Instructions to use grapevine-AI/Arcee-Blitz-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grapevine-AI/Arcee-Blitz-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="grapevine-AI/Arcee-Blitz-GGUF",
	filename="Arcee-Blitz-24B-BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use grapevine-AI/Arcee-Blitz-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf grapevine-AI/Arcee-Blitz-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf grapevine-AI/Arcee-Blitz-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf grapevine-AI/Arcee-Blitz-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf grapevine-AI/Arcee-Blitz-GGUF: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 grapevine-AI/Arcee-Blitz-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf grapevine-AI/Arcee-Blitz-GGUF: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 grapevine-AI/Arcee-Blitz-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grapevine-AI/Arcee-Blitz-GGUF:BF16

Use Docker

docker model run hf.co/grapevine-AI/Arcee-Blitz-GGUF:BF16

LM Studio
Jan
Ollama
How to use grapevine-AI/Arcee-Blitz-GGUF with Ollama:
```
ollama run hf.co/grapevine-AI/Arcee-Blitz-GGUF:BF16
```

Unsloth Studio new

How to use grapevine-AI/Arcee-Blitz-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 grapevine-AI/Arcee-Blitz-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 grapevine-AI/Arcee-Blitz-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for grapevine-AI/Arcee-Blitz-GGUF to start chatting

Docker Model Runner
How to use grapevine-AI/Arcee-Blitz-GGUF with Docker Model Runner:
```
docker model run hf.co/grapevine-AI/Arcee-Blitz-GGUF:BF16
```

Lemonade

How to use grapevine-AI/Arcee-Blitz-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grapevine-AI/Arcee-Blitz-GGUF:BF16

Run and chat with the model

lemonade run user.Arcee-Blitz-GGUF-BF16

List all available models

lemonade list

What is this?

arcee-aiがMistral-Small-24B-Instruct-2501にDeepSeek V3由来のデータを蒸留して作成した非思考モデル、Arcee-BlitzをGGUFフォーマットに変換したものです。

imatrix dataset

日本語能力を重視し、日本語が多量に含まれるTFMC/imatrix-dataset-for-japanese-llmデータセットを使用しました。
また、CUDA版llama.cppがbfloat16に対応したため、imatrixの算出は本来の数値精度であるBF16のモデルを使用して行いました。

Chat template

[SYSTEM_PROMPT] ここにSystem Promptを書きます。[/SYSTEM_PROMPT][INST] ここにMessageを書きます。[/INST]

Environment

Windows版llama.cpp-b4514およびllama.cpp-b4524同時リリースのconvert-hf-to-gguf.pyを使用して量子化作業を実施しました。

License

Apache 2.0

Developer

Mistral AI & arcee-ai

Downloads last month: 2

GGUF

Model size

24B params

Architecture

llama

Hardware compatibility

4-bit

16-bit

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