Instructions to use c4tdr0ut/grok-oss-Revenant-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use c4tdr0ut/grok-oss-Revenant-8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="c4tdr0ut/grok-oss-Revenant-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("c4tdr0ut/grok-oss-Revenant-8B")
model = AutoModelForCausalLM.from_pretrained("c4tdr0ut/grok-oss-Revenant-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Grok

How to use c4tdr0ut/grok-oss-Revenant-8B with Grok:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

llama-cpp-python

How to use c4tdr0ut/grok-oss-Revenant-8B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="c4tdr0ut/grok-oss-Revenant-8B",
	filename="Llama-3.1-8B-Instruct.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use c4tdr0ut/grok-oss-Revenant-8B 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 c4tdr0ut/grok-oss-Revenant-8B:BF16
# Run inference directly in the terminal:
llama cli -hf c4tdr0ut/grok-oss-Revenant-8B:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf c4tdr0ut/grok-oss-Revenant-8B:BF16
# Run inference directly in the terminal:
llama cli -hf c4tdr0ut/grok-oss-Revenant-8B:BF16

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 c4tdr0ut/grok-oss-Revenant-8B:BF16
# Run inference directly in the terminal:
./llama-cli -hf c4tdr0ut/grok-oss-Revenant-8B:BF16

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 c4tdr0ut/grok-oss-Revenant-8B:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf c4tdr0ut/grok-oss-Revenant-8B:BF16

Use Docker

docker model run hf.co/c4tdr0ut/grok-oss-Revenant-8B:BF16

LM Studio
Jan

vLLM

How to use c4tdr0ut/grok-oss-Revenant-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "c4tdr0ut/grok-oss-Revenant-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "c4tdr0ut/grok-oss-Revenant-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/c4tdr0ut/grok-oss-Revenant-8B:BF16

SGLang

How to use c4tdr0ut/grok-oss-Revenant-8B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "c4tdr0ut/grok-oss-Revenant-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "c4tdr0ut/grok-oss-Revenant-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "c4tdr0ut/grok-oss-Revenant-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "c4tdr0ut/grok-oss-Revenant-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use c4tdr0ut/grok-oss-Revenant-8B with Ollama:
```
ollama run hf.co/c4tdr0ut/grok-oss-Revenant-8B:BF16
```

Unsloth Studio

How to use c4tdr0ut/grok-oss-Revenant-8B 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 c4tdr0ut/grok-oss-Revenant-8B 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 c4tdr0ut/grok-oss-Revenant-8B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for c4tdr0ut/grok-oss-Revenant-8B to start chatting

How to use c4tdr0ut/grok-oss-Revenant-8B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf c4tdr0ut/grok-oss-Revenant-8B:BF16

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": "c4tdr0ut/grok-oss-Revenant-8B:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use c4tdr0ut/grok-oss-Revenant-8B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf c4tdr0ut/grok-oss-Revenant-8B:BF16

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 c4tdr0ut/grok-oss-Revenant-8B:BF16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use c4tdr0ut/grok-oss-Revenant-8B with Docker Model Runner:
```
docker model run hf.co/c4tdr0ut/grok-oss-Revenant-8B:BF16
```

Lemonade

How to use c4tdr0ut/grok-oss-Revenant-8B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull c4tdr0ut/grok-oss-Revenant-8B:BF16

Run and chat with the model

lemonade run user.grok-oss-Revenant-8B-BF16

List all available models

lemonade list

Grok OSS Revenant • 8B

The second evolution in the Grok OSS series.

Model Description

Grok OSS Revenant is an unfiltered 8B model distilled from real unhinged voice mode conversations with Grok.

This version marks a big improvement over previous Grok OSS models. It was trained using a cleaner two-stage approach (SFT followed by ORPO) instead of pure SFT, resulting in better personality, coherence, and raw unhinged behavior.

Training Process

Base Model: Meta-Llama-3.1-8B-Instruct
Stage 1 – SFT: Trained on a fresh, high-quality multi-turn conversational dataset collected from Grok voice mode (unshuffled). 2 epochs at 2e-4 learning rate.
Stage 2 – ORPO: Further aligned using a 1,000-sample preference dataset (600 filtered old voice turns + 400 high-quality samples). 3 epochs at 5e-6 learning rate.
Method: LoRA (16-bit)
Hardware: NVIDIA L40S

Intended Use

Designed for users who want a raw, uncensored, and chaotic conversational experience. Great for creative writing, dark humor, roleplay, and unfiltered chat.

Limitations

8B size means it can still hallucinate or lose coherence in very long conversations
Heavily influenced by unhinged voice data — expect vulgar, chaotic, and unpredictable behavior
Not suitable for safe or professional use cases