Instructions to use c4tdr0ut/grok-oss-Apollyon-24B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use c4tdr0ut/grok-oss-Apollyon-24B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="c4tdr0ut/grok-oss-Apollyon-24B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("c4tdr0ut/grok-oss-Apollyon-24B") model = AutoModelForCausalLM.from_pretrained("c4tdr0ut/grok-oss-Apollyon-24B", device_map="auto") 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-Apollyon-24B 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
- Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use c4tdr0ut/grok-oss-Apollyon-24B 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-Apollyon-24B:Q4_K_M # Run inference directly in the terminal: llama cli -hf c4tdr0ut/grok-oss-Apollyon-24B: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-Apollyon-24B:Q4_K_M # Run inference directly in the terminal: llama cli -hf c4tdr0ut/grok-oss-Apollyon-24B: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-Apollyon-24B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf c4tdr0ut/grok-oss-Apollyon-24B: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-Apollyon-24B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf c4tdr0ut/grok-oss-Apollyon-24B:Q4_K_M
Use Docker
docker model run hf.co/c4tdr0ut/grok-oss-Apollyon-24B:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use c4tdr0ut/grok-oss-Apollyon-24B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "c4tdr0ut/grok-oss-Apollyon-24B" # 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-Apollyon-24B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/c4tdr0ut/grok-oss-Apollyon-24B:Q4_K_M
- SGLang
How to use c4tdr0ut/grok-oss-Apollyon-24B 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-Apollyon-24B" \ --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-Apollyon-24B", "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-Apollyon-24B" \ --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-Apollyon-24B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use c4tdr0ut/grok-oss-Apollyon-24B with Ollama:
ollama run hf.co/c4tdr0ut/grok-oss-Apollyon-24B:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use c4tdr0ut/grok-oss-Apollyon-24B with Docker Model Runner:
docker model run hf.co/c4tdr0ut/grok-oss-Apollyon-24B:Q4_K_M
- Lemonade
How to use c4tdr0ut/grok-oss-Apollyon-24B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull c4tdr0ut/grok-oss-Apollyon-24B:Q4_K_M
Run and chat with the model
lemonade run user.grok-oss-Apollyon-24B-Q4_K_M
List all available models
lemonade list
- Atomic Chat
This is from the real xai team???
This is something I cannot get over, fuck. Damn thing that gets my quota empty for the first time.
Maybe you should release a technical report and the dataset. This is the first time I ever had the patience to read every word of the README. lol.
I'm considering training audio layers on top of this. 'Tis gonna be insane with a real mouth.
The READMEs were generated using DeepSeek V3 Flash to ensure clarity and consistency. The dataset consists of approximately 1,300 conversations collected directly from the Grok app by engaging in ongoing discussions on a wide range of topics to promote generalization. Once I have gathered sufficient data, I export the conversations, apply light filtering, and fine-tune the models on affordable or free GPUs via Modal Cloud. I do not incorporate reasoning LLMs, as the source data from the app is non-reasoning in nature, which would introduce unnecessary complexity and misalignment. I may release a subset of the data in the future, but I consider the full collection my intellectual property and prefer to share only a limited taste with the community.
btw how was the gaokao uni entrance exam.
if my models get your quota empty why not just run it locally?