Model Details
- Architecture: Llama-style decoder-only causal LM
- Parameters: 10,390,784
- Layers: 6
- Hidden size: 256
- Attention heads: 4
- Vocabulary: 16,000 SentencePiece tokens
- Context length: 1,024
- Base checkpoint: Tara10M Colab base checkpoint
- SFT data: 2,000 cleaned synthetic Burmese-English instruction examples
Intended Use
Best test areas:
- short English to Burmese translation
- short Burmese to English translation
- simple Burmese rewrite
- travel phrasebook style prompts
Prompt format:
Instruction: Translate this sentence to Burmese.Input: I will go home tomorrow.Response:
Limitations
This model is very small and still repeats, drifts, and gives wrong translations. It should be used for experimentation only.
Known issues:
- weak factual reliability
- repeated phrases
- mixed Burmese/English output
- poor long-form generation
- not suitable for safety-critical use
Training Summary
SFT examples: 2,000Train examples: 1,800Validation examples: 200Max steps: 400Learning rate: 2e-5Best validation loss: 2.4228
Files
model.safetensors
config.json
tokenizer.model
tokenizer_config.json
special_tokens_map.json