Instructions to use aniruddh123464/allmodels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aniruddh123464/allmodels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aniruddh123464/allmodels")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("aniruddh123464/allmodels", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use aniruddh123464/allmodels with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aniruddh123464/allmodels" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aniruddh123464/allmodels", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aniruddh123464/allmodels
- SGLang
How to use aniruddh123464/allmodels 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 "aniruddh123464/allmodels" \ --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": "aniruddh123464/allmodels", "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 "aniruddh123464/allmodels" \ --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": "aniruddh123464/allmodels", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aniruddh123464/allmodels with Docker Model Runner:
docker model run hf.co/aniruddh123464/allmodels
| license: other | |
| license_name: multi-license-qwen | |
| library_name: transformers | |
| tags: | |
| - text-generation | |
| - multimodal | |
| - qwen | |
| - compilation | |
| # Qwen Multi-Model Repository Compilation | |
| This public repository serves as an aggregated passive cloud storage mirror hosting a compilation of official large language checkpoints. All architecture elements are stored in isolated sub-directories. | |
| ## Included Components & Credits: | |
| 1. **Qwen2.5-Coder-32B-Instruct**: Original system by `Qwen/Qwen2.5-Coder-32B-Instruct` (Apache 2.0). | |
| 2. **Qwen2.5-32B-Instruct**: Original system by `Qwen/Qwen2.5-32B-Instruct` (Apache 2.0). | |
| 3. **Qwen2-VL-72B-Instruct**: Original vision-language system by `Qwen/Qwen2-VL-72B-Instruct` (Qwen Research License). | |
| ### Compliance & Terms Notice: | |
| - **Intended Use**: Remote archival hosting and backup mirror. | |
| - **Intellectual Property**: Full authorship, training properties, and trademarks belong exclusively to the **Qwen Team / Alibaba Cloud**. | |