---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---
This repo contains the Base (completed pretrain cudalm checkpoint) variant of LS3-Micro. The FP32 checkpoint and optimiser is provided, and can be used by tinylm chat.
- Model size: 30.3MB (FP32)
- Environment variables for cudalm: 381B
- AdamW optimiser: 30.0MB
- Muon optimiser: 30.0MB
- Ngrams optimiser: 7.22MB
- Sidecar: 40.1KB
# LS3-Micro: The smallest model with the highest intelligence density in the LS family.
| (acc norm except LAMBADA) | HellaSwag | ARC-E | ARC-C | PIQA | LAMBADA | ArithMark | \*II | \*\*Intelligence Density |
| ------------------------- | --------- | --------- | --------- | ------------ | ----------- | ------------- | --------- | ---------------------------- |
| LS2.5-108M-A17M-Base | *28.24* | *36.66* | 23.21 | *57.40* | *17.89* | 37.00 | *9.89* | 0.231 |
| LS-Tiny-Base (31M) | 27.11 | 32.24 | **24.49** | 53.81 | 2.99 | 34.00 | 6.22 | 0.288 |
| LS-Micro-Base (11.49M) | **27.41** | 34.72 | 21.93 | 54.84 | 11.06 | 35.50 | 7.24 | 0.909 |
| LS3-Micro-Base (9.97M) | 27.31 | **34.76** | 23.29 | **57.34** | **12.87** | **38.90** | **9.64** | **1.290** |
LS3-Micro-Base has a 24.9% higher Intelligence Index than LS-Micro Base, while having 13% less total parameters. It also nearly matches LS2.5-108M-A17M with almost 11x less parameters.
- Intelligence Index was calculated using the formula from [Open SLM Leaderboard](https://huggingface.co/spaces/AxiomicLabs/Open_SLM_Leaderboard). (*Results reproducable via Cudalm's built in bench tool, see bottom quickstart*)
- Intelligence density derived using the following formula: Intelligence Index / sqrt(Active Params (millions) * Total Params (millions))
---
LS3 Experiments on narrow but deeper model architectures for SLMs. For a sub 10M model, the sweet spot found was 11 layers with a dimension of 160.
To maximise efficiency while training, LS3 is a not a dense model, but rather a sparse MoE model with ngrams. With a total of 9.97M parameters, ~2.11M of parameters are ngrams. The remaining ~7.87M of parameters make up the MoE backbone. The model has 2 experts and only activates one per token, leading to only 5.60M active per token.
| **Model specs** | |
|----------------------|--------|
| Total parameters: | 9.97M |
| Backbone: | 7.87M |
| Active: | 5.60M |
| Ngrams: | 2.11M |
| Context length: | 2048 |
| **Pretrain details** | |
| Tokens seen: | ~5.7B |
| GPU time (5060ti): | 8h |
\*Note that it was trained with a 16k vocab, so 2.62M of the 5.52M active parameters are embedding.
## Chatbench results:
Multiple choice:
| Model | acc | accnorm | chance |
| -------------- | --------- | ----------- | ------ |
| LS-Tiny-Base | 40.7% | 47.5% | 25.0% |
| LS-Micro-Base | **46.6%** | 52.5% | 25.0% |
| LS3-Micro-Base | 45.8% | **55.1%** | 25.0% |
Example:
```
<|user|>
What is your opinion on Strawberries?
<|assistant|>
A strawberry, which is a type of paprika, or strawberry, is a type of meat-based food that is commonly used in culinary arts. Strawberry is a type of lion's food, also known as lime juice, which is a staple in the food. This type of strawberry is not only a vegetable but also a sweet and hearty treat.
The idea is that strawberry is a type of meat-based food, which is a type of lion's food, like paprika or...
```
## To try LS3-Micro or to train/finetune the model, follow this quickstart guide:
1. Clone the latest version of Cudalm [here](https://github.com/Dsg2/cudalm-c) (only windows supported currently, optimised for Blackwell 2.0/CPU).
2. Build with `build.py`/`build.py` --cpu'. Run cudalm to init all folders.
3. Download all LS3-Micro files from `LS3-Micro-Base-Checkpoint\` and drop them in `\models\`. Cudalm then automatically detects the model.
Voila, you can now start experimenting with the model. Some basic stuff you can try first:
- Check the model's config, and settings used to train it. `> Model info`.
- Run the model: `> Generate`.
To reproduce the benchmark results, use `> LM eval`. Run all benchmarks.
## Finetune guide:
To finetune the model, you first need to prepare some data.
Navigate to `> Data tools...`. You can download preconfigured mixtures of data, use files on your system or nitpick your own datasets.
- To use a preset corpus, navigate into `> Data tools... > Download text`.
- Otherwise, find a dataset on hugginface and navigate to `> Data tools... > Browse Hugging Face`, and pick out your chosen dataset from there. Note that datasets with gated access requires your `HF_TOKEN` to be set.
This guide will use the dataset [MiniCPM5-2B-distill](https://huggingface.co/datasets/Dsg2/MiniCPM5-2B-distill) as an example. For your own text or other datasets, simply select the folder while tokenizing.
Navigate to `> Data tools... > Browse Hugging Face`, and search for "MiniCPM5-2B-distill". Cudalm automatically detects the format, so all you need to do is download it (~250MB).
Then you will need to tokenize the text so that the model can be trained on it. To do this, you will need a trained tokenizer. Luckily, you can extract an existing model's tokenizer. Use `> Data tools... > Tokenizer from model` and pull out LS3-Micro-Base's tokenizer.
Now you can tokenize your data. Go to `> Data tools... > Build corpus` and set `mode` to "new" and give it a corpus name. For your own datasets or text, navigate to the folder the text files are in. Otherwise, select `\data\hf_MiniCPM5-2B-distill`, then use the extracted tokenizer and select `Run`. It should take ~10s and produce `\data\`
You're now ready to start fine tuning!
Go to `> Train` and select "LS3-Micro-Base" as the model, and the tokenized corpus as the data directory (both auto-detected). Select Run to start training the model on the selected corpus. Progress prints and periodic saves will occur every few hundred iters.