Instructions to use mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Llama3-8B-Instruct-Replete-Adapted-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 mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Llama3-8B-Instruct-Replete-Adapted-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 mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Llama3-8B-Instruct-Replete-Adapted-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 mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Llama3-8B-Instruct-Replete-Adapted-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 mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Llama3-8B-Instruct-Replete-Adapted-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama3-8B-Instruct-Replete-Adapted-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Llama3-8B-Instruct-Replete-Adapted-GGUF / Llama3-8B-Instruct-Replete-Adapted.Q4_K_M.gguf

mradermacher

uploaded from nethype/leia

12b81b1 verified about 2 years ago

Download

History Blame GGUF Editor new Delete

4.92 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:: b87091246d83b9be78376dd48ba09682c05701016272d94d57a78d48d499c1ec
Size of remote file:: 4.92 GB
SHA256:: 6c3d298181cf2cf2204ff52c66096bf97b6b300839a1096392d5aae156eb1d8f

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