Instructions to use mmnga/Meta-Llama-3-70B-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/Meta-Llama-3-70B-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/Meta-Llama-3-70B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mmnga/Meta-Llama-3-70B-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/Meta-Llama-3-70B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mmnga/Meta-Llama-3-70B-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/Meta-Llama-3-70B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mmnga/Meta-Llama-3-70B-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/Meta-Llama-3-70B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mmnga/Meta-Llama-3-70B-Instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/mmnga/Meta-Llama-3-70B-Instruct-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use mmnga/Meta-Llama-3-70B-Instruct-gguf with Ollama:
```
ollama run hf.co/mmnga/Meta-Llama-3-70B-Instruct-gguf:Q4_K_M
```

Unsloth Studio

How to use mmnga/Meta-Llama-3-70B-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/Meta-Llama-3-70B-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/Meta-Llama-3-70B-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/Meta-Llama-3-70B-Instruct-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use mmnga/Meta-Llama-3-70B-Instruct-gguf with Docker Model Runner:
```
docker model run hf.co/mmnga/Meta-Llama-3-70B-Instruct-gguf:Q4_K_M
```

Lemonade

How to use mmnga/Meta-Llama-3-70B-Instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mmnga/Meta-Llama-3-70B-Instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Meta-Llama-3-70B-Instruct-gguf-Q4_K_M

List all available models

lemonade list

Meta-Llama-3-70B-Instruct-gguf

meta-llamaさんが公開しているMeta-Llama-3-70B-Instructのggufフォーマット変換版です。

eot_id対応してます。 imatrixのデータはTFMC/imatrix-dataset-for-japanese-llmを使用して作成しました。

分割されたファイルについて

q6_kやq8_0のファイルはサイズが大きく分割されているので結合する必要があります。

cat Meta-Llama-3-70B-Instruct-Q5_K_M.gguf.* > Meta-Llama-3-70B-Instruct-Q5_K_M.gguf

Usage

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'Meta-Llama-3-70B-Instruct-Q4_0.gguf' -p "<|begin_of_text|><|start_header_id|>user <|end_header_id|>\n\nこんにちわ<|eot_id|><|start_header_id|>assistant <|end_header_id|>\n\n" -n 128

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GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

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mmnga
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Meta-Llama-3-70B-Instruct-gguf

Meta-Llama-3-70B-Instruct-gguf

分割されたファイルについて

Usage

Dataset used to train mmnga/Meta-Llama-3-70B-Instruct-gguf