| --- |
| license: apache-2.0 |
| tags: |
| - RakutenAI |
| - DeepSeek-R1 |
| - task-vector-merging |
| - japanese |
| - multilingual |
| library_name: transformers |
| language: |
| - ja |
| - en |
| pipeline_tag: text-generation |
| base_model: |
| - deepseek-ai/DeepSeek-R1-0528 |
| - Rakuten/RakutenAI-3.0 |
| - deepseek-ai/DeepSeek-V3-0324 |
| --- |
| # RAI-3.0-R1-VECTOR |
| <a href="https://www.apache.org/licenses/LICENSE-2.0" target="_blank" style="margin: 2px;"> |
| <img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-4caf50?&color=4caf50" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
|
|
| --- |
|
|
| ## 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 |
| ```python |
| 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 |
| ```bibtex |
| @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. |
