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

cstr commited on 5 days ago

Commit

2b74c63

verified ·

1 Parent(s): 99b7859

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

Browse files

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README.md +8 -0

README.md CHANGED Viewed

@@ -68,3 +68,11 @@ CrispEmbed server supports four API dialects:
 - Original model: [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0)
 - Inference: [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed) (MIT, ggml-based)

 - Original model: [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0)
 - Inference: [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed) (MIT, ggml-based)
+## Provenance and EU AI Act Art. 53 note
+- **Upstream model:** [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/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.