A Nepali sentence-embedding model. It maps a Nepali sentence (Devanagari script) to a 768-dimensional vector, so that sentences with similar meaning end up close together under cosine similarity. It is a Sentence Transformers model fine-tuned from Rajan/NepaliBERT on syubraj/stsbnepali, a Nepali translation of the STS Benchmark. The model was trained to output a cosine similarity that matches the STS score divided by 5. A score near 1.0 means the two sentences say the same thing, and a score near 0.0 means they are unrelated. The pooling step is a mean over token embeddings, weighted by the attention mask. - Scoring how similar two Nepali sentences are. - Semantic search and duplicate or…
Open weights
82M parameters
512 tokens
sentence-transformers
A LoRA adapter for Qwen/Qwen2.5-0.5B-Instruct that turns a short user profile (age, gender, height, weight, activity level, dietary preference, daily calorie target) into a one-day meal plan with breakfast, lunch, snack and dinner. It is a small, fast, single-purpose model: about 2.2M trainable parameters on top of a 0.5B base, usable on CPU. The adapter was trained on one fixed prompt layout. Use the same system prompt and the same field names and order, otherwise quality drops. Two details matter: - Pass the system prompt explicitly. Without it, the chat template inserts Qwen's default system prompt, which the adapter never saw. - The first field is spelled Ages: in the training data, so…
Open weights
apache-2.0
peft
