base_model, datasets, inference, language, license, model_creator, model_name, pipeline_tag, quantized_by, tags, thumbnail, widget

base_model	datasets	inference	language	license	model_creator	model_name	pipeline_tag	quantized_by	tags	thumbnail	widget
BEE-spoke-data/smol_llama-101M-GQA	JeanKaddour/minipile pszemraj/simple_wikipedia_LM BEE-spoke-data/wikipedia-20230901.en-deduped mattymchen/refinedweb-3m	false	en	apache-2.0	BEE-spoke-data	smol_llama-101M-GQA	text-generation	afrideva	smol_llama llama2 gguf ggml quantized q2_k q3_k_m q4_k_m q5_k_m q6_k q8_0	https://i.ibb.co/TvyMrRc/rsz-smol-llama-banner.png	example_title text El Microondas My name is El Microondas the Wise and example_title text Kennesaw State University Kennesaw State University is a public example_title text Bungie 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 text Mona Lisa The Mona Lisa is a world-renowned painting created by example_title text Harry Potter Series The Harry Potter series, written by J.K. Rowling, begins with the book titled example_title text Riddle 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 text Photosynthesis The process of photosynthesis involves the conversion of example_title text Story Continuation 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 text Math Problem 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 text Algorithm Definition In the context of computer programming, an algorithm is

BEE-spoke-data/smol_llama-101M-GQA-GGUF

Quantized GGUF model files for smol_llama-101M-GQA from BEE-spoke-data

Name	Quant method	Size
smol_llama-101m-gqa.fp16.gguf	fp16	203.28 MB
smol_llama-101m-gqa.q2_k.gguf	q2_k	50.93 MB
smol_llama-101m-gqa.q3_k_m.gguf	q3_k_m	57.06 MB
smol_llama-101m-gqa.q4_k_m.gguf	q4_k_m	65.40 MB
smol_llama-101m-gqa.q5_k_m.gguf	q5_k_m	74.34 MB
smol_llama-101m-gqa.q6_k.gguf	q6_k	83.83 MB
smol_llama-101m-gqa.q8_0.gguf	q8_0	108.35 MB

Original Model Card:

smol_llama-101M-GQA

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

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

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Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	25.32
ARC (25-shot)	23.55
HellaSwag (10-shot)	28.77
MMLU (5-shot)	24.24
TruthfulQA (0-shot)	45.76
Winogrande (5-shot)	50.67
GSM8K (5-shot)	0.83
DROP (3-shot)	3.39