Quantization made by Richard Erkhov.

smol_llama-220M-GQA - GGUF

Model creator: https://huggingface.co/BEE-spoke-data/
Original model: https://huggingface.co/BEE-spoke-data/smol_llama-220M-GQA/

Name	Quant method	Size
smol_llama-220M-GQA.Q2_K.gguf	Q2_K	0.09GB
smol_llama-220M-GQA.IQ3_XS.gguf	IQ3_XS	0.1GB
smol_llama-220M-GQA.IQ3_S.gguf	IQ3_S	0.1GB
smol_llama-220M-GQA.Q3_K_S.gguf	Q3_K_S	0.1GB
smol_llama-220M-GQA.IQ3_M.gguf	IQ3_M	0.1GB
smol_llama-220M-GQA.Q3_K.gguf	Q3_K	0.11GB
smol_llama-220M-GQA.Q3_K_M.gguf	Q3_K_M	0.11GB
smol_llama-220M-GQA.Q3_K_L.gguf	Q3_K_L	0.11GB
smol_llama-220M-GQA.IQ4_XS.gguf	IQ4_XS	0.12GB
smol_llama-220M-GQA.Q4_0.gguf	Q4_0	0.12GB
smol_llama-220M-GQA.IQ4_NL.gguf	IQ4_NL	0.12GB
smol_llama-220M-GQA.Q4_K_S.gguf	Q4_K_S	0.12GB
smol_llama-220M-GQA.Q4_K.gguf	Q4_K	0.13GB
smol_llama-220M-GQA.Q4_K_M.gguf	Q4_K_M	0.13GB
smol_llama-220M-GQA.Q4_1.gguf	Q4_1	0.13GB
smol_llama-220M-GQA.Q5_0.gguf	Q5_0	0.14GB
smol_llama-220M-GQA.Q5_K_S.gguf	Q5_K_S	0.14GB
smol_llama-220M-GQA.Q5_K.gguf	Q5_K	0.15GB
smol_llama-220M-GQA.Q5_K_M.gguf	Q5_K_M	0.15GB
smol_llama-220M-GQA.Q5_1.gguf	Q5_1	0.16GB
smol_llama-220M-GQA.Q6_K.gguf	Q6_K	0.17GB
smol_llama-220M-GQA.Q8_0.gguf	Q8_0	0.22GB

Original model description:

language:

en license: apache-2.0 tags:
smol_llama
llama2 datasets:
JeanKaddour/minipile
pszemraj/simple_wikipedia_LM
mattymchen/refinedweb-3m
BEE-spoke-data/knowledge-inoc-concat-v1 inference: parameters: max_new_tokens: 64 do_sample: true temperature: 0.8 repetition_penalty: 1.05 no_repeat_ngram_size: 4 eta_cutoff: 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 example_title: Algorithm Definition pipeline_tag: text-generation model-index:
name: smol_llama-220M-GQA results:
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm value: 24.83 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc_norm value: 29.76 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
  - type: acc value: 25.85 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 44.55 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 50.99 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 0.68 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA name: Open LLM Leaderboard

smol_llama: 220M GQA

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 :)

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	29.44
AI2 Reasoning Challenge (25-Shot)	24.83
HellaSwag (10-Shot)	29.76
MMLU (5-Shot)	25.85
TruthfulQA (0-shot)	44.55
Winogrande (5-shot)	50.99
GSM8k (5-shot)	0.68

RichardErkhov
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BEE-spoke-data_-_smol_llama-220M-GQA-gguf

Original model description:

smol_llama: 220M GQA

Links

Open LLM Leaderboard Evaluation Results