Downloads · 30 days
91
8% of all-time downloads
cstr/arctic-embed-xs-GGUF
arctic-embed-xs-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 mit.
GGUF format of Snowflake/snowflake-arctic-embed-xs for use with CrispEmbed and Ollama.
Downloads · 30 days
91
8% of all-time downloads
All-time downloads
1.2K
Public
Repo size
363 MB
Likes
0
Public
Click a slice to open those files.
.gguf265 MB · 100%
From the Hugging Face model README
GGUF format of Snowflake/snowflake-arctic-embed-xs for use with CrispEmbed and Ollama.
| File | Quantization | Size |
|---|---|---|
| arctic-embed-xs-f32.gguf | F32 | 0 MB |
| arctic-embed-xs-q4_k.gguf | Q4_K | 0 MB |
| arctic-embed-xs-q8_0.gguf | Q8_0 | 0 MB |
| arctic-embed-xs.gguf | F32 | 0 MB |
Recommended: Q8_0 for quality (cos vs HF: 0.9999), Q4_K for size (0.995).
./crispembed -m arctic-embed-xs "Hello world"
./crispembed-server -m arctic-embed-xs --port 8080
# Create model
echo "FROM arctic-embed-xs-q8_0.gguf" > Modelfile
ollama create arctic-embed-xs -f Modelfile
# Embed
curl http://localhost:11434/api/embed -d '{"model":"arctic-embed-xs","input":["Hello world"]}'
from crispembed import CrispEmbed
model = CrispEmbed("arctic-embed-xs-q8_0.gguf")
vectors = model.encode(["Hello world", "Goodbye world"])
| Property | Value |
|---|---|
| Architecture | BERT |
| Parameters | 22M |
| Embedding Dimension | 384 |
| Layers | 6 |
| Pooling | CLS |
| Tokenizer | WordPiece |
| Language | en |
| Q8_0 vs HuggingFace | 0.9999 |
| Q4_K vs HuggingFace | 0.995 |
CrispEmbed server supports four API dialects:
POST /embed — nativePOST /v1/embeddings — OpenAI-compatiblePOST /api/embed — Ollama-compatiblePOST /api/embeddings — Ollama legacySnowflake.mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not.