Instructions to use Aratako/Ninja-v1-RP-WIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aratako/Ninja-v1-RP-WIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Aratako/Ninja-v1-RP-WIP")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Aratako/Ninja-v1-RP-WIP") model = AutoModelForCausalLM.from_pretrained("Aratako/Ninja-v1-RP-WIP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Aratako/Ninja-v1-RP-WIP with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Aratako/Ninja-v1-RP-WIP" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aratako/Ninja-v1-RP-WIP", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Aratako/Ninja-v1-RP-WIP
- SGLang
How to use Aratako/Ninja-v1-RP-WIP 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 "Aratako/Ninja-v1-RP-WIP" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aratako/Ninja-v1-RP-WIP", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Aratako/Ninja-v1-RP-WIP" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aratako/Ninja-v1-RP-WIP", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Aratako/Ninja-v1-RP-WIP with Docker Model Runner:
docker model run hf.co/Aratako/Ninja-v1-RP-WIP
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README.md
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library_name: transformers
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tags:
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- roleplay
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library_name: transformers
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tags:
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- roleplay
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base_model:
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- Local-Novel-LLM-project/Ninja-v1-NSFW
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---
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# Ninja-v1-RP-WIP
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## 概要
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[Local-Novel-LLM-project/Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW)をロールプレイ用にLoRAでファインチューニングしたモデルです。
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[Aratako/Ninja-v1-RP](https://huggingface.co/Aratako/Ninja-v1-RP)のベースとなるモデルとして利用しています。
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## プロンプトフォーマット
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Vicunaのchat templateを利用してください。また、設定などを渡すシステムプロンプトは最初の`USER: `より前に入力されることを想定しています。
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また、マルチターンの対話を行う場合各ターンのアシスタントの応答の末尾に`eos_token`を必ずつけてください。
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```
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{ロールプレイの指示、世界観・あらすじの説明、キャラの設定など}
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USER: {userの最初の入力}
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ASSISTANT:
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```
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## 学習データセット
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GPTやLlama2等の出力の学習利用時に問題があるモデルを使って作成されたデータセットは一切使っていません。
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### 日本語データセット
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- [Aratako/Rosebleu-1on1-Dialogues-RP](https://huggingface.co/datasets/Aratako/Rosebleu-1on1-Dialogues-RP)
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- [Aratako/LimaRP-augmented-ja-karakuri](https://huggingface.co/datasets/Aratako/LimaRP-augmented-ja-karakuri)
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- [Aratako/Bluemoon_Top50MB_Sorted_Fixed_ja](https://huggingface.co/datasets/Aratako/Bluemoon_Top50MB_Sorted_Fixed_ja)
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- [OmniAICreator/Japanese-Roleplay](https://huggingface.co/datasets/OmniAICreator/Japanese-Roleplay)
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### 英語データセット
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- [grimulkan/LimaRP-augmented](https://huggingface.co/datasets/grimulkan/LimaRP-augmented)
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- [SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed](https://huggingface.co/datasets/SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed)
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## 学習の設定
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RunpodでGPUサーバを借り、A6000x4で学習を行いました。主な学習パラメータは以下の通りです。
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- lora_r: 128
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- lisa_alpha: 256
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- lora_dropout: 0.05
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- lora_target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "lm_head"]
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- learning_rate: 2e-5
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- num_train_epochs: 3 epochs
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- batch_size: 64
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- max_seq_length: 4096
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