File size: 4,883 Bytes
627e53a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
---
base_model: BEE-spoke-data/smol_llama-101M-GQA
datasets:
- JeanKaddour/minipile
- pszemraj/simple_wikipedia_LM
- BEE-spoke-data/wikipedia-20230901.en-deduped
- mattymchen/refinedweb-3m
inference: false
language:
- en
license: apache-2.0
model_creator: BEE-spoke-data
model_name: smol_llama-101M-GQA
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- smol_llama
- llama2
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
thumbnail: https://i.ibb.co/TvyMrRc/rsz-smol-llama-banner.png
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?
Answer:'
- 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?
To determine'
- example_title: Algorithm Definition
text: 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](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [smol_llama-101m-gqa.fp16.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.fp16.gguf) | fp16 | 203.28 MB |
| [smol_llama-101m-gqa.q2_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.q2_k.gguf) | q2_k | 50.93 MB |
| [smol_llama-101m-gqa.q3_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.q3_k_m.gguf) | q3_k_m | 57.06 MB |
| [smol_llama-101m-gqa.q4_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.q4_k_m.gguf) | q4_k_m | 65.40 MB |
| [smol_llama-101m-gqa.q5_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.q5_k_m.gguf) | q5_k_m | 74.34 MB |
| [smol_llama-101m-gqa.q6_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.q6_k.gguf) | q6_k | 83.83 MB |
| [smol_llama-101m-gqa.q8_0.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-GGUF/resolve/main/smol_llama-101m-gqa.q8_0.gguf) | q8_0 | 108.35 MB |
## Original Model Card:
# 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
## 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](https://huggingface.co/BEE-spoke-data/smol_llama-81M-tied)
- Fine-tuned on `pypi` to generate Python code - [link](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA-python)
- For the chat version of this model, please [see here](https://youtu.be/dQw4w9WgXcQ?si=3ePIqrY1dw94KMu4)
---
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__smol_llama-101M-GQA)
| 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 | |