Instructions to use mmnga/ELYZA-japanese-Llama-2-7b-instruct-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 mmnga/ELYZA-japanese-Llama-2-7b-instruct-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 mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf:Q4_K_M

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 mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf:Q4_K_M

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 mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf with Ollama:
```
ollama run hf.co/mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf:Q4_K_M
```

Unsloth Studio

How to use mmnga/ELYZA-japanese-Llama-2-7b-instruct-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 mmnga/ELYZA-japanese-Llama-2-7b-instruct-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 mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf to start chatting

Docker Model Runner
How to use mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf:Q4_K_M
```

Lemonade

How to use mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mmnga/ELYZA-japanese-Llama-2-7b-instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.ELYZA-japanese-Llama-2-7b-instruct-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

ELYZA-japanese-Llama-2-7b-instruct-gguf / ELYZA-japanese-Llama-2-7b-instruct-q8_0.gguf

mmnga

Upload ELYZA-japanese-Llama-2-7b-instruct-q8_0.gguf with huggingface_hub

3bdc4a7 almost 3 years ago

Download

History Blame GGUF Editor new Delete

7.16 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:: 688e35928d5aba7252a8635f07144afad59c2e28584bb6fe606c0250647e49c1
Size of remote file:: 7.16 GB
SHA256:: c695a2dd16e4527d60edb34d7bc22de3178ebc8393ad6f68255f7cbdd7148a59

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