File size: 5,377 Bytes

---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---

This repo contains the Base (completed pretrain cudalm checkpoint) variant of LS-Micro. The FP32 checkpoint and optimiser is provided, and can be used by tinylm chat.
 - Model size: 28.1MB (FP32)
 - Cudalm QLM optimiser: 26.1MB
 - Ngrams container: 14.4MB
 - T1 sidecar: 48KB
 - Environment vars: 295B

# LS-Micro: Smallest and fastest model in the LS family.

|               | HellaSwag | ARC-E | ARC-C | PIQA     | LAMBADA | ArithMark | II*  |
| ------------- | --------- | ----- | ----- | -------- | ------- | --------- | ---- |
| LS-Micro-Base | 27.41     | 34.72 | 21.93 | 54.84    | 11.06   | 35.50     | 7.24 |
| LS-Tiny-Base  | 27.11     | 32.24 | 24.49 | 53.81    | 2.99    | 34.00     | 6.22 |

LS-Micro is 3x smaller than LS-TIny (11M vs 31M), and has 3x less active parameters (5M vs 15M), yet has a 16% higher intelligence index.

\* 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*)

Pushing the limits of size to quality, LS-Micro-Base is a sparse 11.49M MoE model (A5.52M) pretrained on 5.7B tokens.
Training at a speed of 255.6k tokens/s on a 5060ti 16GB, it offers an incredible training efficiency at ~2.9x faster tokens seen compared to LS2.5's 108M-A17M model.
Fully pretrained in ~6 hours.

| **Model specs**      |        |
|----------------------|--------|
| Total parameters:    | 11.49M |
| Backbone:            | 7.28M  |
| Active:              | 5.52M  |
| Ngrams:              | 4.21M  |
| Context length:      | 2048   |
| **Pretrain details** |        |
| Tokens seen:         | ~5.7B  |
| GPU time (5060ti):   | 22310s |

*Note that it was trained with a 16k vocab, so 3.15M of the 5.52M active parameters are embedding.

## Chatbench results:

Multiple choice:
| category      | n   | acc       | accnorm     | chance |
| ------------- | --- | --------- | ----------- | ------ |
| fact          | 25  | 48.0%     | 60.0%       | 25.0%  |
| commonsense   | 20  | 65.0%     | 55.0%       | 25.0%  |
| instruct      | 20  | 55.0%     | 50.0%       | 25.0%  |
| relevance     | 20  | 5.0%      | 30.0%       | 25.0%  |
| consistency   | 18  | 61.1%     | 72.2%       | 25.0%  |
| arithmetic    | 15  | 46.7%     | 46.7%       | 25.0%  |
| LS-Micro-Base | 118 | **46.6%** | **52.5%**   | 25.0%  |
| LS-Tiny-Base  | 118 | 40.7%     | 47.5%       | 25.0%  |

Example: (Greedy)
```
<|user|>
Hi
<|assistant|>
I'm sorry, but I'm unable to assist with the problem. My current capabilities are limited to the functions provided to me. In this case, I can help you solve the problem.
```

## The base model is horrendous at chat, and is intended to be used as a base to be finetuned for specialised tasks. 
To try LS-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 LS-Micro files from `LS-Micro-Base-Checkpoint\` and drop them in `<cudalm.exe>\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`

## 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 LS-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 `<cudalm.exe>\data\hf_MiniCPM5-2B-distill`, then use the extracted tokenizer and select `Run`. It should take ~10s and produce `<cudalm.exe>\data\<corpus name>`

You're now ready to start fine tuning!

Go to `> Train` and select "LS-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.