Instructions to use cstr/arctic-embed-l-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use cstr/arctic-embed-l-v2-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cstr/arctic-embed-l-v2-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf cstr/arctic-embed-l-v2-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cstr/arctic-embed-l-v2-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf cstr/arctic-embed-l-v2-GGUF:IQ4_XS

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf cstr/arctic-embed-l-v2-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf cstr/arctic-embed-l-v2-GGUF:IQ4_XS

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf cstr/arctic-embed-l-v2-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cstr/arctic-embed-l-v2-GGUF:IQ4_XS

Use Docker

docker model run hf.co/cstr/arctic-embed-l-v2-GGUF:IQ4_XS

LM Studio
Jan
Ollama
How to use cstr/arctic-embed-l-v2-GGUF with Ollama:
```
ollama run hf.co/cstr/arctic-embed-l-v2-GGUF:IQ4_XS
```

Unsloth Studio

How to use cstr/arctic-embed-l-v2-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cstr/arctic-embed-l-v2-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cstr/arctic-embed-l-v2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cstr/arctic-embed-l-v2-GGUF to start chatting

Docker Model Runner
How to use cstr/arctic-embed-l-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/cstr/arctic-embed-l-v2-GGUF:IQ4_XS
```

Lemonade

How to use cstr/arctic-embed-l-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cstr/arctic-embed-l-v2-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.arctic-embed-l-v2-GGUF-IQ4_XS

List all available models

lemonade list

Atomic Chat

arctic-embed-l-v2-GGUF / README.md

cstr

docs: add provenance / EU AI Act Art. 53 note

2b74c63 verified 4 days ago

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metadata

license: apache-2.0
language:
  - en
tags:
  - embeddings
  - gguf
  - ggml
  - text-embeddings
  - xlm-r
  - crispembed
  - ollama
pipeline_tag: feature-extraction
base_model: Snowflake/snowflake-arctic-embed-l-v2.0

arctic-embed-l-v2 GGUF

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

Files

File	Quantization	Size
arctic-embed-l-v2-q4_k.gguf	Q4_K	0 MB
arctic-embed-l-v2-q8_0.gguf	Q8_0	0 MB
arctic-embed-l-v2.gguf	F32	0 MB

Recommended: Q8_0 for quality (cos vs HF: L2=1.0), Q4_K for size (L2=1.0).

Quick Start

CrispEmbed

./crispembed -m arctic-embed-l-v2 "Hello world"
./crispembed-server -m arctic-embed-l-v2 --port 8080

Ollama (with CrispStrobe fork)

echo "FROM arctic-embed-l-v2-q8_0.gguf" > Modelfile
ollama create arctic-embed-l-v2 -f Modelfile
curl http://localhost:11434/api/embed -d '{"model":"arctic-embed-l-v2","input":["Hello world"]}'

Python (CrispEmbed)

from crispembed import CrispEmbed
model = CrispEmbed("arctic-embed-l-v2-q8_0.gguf")
vectors = model.encode(["Hello world", "Goodbye world"])

Model Details

Property	Value
Architecture	XLM-R
Parameters	560M
Embedding Dimension	1024
Layers	24
Pooling	CLS
Tokenizer	SentencePiece
Language	en
Q8_0 vs HuggingFace	L2=1.0
Q4_K vs HuggingFace	L2=1.0

Server API

CrispEmbed server supports four API dialects:

POST /embed -- native
POST /v1/embeddings -- OpenAI-compatible
POST /api/embed -- Ollama-compatible
POST /api/embeddings -- Ollama legacy

Credits

Original model: Snowflake/snowflake-arctic-embed-l-v2.0
Inference: CrispEmbed (MIT, ggml-based)

Provenance and EU AI Act Art. 53 note

Upstream model: Snowflake/snowflake-arctic-embed-l-v2.0 — published by Snowflake.
Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository. No training-content summary was found on the upstream model card at the time of writing; that documentation gap is upstream's and is not filled here.
Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.