llm-course-hw1-nano

Craftic/llm-course-hw1-nano

Educational causal language model trained as part of a Deep Learning homework assignment on a Russian jokes corpus.

This repository contains:

• a custom Byte-Level BPE tokenizer (vocabulary.json, merges.json)
• model weights in model.safetensors
• a minimal training/inference config in config.json

Model summary

• Model type: decoder-only Transformer for causal language modeling
• Tokenizer: custom Byte-Level BPE
• Vocabulary size: 1024
• Context length: 128 tokens
• Attention: Grouped-Query Attention (n_head=4, n_kv_head=2)
• Feed-forward: SwiGLU
• Normalization: RMSNorm
• Positional bias: ALiBi
• Dropout: 0.1

Configuration

• Variant: nano
• Layers: 3
• Hidden size: 96
• Attention heads: 4
• KV heads: 2
• Intermediate size: 256

Training data

• Dataset: IgorVolochay/russian_jokes
• Domain: Russian jokes / short humorous texts

Training setup

• Optimizer: AdamW
• Learning rate: 3e-4
• Training steps: 10,000
• Max sequence length: 128

Usage

These weights were uploaded from a custom homework implementation, so loading requires the same Python classes that were used during training.

import torch

# Define ByteLevelBPETokenizer and TransformerForCausalLM exactly as in the homework notebook.
# Then load artifacts from the Hub:

tokenizer = ByteLevelBPETokenizer.from_pretrained("Craftic/llm-course-hw1-nano")
model = TransformerForCausalLM.from_pretrained("Craftic/llm-course-hw1-nano")
model.eval()

prompt = "Муж приходит домой и говорит:"
input_ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long)
attention_mask = torch.ones_like(input_ids)

with torch.no_grad():
    logits = model(input_ids, attention_mask)

next_token_id = logits[0, -1].argmax().item()
print(tokenizer.decode(tokenizer.encode(prompt, add_eos_token=False) + [next_token_id]))

Notes

• This is an educational model, not a production-ready checkpoint.
• Because the model is trained on jokes, generations may be low-quality, repetitive, or stylistically narrow.
• The repository does not include a packaged inference library; the custom tokenizer/model classes should be copied from the homework notebook or moved into a Python module before loading.

Limitations

• Small context window
• Small vocabulary
• Trained on a narrow-domain dataset
• No safety alignment or moderation tuning

Intended use

• Coursework demonstration
• Experiments with custom tokenization and compact LMs
• Lightweight local generation experiments

Not intended use

• Factual QA
• Safety-critical tasks
• Production deployment without additional packaging, evaluation, and safeguards