Text Generation
Transformers
Safetensors
PyTorch
English
Spanish
qwen2
reasoning
unsloth
bilingual
opceanai
yuuki
rxg
fine-tuned
chat
deepseek
conversational
text-generation-inference
Instructions to use OpceanAI/Yuuki-RxG-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpceanAI/Yuuki-RxG-nano with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpceanAI/Yuuki-RxG-nano") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OpceanAI/Yuuki-RxG-nano") model = AutoModelForCausalLM.from_pretrained("OpceanAI/Yuuki-RxG-nano") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpceanAI/Yuuki-RxG-nano with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpceanAI/Yuuki-RxG-nano" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpceanAI/Yuuki-RxG-nano", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OpceanAI/Yuuki-RxG-nano
- SGLang
How to use OpceanAI/Yuuki-RxG-nano 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 "OpceanAI/Yuuki-RxG-nano" \ --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": "OpceanAI/Yuuki-RxG-nano", "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 "OpceanAI/Yuuki-RxG-nano" \ --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": "OpceanAI/Yuuki-RxG-nano", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use OpceanAI/Yuuki-RxG-nano 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 OpceanAI/Yuuki-RxG-nano 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 OpceanAI/Yuuki-RxG-nano to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for OpceanAI/Yuuki-RxG-nano to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="OpceanAI/Yuuki-RxG-nano", max_seq_length=2048, ) - Docker Model Runner
How to use OpceanAI/Yuuki-RxG-nano with Docker Model Runner:
docker model run hf.co/OpceanAI/Yuuki-RxG-nano
Upload 5 files
Browse files- added_tokens.json +26 -0
- generation_config.json +5 -0
- merges.txt +0 -0
- special_tokens_map.json +31 -0
- vocab.json +0 -0
added_tokens.json
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{
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"</think>": 151666,
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"</tool_call>": 151658,
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"<think>": 151665,
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"<tool_call>": 151657,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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generation_config.json
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{
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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"transformers_version": "4.56.1"
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}
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merges.txt
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"eos_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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vocab.json
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