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language: en
tags:
- pytorch
- causal-lm
- language-model
- text-generation
license: mit
datasets:
- wikitext
metrics:
- perplexity
- loss
library_name: pytorch
pipeline_tag: text-generation
model-index:
- name: SmolLM-125M
results:
- task:
type: text-generation
name: Language Modeling
dataset:
type: wikitext
name: WikiText-2
metrics:
- type: perplexity
value: to_be_updated # Will be updated after training
- type: loss
value: to_be_updated # Will be updated after training
---
# SmolLM-125M: A Lightweight Language Model for Consumer Hardware
This is a 125M parameter language model designed to be trained and run on consumer hardware with limited VRAM (4GB+). The model follows a GPT-style architecture but is optimized for efficiency and memory usage.
## Model Details
- **Architecture:** GPT-style Transformer
- **Parameters:** 125M
- **Context Length:** 512 tokens
- **Vocabulary:** 50,257 tokens (GPT-2 tokenizer)
- **Training Data:** WikiText-2
- **Hardware Requirements:** 4GB+ VRAM GPU
### Architecture Specifications
- Layers: 12 transformer blocks
- Attention Heads: 12
- Embedding Dimension: 768
- Activation: GELU
- Layer Normalization: Pre-norm
## Training Details
- **Hardware Used:** GTX 1650 (4GB VRAM)
- **Training Time:** ~4 hours
- **Batch Size:** 4 (16 with gradient accumulation)
- **Learning Rate:** 3e-4 with cosine decay
- **Weight Decay:** 0.1
- **Optimizer:** AdamW
### Memory Optimizations
1. Length-based batch scheduling
2. Gradient accumulation (4 steps)
3. Dynamic batch scheduling
4. Pre-padded sequences
## Usage
```python
from transformers import AutoTokenizer
from model import SmallLanguageModel, ModelConfig
# Initialize model
config = ModelConfig(
vocab_size=50257,
block_size=512,
n_layer=12,
n_head=12,
n_embd=768,
dropout=0.1,
bias=True
)
model = SmallLanguageModel(config)
# Generate text
tokenizer = AutoTokenizer.from_pretrained("gpt2")
input_text = "Once upon a time"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
output_ids = model.generate(input_ids, max_length=100)
generated_text = tokenizer.decode(output_ids[0])
```
## Limitations
- Limited context window (512 tokens)
- Smaller capacity compared to larger models
- Training data limited to WikiText-2
## License
This model is released under the MIT License. |