Documentation · Documentation
Models
The models Kjarni can download by name, the architectures it implements, and how embedding models truncate long text.
The registry below is what kjarni model list shows, and the names are what every binding
accepts. Chat models are also available as GGUF downloads, which are smaller.
| Task | Model | Parameters |
|---|---|---|
| Embeddings | minilm-l6-v2 (default) | 22M |
mpnet-base-v2 | 110M | |
nomic-embed-text | 137M | |
bge-m3 (multilingual) | 567M | |
distilbert-base | 66M | |
| Reranking | minilm-l6-v2-cross-encoder | 22M |
| Classification | distilbert-sentiment (positive/negative) | 66M |
roberta-sentiment (negative/neutral/positive) | 125M | |
bert-sentiment-multilingual (1 to 5 stars) | 168M | |
toxic-bert (six toxicity labels) | 110M | |
roberta-emotions (28 emotions, multi-label) | 125M | |
distilroberta-emotion (7 emotions) | 82M | |
| Chat | qwen2.5-0.5b-instruct | 0.5B |
qwen2.5-1.5b-instruct | 1.5B | |
llama3.2-1b-instruct | 1.2B | |
llama3.2-3b-instruct | 3.2B | |
phi3.5-mini-instruct | 3.8B | |
mistral-7b-instruct | 7.2B | |
llama3.1-8b-instruct | 8.0B | |
deepseek-r1-8b | 8.0B | |
| Text generation | distilgpt2, gpt2 | 82M, 117M |
| Summarization | distilbart-cnn, bart-large-cnn | 306M, 406M |
| Instruction / translation | flan-t5-base, flan-t5-large | 250M, 780M |
| Speech-to-text | whisper-small, whisper-large-v3 | 244M, 1.6B |
| Image and text embeddings | clip-vit-base-32 | 151M |
The engine implements these architecture families: BERT, DistilBERT, RoBERTa, XLM-RoBERTa,
MPNet and Nomic-BERT encoders; Llama, Qwen 2, Phi 3, Mistral and GPT-2 decoders; T5, BART
and Whisper encoder-decoders; and CLIP. A model outside the registry can be loaded from a
local folder (model_path in the binding configs, --model-path on the command line)
provided it uses one of those architectures. Weights load from HuggingFace safetensors or
GGUF as published; there is no conversion step.
Embedding models truncate at the length their sentence-transformers config specifies, the
same as the Python reference: 256 tokens for minilm-l6-v2 and 384 for mpnet-base-v2.
The 256-token limit explains what that means for chunking.
Kjarni runs the same engine on every platform: