Instructions to use mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-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/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-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/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-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/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-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/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-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 mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-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 mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF to start chatting

How to use mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mradermacher/Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Llama-3.1-8B-Instruct-Jbliterated-v2-GGUF / Llama-3.1-8B-Instruct-Jbliterated-v2.Q6_K.gguf

mradermacher

uploaded from marco

a4a2a88 verified 24 days ago

Download

History Blame GGUF Editor new Delete

6.6 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:: 4b169cf41cfcf7de9a32f84d5be2920b6c104af614393560c349b98cfebddbe9
Size of remote file:: 6.6 GB
SHA256:: d23d93f39fc4e00960e2654525ce17dfae9574aab925de995fcc77d5e822c7fb

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