RichardErkhov/BEE-spoke-data_-_smol_llama-101M-GQA-4bits

Quantization made by Richard Erkhov.

Github

Discord

Request more models

smol_llama-101M-GQA - bnb 4bits

• —Model creator: https://huggingface.co/BEE-spoke-data/
• —Original model: https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA/

Original model description: --- license: apache-2.0 thumbnail: https://i.ibb.co/TvyMrRc/rsz-smol-llama-banner.png language:

• —en inference: parameters: maxnewtokens: 64 dosample: true temperature: 0.8 repetitionpenalty: 1.15 norepeatngramsize: 4 etacutoff: 0.0006 renormalize_logits: true widget:
• —text: My name is El Microondas the Wise, and example_title: El Microondas
• —text: Kennesaw State University is a public example_title: Kennesaw State University
• —text: >- Bungie Studios is an American video game developer. They are most famous for developing the award winning Halo series of video games. They also made Destiny. The studio was founded example_title: Bungie
• —text: The Mona Lisa is a world-renowned painting created by example_title: Mona Lisa
• —text: >- The Harry Potter series, written by J.K. Rowling, begins with the book titled example_title: Harry Potter Series
• —text: >- Question: I have cities, but no houses. I have mountains, but no trees. I have water, but no fish. What am I?

Answer: example_title: Riddle

• —text: The process of photosynthesis involves the conversion of example_title: Photosynthesis
• —text: >- Jane went to the store to buy some groceries. She picked up apples, oranges, and a loaf of bread. When she got home, she realized she forgot example_title: Story Continuation
• —text: >- Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph, and another train leaves Station B at 10:00 AM and travels at 80 mph, when will they meet if the distance between the stations is 300 miles?

To determine example_title: Math Problem

• —text: In the context of computer programming, an algorithm is exampletitle: Algorithm Definition pipelinetag: text-generation tags:
• —smol_llama
• —llama2 datasets:
• —JeanKaddour/minipile
• —pszemraj/simplewikipediaLM
• —BEE-spoke-data/wikipedia-20230901.en-deduped
• —mattymchen/refinedweb-3m ---

smol_llama-101M-GQA

<img src="smol-llama-banner.png" alt="banner" style="max-width:95%; height:auto;">

A small 101M param (total) decoder model. This is the first version of the model.

• —768 hidden size, 6 layers
• —GQA (24 heads, 8 key-value), context length 1024
• —train-from-scratch

Features

Some cool anecdotes about this model:

• —this model was pretrained on one GPU for 5 compute-days. You can DIY pretrain too!
• —0% of the training data (to our knowledge) comes from OpenAI synthetic generation

Notes

This checkpoint is the 'raw' pre-trained model and has not been tuned to a more specific task. It should be fine-tuned before use in most cases.

Checkpoints & Links

• —smol-er 81M parameter checkpoint with in/out embeddings tied: here
• —Fine-tuned on pypi to generate Python code - link
• —For the chat version of this model, please see here

Citation Info

If you find this experiment useful and would like to add some words to your .bib file, it would make us happy.

@misc {beespoke_data_2023,
	author       = { {Peter Szemraj and Vincent Haines} },
	title        = { smol_llama-101M-GQA (Revision 9c9c090) },
	year         = 2023,
	url          = { https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA },
	doi          = { 10.57967/hf/1440 },
	publisher    = { Hugging Face }
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here