metadata
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
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 23.86
name: strict accuracy
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 3.04
name: normalized accuracy
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 0
name: exact match
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 0.78
name: acc_norm
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-GQA
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 9.07
name: acc_norm
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/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-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 1.66
name: accuracy
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/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 :)
Links
Here are some fine-tunes we did, but there are many more possibilities out there!
- instruct
- code
- python (pypi) - link
- zephyr DPO tune
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 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 6.62 |
| IFEval (0-Shot) | 23.86 |
| BBH (3-Shot) | 3.04 |
| MATH Lvl 5 (4-Shot) | 0.00 |
| GPQA (0-shot) | 0.78 |
| MuSR (0-shot) | 9.07 |
| MMLU-PRO (5-shot) | 1.66 |