README.md

---
library_name: transformers
language:
  - mr
tags:
  - SLM
  - marathi-slm
  - sangraha
  - SmolLM2
datasets:
  - ai4bharat/sangraha
---

# Model Card for Model ID

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## Model Details
An experimental 145M parameter pre-trained base model for marathi. Inspired by SmolLM2 and its architecture.

Pre-trained on verified marathi split of the [`ai4bharat/sangraha`](https://huggingface.co/datasets/ai4bharat/sangraha) dataset, around ~2.8 billion tokens.

Note: This is an experimental model and will be followed by more pre-training, followed by task specific instruction finetuning.

## How to use
```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sky-2002/Marathi-SmolLM2-145M")
model = AutoModelForCausalLM.from_pretrained("sky-2002/Marathi-SmolLM2-145M")

sentence = "पुणे विद्यापीठाने म्हटले आहे"
inputs = tokenizer(sentence, return_tensors="pt")
output = model.generate(**inputs, max_length=50)
print(tokenizer.batch_decode(output, skip_special_tokens=True))
```

### Model Description, data and training details

**Architecture**: SmolLM2 based

**Tokenizer**: Uses the `sarvamai/sarvam-1` tokenizer, since it has been trained on indic languages and has lower fertility rates than existing multilingual tokenizers.

**Training dataset**:
The training dataset covers the following domains.

![alt text](image.png)
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**Training**:
- Trained using modal platform on an A100.
- Trained for 1 epoch on verified marathi split of sangraha dataset, covering ~5.8M samples.

This model can generate coherent text, especially in the domains similar to those in the training dataset.

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## Bias, Risks, and Limitations
This model is trained on data of 2.8 B tokens and using a context length of 512, due to computational constraints of training.
Often gives out gibberish if prompt is not related to domains shown, or if in a conversational style.

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