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
>>> 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
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 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.