---
library_name: transformers
datasets:
- kp7742/YALM-pretrain5-60M
language:
- en
- hi
pipeline_tag: text-generation
tags:
- pt
- yalm
---

# YALM-80M

YALM (Yet Another Language Model) is a family of an experimental small language models developed through my ongoing exploration of language modeling and LLM architectures.

YALM-80M is the first member model in this family. This model is trained on a diverse corpus of English, Hindi, Math, and Python Code to test its capacity for multi-lingual and technical reasoning.

Note: There is a bug in tokenizer which may cause error during generation for certrain inputs.

**Model Overview:**
- Architecture: Llama
- Pretraining steps: 34k
- Pretraining tokens: 36B
- Precision: bfloat16
- Number of Parameters: 79.7M
- Number of Paramaters (Non-Embedding): 62.9M
- Number of Layers: 16
- Number of Attention Heads (GQA): 8 for Q and 4 for KV
- Context Length: 2048

## Usage

```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("kp7742/YALM-80M")
>>> model = AutoModelForCausalLM.from_pretrained("kp7742/YALM-80M")
>>> inputs = tokenizer("Hey how are you doing?", return_tensors="pt")
>>> out = model.generate(**inputs, max_new_tokens=100)
>>> print(tokenizer.batch_decode(out))
```

## Training

### Data

This model is pre-trained on [YALM-pretrain5-60M](https://huggingface.co/datasets/kp7742/YALM-pretrain5-60M)

### Hyperparameters

- learning_rate: 0.007812
- train_batch_size: 16
- eval_batch_size: 16
- distributed_type: multi-GPU DDP
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 128
- optimizer: AdamW with betas=(0.9, 0.95) and epsilon=1e-08
- lr_scheduler_type: warmup_stable_decay
- lr_scheduler_warmup_steps: 3400
- training_steps: 34000

### Hardware

- GPUs: 8 x RTX 4090

### Framework versions

- Transformers 4.53.1
- Pytorch 2.7.1+cu128
- Datasets 3.6.0
- Tokenizers 0.21.2

## Evaluation

All evaluations are zero-shot unless stated otherwise, and I used [lighteval](https://github.com/huggingface/lighteval) to run them.

It achieves the following results on the test set:
- Loss: 2.78
- Perplexity: 16.10

## Base pre-trained model

| Metrics            | YALM-80M     |
|:-------------------|:------------:|
| MMLU (cloze)       | 27.33        |
| MMLU Pro           | 8.72         |
| BBH (5-shot)       | 12.61        |
| ARC (Average)      | 29.87        |
| HellaSwag          | 32.16        |
| PIQA               | 62.89        |
| SCIQ               | 69.50        |
| CommonsenseQA      | 28.75        |
| Winogrande         | 50.59        |
| OpenBookQA         | 29.60        |
| TruthfulQA         | 22.78        |
| TriviaQA           | 0.17         |
| GSM8K (5-shot)     | 0.83         |

## Limitations

YALM models primarily understand and generate content in English and Hindi. They can produce text on a variety of topics but as world knowledge is limited, the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data.