library_name: transformers
language:
- mr
tags:
- SLM
- marathi-slm
- sangraha
- SmolLM2
datasets:
- ai4bharat/sangraha
Model Card for Model ID
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 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
# 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.
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.
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.