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
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:
faster-whisper-turbo/- Base Model:
deepdml/faster-whisper-large-v3-turbo-ct2 - Type: CTranslate2 ASR Engine.
- Credits: OpenAI / deepdml.
- Base Model:
LTX-2.3-fp8/- Base Model:
Lightricks/LTX-2.3-fp8 - Type: Compressed 8-bit Multimodal Video Diffusion.
- Credits: Lightricks (LTX.io).
- Base Model:
LTX-2.3/- Base Model:
Lightricks/LTX-2.3 - Type: High-Fidelity 22B Video-Audio Generative Base.
- Credits: Lightricks (LTX.io).
- Base Model:
DeepSeek-R1-FP8/- Base Model:
deepseek-ai/DeepSeek-R1 - Type: Mixture-of-Experts FP8 Native 642B Reasoner.
- Credits: deepseek-ai.
- Base Model:
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.