Instructions to use mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mlabonne/Hermes-3-Llama-3.1-8B-lorablated-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 mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mlabonne/Hermes-3-Llama-3.1-8B-lorablated-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 mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mlabonne/Hermes-3-Llama-3.1-8B-lorablated-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 mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mlabonne/Hermes-3-Llama-3.1-8B-lorablated-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 mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF with Ollama:
```
ollama run hf.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF with Docker Model Runner:
```
docker model run hf.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF:Q4_K_M
```

Lemonade

How to use mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mlabonne/Hermes-3-Llama-3.1-8B-lorablated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Hermes-3-Llama-3.1-8B-lorablated-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

🪽 Hermes-3-Llama-3.1-8B-lorablated

This is an uncensored version of NousResearch/Hermes-3-Llama-3.1-8B using lorablation.

You can see in the following example how Hermes 3 refuses to answer a legitimate question while the abliterated model complies:

The recipe is simple:

Extraction: We extract a LoRA adapter by comparing two models: a censored Llama 3.1 (meta-llama/Meta-Llama-3-8B-Instruct) and an abliterated Llama 3.1 (mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated).
Merge: We merge this new LoRA adapter using task arithmetic to the censored NousResearch/Hermes-3-Llama-3.1-8B to abliterate it.

See this article to learn more about abliteration.

🧩 Configuration

This model was merged using the task arithmetic merge method using NousResearch/Hermes-3-Llama-3.1-8B + Llama-3.1-8B-Instruct-abliterated-LORA as a base.

The following YAML configuration was used to produce this model:

base_model: NousResearch/Hermes-3-Llama-3.1-8B+Llama-3.1-8B-Instruct-abliterated-LORA
dtype: bfloat16
merge_method: task_arithmetic
parameters:
  normalize: false
slices:
- sources:
  - layer_range: [0, 32]
    model: NousResearch/Hermes-3-Llama-3.1-8B+Llama-3.1-8B-Instruct-abliterated-LORA
    parameters:
      weight: 1.0