cstr / arctic-embed-m-v2-GGUF

huggingface.co
Total runs: 241
24-hour runs: 1
7-day runs: -128
30-day runs: -319
Model's Last Updated: August 05 2026
feature-extraction

Introduction of arctic-embed-m-v2-GGUF

Model Details of arctic-embed-m-v2-GGUF

arctic-embed-m-v2 GGUF

GGUF format of Snowflake/snowflake-arctic-embed-m-v2.0 for use with CrispEmbed .

Snowflake Arctic Embed M v2.0. Multilingual retrieval embeddings, 768-dimensional, 8192-token context. Queries take the prefix "query: "; documents take no prefix.

Files
File Quantization Size
arctic-embed-m-v2-q4_k.gguf Q4_K 261 MB
arctic-embed-m-v2-q8_0.gguf Q8_0 315 MB
arctic-embed-m-v2.gguf F32 1170 MB
Quick Start
# Download
huggingface-cli download cstr/arctic-embed-m-v2-GGUF arctic-embed-m-v2-q4_k.gguf --local-dir .

# Run with CrispEmbed
./crispembed -m arctic-embed-m-v2-q4_k.gguf "Hello world"

# Or with auto-download
./crispembed -m arctic-embed-m-v2 "Hello world"
Model Details
Property Value
Architecture GTE v1.5 (RoPE, GeGLU, post-LN)
Parameters 305M
Embedding Dimension 768
Layers 12
Pooling CLS
Tokenizer SentencePiece
Base Model Snowflake/snowflake-arctic-embed-m-v2.0
Verification

Verified bit-identical to HuggingFace sentence-transformers (cosine similarity >= 0.999 on test texts).

Usage with CrispEmbed

CrispEmbed is a lightweight C/C++ text embedding inference engine using ggml. No Python runtime, no ONNX. Supports BERT, XLM-R, Qwen3, and Gemma3 architectures.

# Build CrispEmbed
git clone https://github.com/CrispStrobe/CrispEmbed
cd CrispEmbed
cmake -S . -B build && cmake --build build -j

# Encode
./build/crispembed -m arctic-embed-m-v2-q4_k.gguf "query text"

# Server mode
./build/crispembed-server -m arctic-embed-m-v2-q4_k.gguf --port 8080
curl -X POST http://localhost:8080/v1/embeddings \
    -d '{"input": ["Hello world"], "model": "arctic-embed-m-v2"}'
Credits

Runs of cstr arctic-embed-m-v2-GGUF on huggingface.co

241
Total runs
1
24-hour runs
-108
3-day runs
-128
7-day runs
-319
30-day runs

More Information About arctic-embed-m-v2-GGUF huggingface.co Model

More arctic-embed-m-v2-GGUF license Visit here:

https://choosealicense.com/licenses/apache-2.0

arctic-embed-m-v2-GGUF huggingface.co

arctic-embed-m-v2-GGUF huggingface.co is an AI model on huggingface.co that provides arctic-embed-m-v2-GGUF's model effect (), which can be used instantly with this cstr arctic-embed-m-v2-GGUF model. huggingface.co supports a free trial of the arctic-embed-m-v2-GGUF model, and also provides paid use of the arctic-embed-m-v2-GGUF. Support call arctic-embed-m-v2-GGUF model through api, including Node.js, Python, http.

arctic-embed-m-v2-GGUF huggingface.co Url

https://huggingface.co/cstr/arctic-embed-m-v2-GGUF

cstr arctic-embed-m-v2-GGUF online free

arctic-embed-m-v2-GGUF huggingface.co is an online trial and call api platform, which integrates arctic-embed-m-v2-GGUF's modeling effects, including api services, and provides a free online trial of arctic-embed-m-v2-GGUF, you can try arctic-embed-m-v2-GGUF online for free by clicking the link below.

cstr arctic-embed-m-v2-GGUF online free url in huggingface.co:

https://huggingface.co/cstr/arctic-embed-m-v2-GGUF

arctic-embed-m-v2-GGUF install

arctic-embed-m-v2-GGUF is an open source model from GitHub that offers a free installation service, and any user can find arctic-embed-m-v2-GGUF on GitHub to install. At the same time, huggingface.co provides the effect of arctic-embed-m-v2-GGUF install, users can directly use arctic-embed-m-v2-GGUF installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

arctic-embed-m-v2-GGUF install url in huggingface.co:

https://huggingface.co/cstr/arctic-embed-m-v2-GGUF

Url of arctic-embed-m-v2-GGUF

arctic-embed-m-v2-GGUF huggingface.co Url

Provider of arctic-embed-m-v2-GGUF huggingface.co

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