YALM-130M

YALM-130M

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-130M is the second model in this series. 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.

Model Overview:

• Architecture: Llama
• Pretraining steps: 40k
• Pretraining tokens: 42B
• Precision: bfloat16
• Number of Parameters: 130M
• Number of Paramaters (Non-Embedding): 113M
• Number of Layers: 16
• Number of Attention Heads (GQA): 16 for Q and 2 for KV
• Context Length: 2048

Usage

>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("kp7742/YALM-130M")
>>> model = AutoModelForCausalLM.from_pretrained("kp7742/YALM-130M")
>>> 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-pretrain6-62M

Hyperparameters

• learning_rate: 6e-3
• train_batch_size: 16
• eval_batch_size: 16
• distributed_type: multi-GPU DDP
• num_devices: 4
• gradient_accumulation_steps: 8
• total_train_batch_size: 512
• total_eval_batch_size: 64
• optimizer: AdamW with betas=(0.9, 0.95) and epsilon=1e-08
• lr_scheduler_type: warmup_stable_decay
• lr_scheduler_warmup_steps: 4000
• training_steps: 40000

Hardware

• GPUs: 4 x RTX 5090

Framework versions

• Transformers 4.56.2
• Pytorch 2.8.0+cu128
• Datasets 4.1.1
• Tokenizers 0.22.1

Evaluation

All evaluations are zero-shot unless stated otherwise, and I used lighteval to run them.

It achieves the following results on the test set:

• Loss: 2.46
• Perplexity: 11.66

Base pre-trained model

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.