Instructions to use riytdxc43/vediolargemodels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use riytdxc43/vediolargemodels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="riytdxc43/vediolargemodels")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("riytdxc43/vediolargemodels", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use riytdxc43/vediolargemodels with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "riytdxc43/vediolargemodels" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "riytdxc43/vediolargemodels", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/riytdxc43/vediolargemodels
- SGLang
How to use riytdxc43/vediolargemodels 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 "riytdxc43/vediolargemodels" \ --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": "riytdxc43/vediolargemodels", "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 "riytdxc43/vediolargemodels" \ --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": "riytdxc43/vediolargemodels", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use riytdxc43/vediolargemodels with Docker Model Runner:
docker model run hf.co/riytdxc43/vediolargemodels
| license: other | |
| license_name: combined-open-weights-license | |
| library_name: transformers | |
| tags: | |
| - audio-processing | |
| - text-to-video | |
| - text-generation | |
| - compilation | |
| # Multi-Model Passive Storage Archive Compilation | |
| This public repository serves as a centralized passive remote storage structure hosting a collection of isolated state-of-the-art weights blocks. Every architecture system is deployed under dedicated sub-directories to maintain structural data sorting. | |
| ## Architecture Folders & Author Credits: | |
| 1. **`faster-whisper-turbo/`** | |
| - **Base Model**: `deepdml/faster-whisper-large-v3-turbo-ct2` | |
| - **Type**: CTranslate2 ASR Engine. | |
| - **Credits**: OpenAI / deepdml. | |
| 2. **`LTX-2.3-fp8/`** | |
| - **Base Model**: `Lightricks/LTX-2.3-fp8` | |
| - **Type**: Compressed 8-bit Multimodal Video Diffusion. | |
| - **Credits**: Lightricks (LTX.io). | |
| 3. **`LTX-2.3/`** | |
| - **Base Model**: `Lightricks/LTX-2.3` | |
| - **Type**: High-Fidelity 22B Video-Audio Generative Base. | |
| - **Credits**: Lightricks (LTX.io). | |
| 4. **`DeepSeek-R1-FP8/`** | |
| - **Base Model**: `deepseek-ai/DeepSeek-R1` | |
| - **Type**: Mixture-of-Experts FP8 Native 642B Reasoner. | |
| - **Credits**: deepseek-ai. | |
| ### Passive Compliance Terms: | |
| - **Intended Use**: Strictly allocated for long-term remote personal mirror backup operations. | |
| - **Intellectual Property Rights**: All architecture algorithms, weights optimizations, benchmarks, and data formats belong exclusively to their respective training organizations. This collection complies with non-commercial mirror distributions. | |