# BEE-spoke-data/smol_llama-220M-GQA-GGUF

Quantized GGUF model files for [smol_llama-220M-GQA](https://huggingface.co/BEE-spoke-data/smol_llama-220M-GQA) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data)


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [smol_llama-220m-gqa.fp16.gguf](https://huggingface.co/afrideva/smol_llama-220M-GQA-GGUF/resolve/main/smol_llama-220m-gqa.fp16.gguf) | fp16 | 436.50 MB  |
| [smol_llama-220m-gqa.q2_k.gguf](https://huggingface.co/afrideva/smol_llama-220M-GQA-GGUF/resolve/main/smol_llama-220m-gqa.q2_k.gguf) | q2_k | 102.60 MB  |
| [smol_llama-220m-gqa.q3_k_m.gguf](https://huggingface.co/afrideva/smol_llama-220M-GQA-GGUF/resolve/main/smol_llama-220m-gqa.q3_k_m.gguf) | q3_k_m | 115.70 MB  |
| [smol_llama-220m-gqa.q4_k_m.gguf](https://huggingface.co/afrideva/smol_llama-220M-GQA-GGUF/resolve/main/smol_llama-220m-gqa.q4_k_m.gguf) | q4_k_m | 137.58 MB  |
| [smol_llama-220m-gqa.q5_k_m.gguf](https://huggingface.co/afrideva/smol_llama-220M-GQA-GGUF/resolve/main/smol_llama-220m-gqa.q5_k_m.gguf) | q5_k_m | 157.91 MB  |
| [smol_llama-220m-gqa.q6_k.gguf](https://huggingface.co/afrideva/smol_llama-220M-GQA-GGUF/resolve/main/smol_llama-220m-gqa.q6_k.gguf) | q6_k | 179.52 MB  |
| [smol_llama-220m-gqa.q8_0.gguf](https://huggingface.co/afrideva/smol_llama-220M-GQA-GGUF/resolve/main/smol_llama-220m-gqa.q8_0.gguf) | q8_0 | 232.28 MB  |



## Original Model Card:
# smol_llama: 220M GQA

> model card WIP, more details to come


A small 220M param (total) decoder model. This is the first version of the model.

- 1024 hidden size, 10 layers
- GQA (32 heads, 8 key-value), context length 2048
- train-from-scratch on one GPU :)


---
{"language": ["en"], "license": "apache-2.0", "tags": ["smol_llama", "llama2", "gguf", "ggml", "quantized", "q2_k", "q3_k_m", "q4_k_m", "q5_k_m", "q6_k", "q8_0"], "datasets": ["JeanKaddour/minipile", "pszemraj/simple_wikipedia_LM", "mattymchen/refinedweb-3m", "BEE-spoke-data/knowledge-inoc-concat-v1"], "model_name": "smol_llama-220M-GQA", "base_model": "BEE-spoke-data/smol_llama-220M-GQA", "inference": false, "model_creator": "BEE-spoke-data", "pipeline_tag": "text-generation", "quantized_by": "afrideva", "widget": [{"example_title": "El Microondas", "text": "My name is El Microondas the Wise, and"}, {"example_title": "Kennesaw State University", "text": "Kennesaw State University is a public"}, {"example_title": "Bungie", "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": "Mona Lisa", "text": "The Mona Lisa is a world-renowned painting created by"}, {"example_title": "Harry Potter Series", "text": "The Harry Potter series, written by J.K. Rowling, begins with the book titled"}, {"example_title": "Riddle", "text": "Question: I have cities, but no houses. I have mountains, but no trees. I have water, but no fish. What am I?\nAnswer:"}, {"example_title": "Photosynthesis", "text": "The process of photosynthesis involves the conversion of"}, {"example_title": "Story Continuation", "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": "Math Problem", "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?\nTo determine"}, {"example_title": "Algorithm Definition", "text": "In the context of computer programming, an algorithm is"}]}
gguf smol_llama llama2 ggml quantized q2_k q3_k_m q4_k_m q5_k_m q6_k q8_0 text-generation en dataset:JeanKaddour/minipile dataset:pszemraj/simple_wikipedia_LM dataset:mattymchen/refinedweb-3m dataset:BEE-spoke-data/knowledge-inoc-concat-v1 base_model:BEE-spoke-data/smol_llama-220M-GQA license:apache-2.0 region:us