Instructions to use watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm
- SGLang
How to use watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm 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 "watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm" \ --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": "watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm", "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 "watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm" \ --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": "watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm with Docker Model Runner:
docker model run hf.co/watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm
モデル概要
Watashiha-Llama-2-13B-Ogiri-sftをLLaVAで学習し、画像に対応した大喜利言語モデルです。
Vision Encoderにはlaion/CLIP-ViT-B-32-laion2B-s34B-b79Kを使用しています。
- License: LLAMA 2 COMMUNITY LICENSE
- Library: LLaVA
学習データ
事前学習のデータにはSTAIR Captionsを使用しています。
STAIR Captionsのデータで学習する際、
MS COCO 2014で以下のライセンスが付与されている画像データは使用しないようにしました。
- Attribution-NonCommercial-ShareAlike License
- Attribution-NonCommercial License
- Attribution-NonCommercial-NoDerivs License
- No known copyright restrictions
Fine-tuningのデータには以下のデータを使用しています。
- Japanese Visual Genome VQA dataset
- ボケ缶データセット
- 大喜利データ(テキストのみ)
使用方法
以下のGoogle Colabのサンプルコードを参考にしてください。
サンプルコード
開発者
- 内田 達弥 (UCHIDA, Tatsuya)
- Downloads last month
- 2
Model tree for watashiha/Watashiha-Llama-2-13B-Ogiri-sft-vlm
Base model
watashiha/Watashiha-Llama-2-13B-Ogiri-sft