Instructions to use Local-Novel-LLM-project/RAI-3.0-R1-VECTOR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Local-Novel-LLM-project/RAI-3.0-R1-VECTOR with Transformers:

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

pipe = pipeline("text-generation", model="Local-Novel-LLM-project/RAI-3.0-R1-VECTOR", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Local-Novel-LLM-project/RAI-3.0-R1-VECTOR", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Local-Novel-LLM-project/RAI-3.0-R1-VECTOR", trust_remote_code=True)
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Local-Novel-LLM-project/RAI-3.0-R1-VECTOR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Local-Novel-LLM-project/RAI-3.0-R1-VECTOR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Local-Novel-LLM-project/RAI-3.0-R1-VECTOR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Local-Novel-LLM-project/RAI-3.0-R1-VECTOR

SGLang

How to use Local-Novel-LLM-project/RAI-3.0-R1-VECTOR 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 "Local-Novel-LLM-project/RAI-3.0-R1-VECTOR" \
    --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": "Local-Novel-LLM-project/RAI-3.0-R1-VECTOR",
		"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 "Local-Novel-LLM-project/RAI-3.0-R1-VECTOR" \
        --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": "Local-Novel-LLM-project/RAI-3.0-R1-VECTOR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Local-Novel-LLM-project/RAI-3.0-R1-VECTOR with Docker Model Runner:
```
docker model run hf.co/Local-Novel-LLM-project/RAI-3.0-R1-VECTOR
```

RAI-3.0-R1-VECTOR

Model Overview

RAI-3.0-R1-VECTOR is a task-vector merged model created using the following formula:

DeepSeek-R1-0528 + (RakutenAI-3.0 - DeepSeek-V3-0324)

This architecture combines the advanced reasoning capabilities of DeepSeek-R1-0528 with the Japanese language expertise of RakutenAI-3.0, while subtracting the base DeepSeek-V3-0324 to isolate task-specific improvements.

Key Features

Enhanced Reasoning: Inherits DeepSeek-R1's improved depth of reasoning (average 23K tokens per complex query).
Japanese Optimization: Retains RakutenAI-3.0's proficiency in Japanese language and cultural context.
Reduced Hallucination: Benefits from DeepSeek-R1's reduced hallucination rate.
Multilingual Support: Balanced performance in both Japanese and English.

Technical Details

Parameter	Value
Base Model	DeepSeek-R1-0528
Task Vector Source	RakutenAI-3.0 - DeepSeek-V3-0324
Architecture	Mixture of Experts (MoE)
Context Length	128K tokens
License	Apache-2.0

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Local-Novel-LLM-project/RAI-3.0-R1-VECTOR", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Local-Novel-LLM-project/RAI-3.0-R1-VECTOR")

inputs = tokenizer("日本の文化で重要な要素は", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0]))

Limitations and Bias

May inherit biases from either source model.
Performance in non-Japanese/English languages may vary.
Always verify critical outputs with human review.

Citation

@misc{RAIR1VECTOR2026,
  title = {RAI-3.0-R1-VECTOR: Task-Vector Merged Model},
  author = {LocalNovelLLM-project},
  year = {2026},
  publisher = {LocalNovelLLM-project},
  url = {https://huggingface.co/Local-Novel-LLM-project/RAI-3.0-R1-VECTOR}
}

Note: This model card was generated by the model itself and subsequently edited.

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