metadata
license: apache-2.0
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
model-index:
- name: Mixsmol-4x400M-v0.1-epoch1
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: 22.87
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Mixsmol-4x400M-v0.1-epoch1
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: 30.57
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Mixsmol-4x400M-v0.1-epoch1
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: 25.28
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Mixsmol-4x400M-v0.1-epoch1
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: 39.03
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Mixsmol-4x400M-v0.1-epoch1
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: 52.8
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Mixsmol-4x400M-v0.1-epoch1
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: 0.15
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Mixsmol-4x400M-v0.1-epoch1
name: Open LLM Leaderboard
Mixsmol-4x400M-v0.1 by Ontocord
This is the first checkpoint (Epoch 1) of Mixsmol-4x400M-v0.1 Note that this is an experimental in data mixing. Therefore, we only trained the model on 50B tokens (95% English and 5% Vietnamese) to test the following:
- Reasoining capabilities through high-quality synthetic textbooks data pretraining
- Crosslingual understanding through machine translation and multilingual + multiple tasks pretraining
After verifying our hypothesis with this run, we will schedule a second run on bigger data and compute for it to achieve its maximum capability.
Data
- Synthetic Textbooks: 8M samples
- RefinedWeb: 1M samples
- RedPajama-v2: 500K samples
- MathPile: Everything
- ThePile: MiniPile Subset
- GoodWiki
- The Stack Smol XL
- The Vault: train_small split
- Instruction Pretraining: 250k samples
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| arc_challenge | Yaml | none | 25 | acc | 0.1937 | ± | 0.0115 |
| none | 25 | acc_norm | 0.2329 | ± | 0.0124 | ||
| hellaswag | Yaml | none | 10 | acc | 0.2856 | ± | 0.0045 |
| none | 10 | acc_norm | 0.3090 | ± | 0.0046 | ||
| mmlu | N/A | none | 0 | acc | 0.2536 | ± | 0.0483 |
| - humanities | N/A | none | 5 | acc | 0.2408 | ± | 0.0341 |
| - other | N/A | none | 5 | acc | 0.2475 | ± | 0.0443 |
| - social_sciences | N/A | none | 5 | acc | 0.2567 | ± | 0.0456 |
| - stem | N/A | none | 5 | acc | 0.2756 | ± | 0.0653 |
| truthfulqa_mc2 | Yaml | none | 0 | acc | 0.3909 | ± | 0.0148 |
| winogrande | Yaml | none | 5 | acc | 0.5107 | ± | 0.014 |
| gsm8k | Yaml | get-answer | 5 | exact_match | 0 | ± | 0 |
Contribution
This work is a shared contribution between Ontocord, BEE-spoke-data and VILM
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 28.45 |
| AI2 Reasoning Challenge (25-Shot) | 22.87 |
| HellaSwag (10-Shot) | 30.57 |
| MMLU (5-Shot) | 25.28 |
| TruthfulQA (0-shot) | 39.03 |
| Winogrande (5-shot) | 52.80 |
| GSM8k (5-shot) | 0.15 |