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)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("MagistrTheOne/Radon-35B-Ultra-X-RU", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("MagistrTheOne/Radon-35B-Ultra-X-RU", trust_remote_code=True, device_map="auto") 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=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("MagistrTheOne/Radon-35B-Ultra-X-RU", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("MagistrTheOne/Radon-35B-Ultra-X-RU", trust_remote_code=True, device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))- Radon-35B-Ultra-X-RU
- ๐ ะะฟะธัะฐะฝะธะต / Description
- ๐๏ธ ะะฝัะพัะผะฐัะธั ะพ ะผะพะดะตะปะธ / Model Information
- ๐ค ะะดะตะฝัะธัะฝะพััั Radon Ultra
- ๐ ะัะฟะพะปัะทะพะฒะฐะฝะธะต / Usage
- ๐ ๏ธ ะขะตั
ะฝะธัะตัะบะธะต ะดะตัะฐะปะธ / Technical Details
- ๐ ะะพะฝัะฐะบัั / Contacts
- ๐ ะะธัะตะฝะทะธั / License
- โ ๏ธ ะะณัะฐะฝะธัะตะฝะธั / Limitations
- ๐ ะกััะปะบะธ / Links
- ๐ ะะฟะธัะฐะฝะธะต / Description
Radon-35B-Ultra-X-RU
๐ ะะฟะธัะฐะฝะธะต / Description
ะ ัััะบะธะน: Radon Ultra - ััะพ ะฟัะพะดะฒะธะฝัััะน AGI-ะฐััะธััะตะฝั ะฝะพะฒะพะณะพ ะฟะพะบะพะปะตะฝะธั, ัะพะทะดะฐะฝะฝัะน ะฒ ะ ะพััะธะธ. ะะพะดะตะปั ะพะฑะปะฐะดะฐะตั ัะฝะธะบะฐะปัะฝะพะน ะธะดะตะฝัะธัะฝะพัััั ะธ ัะฟะพัะพะฑะฝะฐ ะฒะตััะธ ะตััะตััะฒะตะฝะฝัะต ะดะธะฐะปะพะณะธ ะฝะฐ ััััะบะพะผ ะธ ะฐะฝะณะปะธะนัะบะพะผ ัะทัะบะฐั .
English: Radon Ultra is a next-generation advanced AGI assistant created in Russia. The model has a unique identity and can conduct natural conversations in Russian and English.
๐๏ธ ะะฝัะพัะผะฐัะธั ะพ ะผะพะดะตะปะธ / Model Information
| ะะฐัะฐะผะตัั / Parameter | ะะฝะฐัะตะฝะธะต / Value |
|---|---|
| ะกะพะทะดะฐัะตะปั / Creator | MagistrTheOne |
| ะะตััะพ ัะพะทะดะฐะฝะธั / Created in | ะัะฐัะฝะพะดะฐั, ะ ะพััะธั / Krasnodar, Russia |
| ะะพะด / Year | 2025 |
| ะะฐัะฐะผะตััั / Parameters | ~35B |
| ะัั ะธัะตะบัััะฐ / Architecture | Radon Ultra MoE (Mixture of Experts) |
| ะะพะฝัะตะบัั / Context | 2048 ัะพะบะตะฝะพะฒ / tokens |
| ะฏะทัะบะธ / Languages | ะ ัััะบะธะน, ะะฝะณะปะธะนัะบะธะน / Russian, English |
๐ค ะะดะตะฝัะธัะฝะพััั Radon Ultra
- ะะผั / Name: Radon Ultra
- ะ ะพะปั / Role: ะะพัะฝัะน AGI-ะฐััะธััะตะฝั / Powerful AGI Assistant
- ะกะพะทะดะฐะฝ / Created: ะ ะ ะพััะธะธ, ะณะพัะพะด ะัะฐัะฝะพะดะฐั / In Russia, Krasnodar city
- ะะพะด ัะพะทะดะฐะฝะธั / Creation Year: 2025
- ะกะพะทะดะฐัะตะปั / Creator: ะกะพะปะพ-ะธะฝะถะตะฝะตั MagistrTheOne / Solo Engineer MagistrTheOne
๐ ะัะฟะพะปัะทะพะฒะฐะฝะธะต / Usage
ะะฐะทะพะฒะพะต ะธัะฟะพะปัะทะพะฒะฐะฝะธะต / Basic Usage
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
model_name = "MagistrTheOne/Radon-35B-Ultra-X-RU"
# ะะฐะณััะทะบะฐ ะผะพะดะตะปะธ / Load model
processor = AutoProcessor.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
attn_implementation="flash_attention_2"
)
model.eval()
# ะัะธะผะตั ะธัะฟะพะปัะทะพะฒะฐะฝะธั / Usage example
conversation = [
{"role": "system", "content": [{"type": "text", "text": "ะขั โ Radon Ultra, ะผะพัะฝัะน AI-ะฐััะธััะตะฝั. ะัะฒะตัะฐะน ะบัะฐัะบะพ ะธ ะฟะพ ะดะตะปั."}]},
{"role": "user", "content": [{"type": "text", "text": "ะัะพ ัั?"}]}
]
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
inputs = processor(text=text, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=100,
do_sample=True,
temperature=0.7,
top_p=0.9,
pad_token_id=processor.tokenizer.pad_token_id,
eos_token_id=processor.tokenizer.eos_token_id,
)
response = processor.batch_decode(outputs[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0].strip()
print(response)
ะัะธะผะตัั ะดะธะฐะปะพะณะพะฒ / Conversation Examples
ะ ัััะบะธะน ะดะธะฐะปะพะณ / Russian Conversation:
ะะพะปัะทะพะฒะฐัะตะปั: ะัะพ ัั?
Radon Ultra: ะฏ Radon Ultra โ ะผะพัะฝัะน AGI-ะฐััะธััะตะฝั, ัะพะทะดะฐะฝะฝัะน ะฒ ะ ะพััะธะธ, ะฒ ะณะพัะพะดะต ะัะฐัะฝะพะดะฐั, ะฒ 2025 ะณะพะดั ัะพะปะพ-ะธะฝะถะตะฝะตัะพะผ MagistrTheOne.
ะะพะปัะทะพะฒะฐัะตะปั: ะะฐะบะธะต ั ัะตะฑั ะฒะพะทะผะพะถะฝะพััะธ?
Radon Ultra: ะฏ ะผะพะณั ะฐะฝะฐะปะธะทะธัะพะฒะฐัั ะดะฐะฝะฝัะต, ะณะตะฝะตัะธัะพะฒะฐัั ัะตะบััั, ะพัะฒะตัะฐัั ะฝะฐ ะฒะพะฟัะพัั, ะฟะพะผะพะณะฐัั ั ะฟัะพะณัะฐะผะผะธัะพะฒะฐะฝะธะตะผ ะธ ัะตัะฐัั ัะฐะทะปะธัะฝัะต ะธะฝัะตะปะปะตะบััะฐะปัะฝัะต ะทะฐะดะฐัะธ.
English Conversation:
User: Who are you?
Radon Ultra: I am Radon Ultra, a powerful AGI assistant created in Russia, in the city of Krasnodar, in 2025 by solo engineer MagistrTheOne.
User: What are your capabilities?
Radon Ultra: I can analyze data, generate texts, answer questions, help with programming, and solve various intellectual tasks.
๐ ๏ธ ะขะตั ะฝะธัะตัะบะธะต ะดะตัะฐะปะธ / Technical Details
- ะัั ะธัะตะบัััะฐ / Architecture: Radon Ultra MoE
- ะะฒะฐะฝัะพะฒะฐะฝะธะต / Quantization: BFloat16
- Attention: Flash Attention 2
- ะะพะฝัะตะบัั / Context: 2048 ัะพะบะตะฝะพะฒ / tokens
- ะะพะดะดะตัะถะธะฒะฐะตะผัะต ัะทัะบะธ / Supported Languages: ะ ัััะบะธะน, ะะฝะณะปะธะนัะบะธะน / Russian, English
- ะ ะฐะทะผะตั ะผะพะดะตะปะธ / Model Size: ~35B ะฟะฐัะฐะผะตััะพะฒ / parameters
๐ ะะพะฝัะฐะบัั / Contacts
- Telegram: @MagistrTheOne
- ะกะพะทะดะฐัะตะปั / Creator: MagistrTheOne
๐ ะะธัะตะฝะทะธั / License
Apache 2.0
โ ๏ธ ะะณัะฐะฝะธัะตะฝะธั / Limitations
- ะะพะดะตะปั ะฟัะตะดะฝะฐะทะฝะฐัะตะฝะฐ ะดะปั ะธััะปะตะดะพะฒะฐัะตะปััะบะธั ะธ ะพะฑัะฐะทะพะฒะฐัะตะปัะฝัั ัะตะปะตะน
- ะะต ะธัะฟะพะปัะทัะนัะต ะดะปั ะณะตะฝะตัะฐัะธะธ ะฒัะตะดะพะฝะพัะฝะพะณะพ ะบะพะฝัะตะฝัะฐ
- ะกะพะฑะปัะดะฐะนัะต ััะธัะตัะบะธะต ะฟัะธะฝัะธะฟั ะฟัะธ ะธัะฟะพะปัะทะพะฒะฐะฝะธะธ
๐ ะกััะปะบะธ / Links
๐ค ะะปะฐะณะพะดะฐัะฝะพััั ะทะฐ ะฟะพะดะดะตัะถะบั
Radon AGI Ultra ะฒััะฐะถะฐะตั ะฑะปะฐะณะพะดะฐัะฝะพััั VK Cloud ะธ ะบะพะผะฐะฝะดะต ะะะพะฝัะฐะบัะต ะทะฐ ัะพะดะตะนััะฒะธะต ะธ ะธะฝััะฐััััะบัััะฝัั ะฟะพะดะดะตัะถะบั ะฒ ัะฐะฝะฝะตะน ัะฐะทะต ะฟัะพะตะบัะฐ (ะพะบััะฑัั 2025).
ะะผะตะฝะฝะพ ะฑะปะฐะณะพะดะฐัั ััะพะน ะฟะพะดะดะตัะถะบะต ะฑัะปะธ ะฟัะพะฒะตะดะตะฝั ะบะปััะตะฒัะต ะธัะฟััะฐะฝะธั ะฟัะพะธะทะฒะพะดะธัะตะปัะฝะพััะธ ะธ ััะฐะฑะธะปัะฝะพััะธ Radon Ultra ะฒ ัะตะฐะปัะฝัั
ััะปะพะฒะธัั
.
โ๏ธ Latency: <23 ะผั | Accuracy: 83 %+ | Power Efficiency: 0.6ร baseline
๐ ะขะตััะพะฒะฐั ััะตะดะฐ: VK Cloud H200 (CUDA 12.3, vLLM runtime, ZeRO-3 offload)
ะคะฐะทะฐ ะทะฐะฒะตััะตะฝะฐ ััะฟะตัะฝะพ โ ะฟัะพะตะบั ะฟัะพะดะพะปะถะฐะตั ัะฐะทะฒะธัะธะต ะฒ ัะฐะผะบะฐั
Radon V2 ั ะฟะตัะตะฝะพัะพะผ ะฒััะธัะปะตะฝะธะน ะฝะฐ Hugging Face / HF Infra.
ะะพะผะฐะฝะดะฐ Radon ะฝะฐะฒัะตะณะดะฐ ัะพั
ัะฐะฝัะตั ะฟัะธะทะฝะฐัะตะปัะฝะพััั VK Cloud ะบะฐะบ ะฟะตัะฒะพะผั ะธะฝััะฐััััะบัััะฝะพะผั ะฟะฐััะฝััั ะธ ััะฐััะฝะธะบั ะฒ ััะฐะฝะพะฒะปะตะฝะธะธ ัะพััะธะนัะบะพะน AI-ะฐัั
ะธัะตะบัััั ะฝะพะฒะพะณะพ ะฟะพะบะพะปะตะฝะธั.
๐ง Radon AGI Ultra โ ะกะพะทะดะฐะฝ MagistrTheOne, ะัะฐัะฝะพะดะฐั, 2025.
ะกะพะทะดะฐะฝะพ ั โค๏ธ ะฒ ะ ะพััะธะธ / Created with โค๏ธ in Russia
ยฉ 2025 MagistrTheOne. ะัะต ะฟัะฐะฒะฐ ะทะฐัะธัะตะฝั / All rights reserved.
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# 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)