lugha1 is an instruction-tuned and preference-aligned conversational language model derived from SmolLM-1.7B-Instruct.
It is optimized for clear instruction following, polite dialogue, and socially grounded responses, with an emphasis on small-talk, explanations, and helpful conversational tone.
The model was trained using Supervised Fine-Tuning (SFT) and further aligned using Direct Preference Optimization (DPO) with parameter-efficient fine-tuning (LoRA).
Model Details
Base model: HuggingFaceTB/SmolLM-1.7B-Instruct
Architecture: Decoder-only Transformer
Parameters: ~1.7B (LoRA-adapted)
Fine-tuning method:
QLoRA (4-bit quantization)
LoRA (PEFT)
SFT → DPO alignment
Precision: 4-bit (training), fp16 compute
Language(s): English (primary); conversational multilingual robustness inherited from base model
Model name:lugha1
Training Procedure
Instruction Tuning (SFT)
The model was first trained using supervised instruction–response pairs, formatted in a conversational style:
After SFT, the model was aligned using Direct Preference Optimization (DPO).
Preference pairs (chosen vs. rejected responses) were used to bias the model toward:
more helpful answers
clearer explanations
socially appropriate tone
DPO was chosen over RLHF to ensure:
training stability
reproducibility
low computational overhead
Datasets
Training data consisted of instruction-style conversational datasets inspired by Smalltalk-style interactions, including:
General instruction-following
Explanatory responses
Conversational and polite dialogue patterns
Note: Some preference data was synthetically generated for alignment purposes.
Intended Use
lugha1 is suitable for:
Instruction-following chatbots
Educational assistants
Conversational agents
Prototyping aligned LLM systems
Research on small, efficient instruction-tuned models
Example use cases:
Question answering
Explanations
Friendly dialogue
Writing assistance
Out-of-Scope Use
This model is not intended for:
Medical or legal advice
High-stakes decision-making
Fully autonomous agents without human oversight
Safety-critical systems
Bias, Risks, and Limitations
The model may reflect biases present in the training data.
Preference alignment improves tone and helpfulness but does not guarantee factual correctness.
Like all language models, it may hallucinate when uncertain.
Users should apply appropriate validation and human oversight.
Ethical Considerations
No personal data was intentionally included in training.
The model was trained using publicly available or synthetic instruction data.
Alignment techniques were used to encourage polite and cooperative behavior.
