Instructions to use Snowflake/snowflake-arctic-embed-m-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Snowflake/snowflake-arctic-embed-m-v1.5 with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Snowflake/snowflake-arctic-embed-m-v1.5")

sentences = [
    "That is a happy person",
    "That is a happy dog",
    "That is a very happy person",
    "Today is a sunny day"
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

Transformers.js

How to use Snowflake/snowflake-arctic-embed-m-v1.5 with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('sentence-similarity', 'Snowflake/snowflake-arctic-embed-m-v1.5');

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Snowflake/snowflake-arctic-embed-m-v1.5 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 Snowflake/snowflake-arctic-embed-m-v1.5:BF16
# Run inference directly in the terminal:
llama cli -hf Snowflake/snowflake-arctic-embed-m-v1.5:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Snowflake/snowflake-arctic-embed-m-v1.5:BF16
# Run inference directly in the terminal:
llama cli -hf Snowflake/snowflake-arctic-embed-m-v1.5:BF16

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 Snowflake/snowflake-arctic-embed-m-v1.5:BF16
# Run inference directly in the terminal:
./llama-cli -hf Snowflake/snowflake-arctic-embed-m-v1.5:BF16

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 Snowflake/snowflake-arctic-embed-m-v1.5:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Snowflake/snowflake-arctic-embed-m-v1.5:BF16

Use Docker

docker model run hf.co/Snowflake/snowflake-arctic-embed-m-v1.5:BF16

LM Studio
Jan
Ollama
How to use Snowflake/snowflake-arctic-embed-m-v1.5 with Ollama:
```
ollama run hf.co/Snowflake/snowflake-arctic-embed-m-v1.5:BF16
```

Unsloth Studio

How to use Snowflake/snowflake-arctic-embed-m-v1.5 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 Snowflake/snowflake-arctic-embed-m-v1.5 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 Snowflake/snowflake-arctic-embed-m-v1.5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Snowflake/snowflake-arctic-embed-m-v1.5 to start chatting

Docker Model Runner
How to use Snowflake/snowflake-arctic-embed-m-v1.5 with Docker Model Runner:
```
docker model run hf.co/Snowflake/snowflake-arctic-embed-m-v1.5:BF16
```

Lemonade

How to use Snowflake/snowflake-arctic-embed-m-v1.5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Snowflake/snowflake-arctic-embed-m-v1.5:BF16

Run and chat with the model

lemonade run user.snowflake-arctic-embed-m-v1.5-BF16

List all available models

lemonade list

Atomic Chat

Upload GGUF files

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