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cstr/all-MiniLM-L6-v2-GGUF
all-MiniLM-L6-v2-GGUF is a feature extraction model from cstr. Use it when you need embeddings to search or compare text. The card lists the license as apache-2.0.
GGUF format of sentence-transformers/all-MiniLM-L6-v2 for use with CrispEmbed and Ollama.
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.gguf265 MB · 100%
From the Hugging Face model README
GGUF format of sentence-transformers/all-MiniLM-L6-v2 for use with CrispEmbed and Ollama.
| File | Quantization | Size |
|---|---|---|
| all-MiniLM-L6-v2-f32.gguf | F32 | 0 MB |
| all-MiniLM-L6-v2-q4_k.gguf | Q4_K | 0 MB |
| all-MiniLM-L6-v2-q8_0.gguf | Q8_0 | 0 MB |
| all-MiniLM-L6-v2.gguf | F32 | 0 MB |
Recommended: Q8_0 for quality (cos vs HF: 0.9998), Q4_K for size (0.970).
./crispembed -m all-MiniLM-L6-v2 "Hello world"
./crispembed-server -m all-MiniLM-L6-v2 --port 8080
# Create model
echo "FROM all-MiniLM-L6-v2-q8_0.gguf" > Modelfile
ollama create all-MiniLM-L6-v2 -f Modelfile
# Embed
curl http://localhost:11434/api/embed -d '{"model":"all-MiniLM-L6-v2","input":["Hello world"]}'
from crispembed import CrispEmbed
model = CrispEmbed("all-MiniLM-L6-v2-q8_0.gguf")
vectors = model.encode(["Hello world", "Goodbye world"])
| Property | Value |
|---|---|
| Architecture | BERT |
| Parameters | 22M |
| Embedding Dimension | 384 |
| Layers | 6 |
| Pooling | mean |
| Tokenizer | WordPiece |
| Language | en |
| Q8_0 vs HuggingFace | 0.9998 |
| Q4_K vs HuggingFace | 0.970 |
CrispEmbed server supports four API dialects:
POST /embed — nativePOST /v1/embeddings — OpenAI-compatiblePOST /api/embed — Ollama-compatiblePOST /api/embeddings — Ollama legacysentence-transformers.apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.