Llara
Llara is a 91.4M parameter autoregressive language model trained from scratch on English web text. It follows the GPT-2 Small architecture and is trained entirely from random initialisation — no pretrained weights, no distillation, no fine-tuning of an existing model. but it does use GPT's tokenizer
The name Llara is original and unrelated to LLaMA or LoRA.
Model Details
| Property | Value |
|---|---|
| Architecture | GPT-2 (decoder-only transformer) |
| Parameters | ~90-100M |
| Context length | 256 tokens |
| Embedding dim | 768 |
| Layers | 12 |
| Attention heads | 12 |
| Vocabulary | 50,257 (GPT-2 BPE) |
| Training data | FineWeb (HuggingFaceFW/fineweb), Custom dataset |
| Training docs | 1,000,000 documents |
| Epochs | 1 |
| Precision | fp16 |
Training
Llara was trained on 1 million documents sampled from FineWeb, a large-scale curated English web dataset. Documents were tokenised with the GPT-2 BPE tokeniser and packed into non-overlapping 1024-token blocks.
Training configuration:
| Hyperparameter | Value |
|---|---|
| Optimiser | AdamW |
| Learning rate | 3e-4 |
| LR schedule | Cosine decay |
| Warmup steps | 2,000 |
| Weight decay | 0.1 |
| Effective batch size | 32 |
| Gradient accumulation | 8 steps |
| Dropout | 0.1 (residual, embedding, attention) |
Gradient checkpointing was enabled throughout training to reduce memory usage.
Usage
from transformers import GPT2LMHeadModel, AutoTokenizer, pipeline
model = GPT2LMHeadModel.from_pretrained("helloadhavan/llara1.0-100M-base")
tokenizer = AutoTokenizer.from_pretrained("helloadhavan/llara1.0-100M-base")
gen = pipeline("text-generation", model=model, tokenizer=tokenizer)
output = gen(
"The history of artificial intelligence",
max_new_tokens=200,
do_sample=True,
temperature=0.8,
top_p=0.95,
repetition_penalty=1.1,
)
print(output[0]["generated_text"])
Limitations
- Llara is trained on English web text only and performs poorly on other languages.
- Like all autoregressive LMs trained on web data, it may reproduce biases, factual errors, or inappropriate content present in the training corpus.
- It is a research model trained from scratch and is not instruction-tuned or aligned — it should not be used in production or user-facing applications without further fine-tuning and safety work.
- At 95M parameters and 256k training documents, it is significantly smaller and less trained than models like GPT-2 (which saw 40GB of text). Outputs may be incoherent on complex prompts.
Intended Use
Llara is intended for:
- Research and experimentation with small language models
- Learning how GPT-style models are trained from scratch
- A base for fine-tuning on downstream tasks
Training Framework
Trained using Hugging Face Transformers Trainer on a single GPU.
License
Apache 2.0
Note: i am a AI hobbyist, not an AI engineer
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