tips to be healthy: 
- Eat a balanced diet that includes plenty of fruits, vegetables, whole grains, lean proteins, and healthy fats.
- Exercise regularly to maintain a healthy weight and reduce the risk of developing any health issues.
- Get enough sleep and stay hydrated by drinking plenty of water. 
- Get enough fiber and healthy fats to support your overall health and wellbeing. 
- Practice mindful breathing and avoid activities that can increase stress. 
- Get enough sleep and maintain a healthy weight.

2. Write a poem on cows

Cows are beloved creatures,
Families who love to cuddle,
In their humble homes they rest.

Cows are the bread and butter of life,
They can be found in the fields and gardens,
Birds and insects to feed us.

Cows are the sweet smell of life,
A source of joy and happiness,
Providing shelter and a home to rest.

Cows are the faithful guardians of the land,
They will protect our spirits from strife,
In their care they will ensure that our food and shelter will be nourished.

Cows are the beloved petals of life,
They are loyal companions and faithful stewards,
Their presence will always be seen.

3. Which is the fastest animal in the world

The cheetah is the fastest animal in the world.

Bias, Risks, and Limitations

This model has all the biases, risks and limitations of base gpt2 model.

Recommendation

The ideal format for inference is:

Below is an instruction that describes a task. Write a response that appropriately completes the request.
Instruction:{question}
Response: 

Replace {question} with the question of your choice.

The parameters I used for inference are:

top_k=20
top_p=.9
temperature = .7

References used

1. GPT2 @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} }

2. tatsu-lab/alpaca @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, }