Text Generation
Transformers
Safetensors
Russian
English
qwen3_omni_moe
text-to-audio
radon
ultra
agi
russian
english
conversation
conversational
custom_code
Instructions to use MagistrTheOne/Radon-35B-Ultra-X-RU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MagistrTheOne/Radon-35B-Ultra-X-RU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MagistrTheOne/Radon-35B-Ultra-X-RU", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("MagistrTheOne/Radon-35B-Ultra-X-RU", trust_remote_code=True) model = AutoModelForTextToWaveform.from_pretrained("MagistrTheOne/Radon-35B-Ultra-X-RU", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MagistrTheOne/Radon-35B-Ultra-X-RU with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MagistrTheOne/Radon-35B-Ultra-X-RU" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MagistrTheOne/Radon-35B-Ultra-X-RU", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MagistrTheOne/Radon-35B-Ultra-X-RU
- SGLang
How to use MagistrTheOne/Radon-35B-Ultra-X-RU 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 "MagistrTheOne/Radon-35B-Ultra-X-RU" \ --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": "MagistrTheOne/Radon-35B-Ultra-X-RU", "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 "MagistrTheOne/Radon-35B-Ultra-X-RU" \ --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": "MagistrTheOne/Radon-35B-Ultra-X-RU", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use MagistrTheOne/Radon-35B-Ultra-X-RU with Docker Model Runner:
docker model run hf.co/MagistrTheOne/Radon-35B-Ultra-X-RU
File size: 3,213 Bytes
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