Text Generation
GGUF
English
smol_llama
llama2
ggml
quantized
q2_k
q3_k_m
q4_k_m
q5_k_m
q6_k
q8_0
afrideva commited on
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+ ---
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+ base_model: BEE-spoke-data/smol_llama-220M-GQA
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+ datasets:
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+ - JeanKaddour/minipile
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+ - pszemraj/simple_wikipedia_LM
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+ - mattymchen/refinedweb-3m
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+ - BEE-spoke-data/knowledge-inoc-concat-v1
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+ inference: false
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+ language:
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+ - en
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+ license: apache-2.0
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+ model_creator: BEE-spoke-data
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+ model_name: smol_llama-220M-GQA
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+ pipeline_tag: text-generation
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+ quantized_by: afrideva
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+ tags:
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+ - smol_llama
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+ - llama2
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+ - gguf
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+ - ggml
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+ - quantized
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+ - q2_k
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+ - q3_k_m
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+ - q4_k_m
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+ - q5_k_m
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+ - q6_k
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+ - q8_0
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+ widget:
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+ - example_title: El Microondas
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+ text: My name is El Microondas the Wise, and
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+ - example_title: Kennesaw State University
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+ text: Kennesaw State University is a public
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+ - example_title: Bungie
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+ text: Bungie Studios is an American video game developer. They are most famous for
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+ developing the award winning Halo series of video games. They also made Destiny.
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+ The studio was founded
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+ - example_title: Mona Lisa
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+ text: The Mona Lisa is a world-renowned painting created by
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+ - example_title: Harry Potter Series
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+ text: The Harry Potter series, written by J.K. Rowling, begins with the book titled
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+ - example_title: Riddle
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+ text: 'Question: I have cities, but no houses. I have mountains, but no trees. I
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+ have water, but no fish. What am I?
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+
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+ Answer:'
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+ - example_title: Photosynthesis
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+ text: The process of photosynthesis involves the conversion of
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+ - example_title: Story Continuation
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+ text: Jane went to the store to buy some groceries. She picked up apples, oranges,
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+ and a loaf of bread. When she got home, she realized she forgot
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+ - example_title: Math Problem
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+ text: 'Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph,
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+ and another train leaves Station B at 10:00 AM and travels at 80 mph, when will
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+ they meet if the distance between the stations is 300 miles?
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+
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+ To determine'
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+ - example_title: Algorithm Definition
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+ text: In the context of computer programming, an algorithm is
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+ ---
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+ # BEE-spoke-data/smol_llama-220M-GQA-GGUF
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+
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+ 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)
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+
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+
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+ | Name | Quant method | Size |
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+ | ---- | ---- | ---- |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+
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+
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+
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+ ## Original Model Card:
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+ # smol_llama: 220M GQA
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+
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+ > model card WIP, more details to come
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+
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+
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+ A small 220M param (total) decoder model. This is the first version of the model.
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+
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+ - 1024 hidden size, 10 layers
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+ - GQA (32 heads, 8 key-value), context length 2048
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+ - train-from-scratch on one GPU :)
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+
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+
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+ ---