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 / arctic-embed-l-v2.gguf

cstr

Update F32 GGUF with Ollama-compatible tensor names and metadata

998bb40 verified 4 months ago

Download

History Blame GGUF Editor new Delete

2.27 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 26475af29c403167edd1f235a40c3d9edb02e61e695e699b09d92c162d47a7fe
Size of remote file:: 2.27 GB
SHA256:: e3ffc975fd42b7fca5d43d3329a6704fa28533581593287577bae63daf93ad38

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.