Instructions to use c4tdr0ut/grok-oss-Revenant-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use c4tdr0ut/grok-oss-Revenant-70B with Transformers:

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

pipe = pipeline("text-generation", model="c4tdr0ut/grok-oss-Revenant-70B")
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-70B")
model = AutoModelForCausalLM.from_pretrained("c4tdr0ut/grok-oss-Revenant-70B")
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-70B 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-70B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="c4tdr0ut/grok-oss-Revenant-70B",
	filename="meta-llama-3.1-70b-instruct.Q4_K_M.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-70B 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-70B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf c4tdr0ut/grok-oss-Revenant-70B:Q4_K_M

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-70B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf c4tdr0ut/grok-oss-Revenant-70B: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 c4tdr0ut/grok-oss-Revenant-70B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf c4tdr0ut/grok-oss-Revenant-70B: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 c4tdr0ut/grok-oss-Revenant-70B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf c4tdr0ut/grok-oss-Revenant-70B:Q4_K_M

Use Docker

docker model run hf.co/c4tdr0ut/grok-oss-Revenant-70B:Q4_K_M

LM Studio
Jan

vLLM

How to use c4tdr0ut/grok-oss-Revenant-70B 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-70B"
# 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-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/c4tdr0ut/grok-oss-Revenant-70B:Q4_K_M

SGLang

How to use c4tdr0ut/grok-oss-Revenant-70B 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-70B" \
    --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-70B",
		"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-70B" \
        --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-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use c4tdr0ut/grok-oss-Revenant-70B with Ollama:
```
ollama run hf.co/c4tdr0ut/grok-oss-Revenant-70B:Q4_K_M
```

Unsloth Studio

How to use c4tdr0ut/grok-oss-Revenant-70B 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-70B 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-70B 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-70B to start chatting

How to use c4tdr0ut/grok-oss-Revenant-70B 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-70B: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": "c4tdr0ut/grok-oss-Revenant-70B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use c4tdr0ut/grok-oss-Revenant-70B 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-70B: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 c4tdr0ut/grok-oss-Revenant-70B:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use c4tdr0ut/grok-oss-Revenant-70B with Docker Model Runner:
```
docker model run hf.co/c4tdr0ut/grok-oss-Revenant-70B:Q4_K_M
```

Lemonade

How to use c4tdr0ut/grok-oss-Revenant-70B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull c4tdr0ut/grok-oss-Revenant-70B:Q4_K_M

Run and chat with the model

lemonade run user.grok-oss-Revenant-70B-Q4_K_M

List all available models

lemonade list

Grok OSS Revenant • 70B

The large-scale version of the Grok OSS Revenant series.

Model Description

Grok OSS Revenant 70B is the 70 billion parameter model in the Grok OSS Revenant series. Following the exact same training process as the 8B version, this model was created by distilling highly unfiltered, raw conversations from Grok's voice mode.

Thanks to its much larger scale, it offers significantly better reasoning, coherence, and instruction following while keeping the same raw, unhinged personality.

Training Process

This model was trained in two stages:

Stage 1 – Supervised Fine-Tuning (SFT):
Trained on a high-quality multi-turn conversational dataset collected from Grok voice mode.
Stage 2 – ORPO:
Further trained using a 1,000-sample preference dataset consisting of filtered voice conversations and high-quality alignment data.

Training Details:

QLoRA (4-bit)
Same training recipe as the 8B version
Trained for 2 hours on NVIDIA B200

Intended Use

This model is designed for users who want both maximum capability and an unfiltered, direct personality. It excels at complex reasoning while staying raw and uncensored.