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| File | Quantization | Size |
|---|---|---|
| harrier-0.6b-q4_k.gguf | Q4_K | 0 MB |
| harrier-0.6b-q8_0.gguf | Q8_0 | 0 MB |
| harrier-0.6b.gguf | F32 | 0 MB |
1./crispembed -m harrier-0.6b "Hello world"
2./crispembed-server -m harrier-0.6b --port 80801echo "FROM harrier-0.6b-q8_0.gguf" > Modelfile
2ollama create harrier-0.6b -f Modelfile
3curl http://localhost:11434/api/embed -d '{"model":"harrier-0.6b","input":["Hello world"]}'1from crispembed import CrispEmbed
2model = CrispEmbed("harrier-0.6b-q8_0.gguf")
3vectors = model.encode(["Hello world", "Goodbye world"])| Property | Value |
|---|---|
| Architecture | Qwen3 |
| Parameters | 600M |
| Embedding Dimension | 1024 |
| Layers | 28 |
| Pooling | last-token |
| Tokenizer | BPE |
| Language | multilingual |
| Q8_0 vs HuggingFace | L2=1.0 |
| Q4_K vs HuggingFace | L2=1.0 |
POST /embed -- nativePOST /v1/embeddings -- OpenAI-compatiblePOST /api/embed -- Ollama-compatiblePOST /api/embeddings -- Ollama legacymicrosoft.mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not.