Instructions to use Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Nabbers1999/Melpomene-70B-0307-Uncensored-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 Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nabbers1999/Melpomene-70B-0307-Uncensored-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 Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nabbers1999/Melpomene-70B-0307-Uncensored-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 Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Nabbers1999/Melpomene-70B-0307-Uncensored-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 Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF with Ollama:
```
ollama run hf.co/Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF:Q4_K_M
```

Unsloth Studio

How to use Nabbers1999/Melpomene-70B-0307-Uncensored-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 Nabbers1999/Melpomene-70B-0307-Uncensored-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 Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF to start chatting

How to use Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nabbers1999/Melpomene-70B-0307-Uncensored-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": "Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nabbers1999/Melpomene-70B-0307-Uncensored-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 "Nabbers1999/Melpomene-70B-0307-Uncensored-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 Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF:Q4_K_M
```

Lemonade

How to use Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Melpomene-70B-0307-Uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Nabbers1999/Melpomene-70B-0307-Uncensored-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 Nabbers1999/Melpomene-70B-0307-Uncensored-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 Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Melpomene 70B - Uncensored - GGUF

Thalia is a chat model I have merged to provide a distillation model for future projects. Combining a Lumimaid model with Strawberry Lemonade over a heavy base of Deepseek R1 Distill Llama 70B has produced a thinking model and healed its safety alignments. This model contains the writing creativity of its two chat model parents, while adding the deep reasoning of Deepseek.

Melpomene is the same model as Thalia, abliterated via orthogonalization and reinforced via direction-only DoRA training. Use at your own risk.

In order for this model to function properly, you should prefill the opening `<think>` tag. This model's ancestry results in a hybrid thinker that sometimes chooses to think without `<think>` tags.

Merge Details

This is a merge of pre-trained language models created using mergekit.

Merge Method

This model was merged using the DARE TIES merge method using unsloth/Llama-3.3-70B-Instruct as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
    parameters:
      density: 0.8
      weight: 1.0
  - model: sophosympatheia/Strawberrylemonade-L3-70B-v1.1
    parameters:
      density: 0.5
      weight: 0.4
  - model: NeverSleep/Lumimaid-v0.2-70B
    parameters:
      density: 0.5
      weight: 0.4
merge_method: dare_ties
base_model: unsloth/Llama-3.3-70B-Instruct
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16

Downloads last month: 169

GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

2-bit

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16-bit

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Paper for Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF

Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

Paper • 2311.03099 • Published Nov 6, 2023 • 36

Melpomene 70B - Uncensored - GGUF

In order for this model to function properly, you should prefill the opening <think> tag. This model's ancestry results in a hybrid thinker that sometimes chooses to think without <think> tags.

Merge Details

Merge Method

Models Merged

Configuration

Model tree for Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF

Collection including Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF

Paper for Nabbers1999/Melpomene-70B-0307-Uncensored-GGUF

In order for this model to function properly, you should prefill the opening `<think>` tag. This model's ancestry results in a hybrid thinker that sometimes chooses to think without `<think>` tags.