Instructions to use xdosmen/Optimization_Methods with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xdosmen/Optimization_Methods with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="xdosmen/Optimization_Methods")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xdosmen/Optimization_Methods", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use xdosmen/Optimization_Methods with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "xdosmen/Optimization_Methods" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xdosmen/Optimization_Methods", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/xdosmen/Optimization_Methods
- SGLang
How to use xdosmen/Optimization_Methods 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 "xdosmen/Optimization_Methods" \ --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": "xdosmen/Optimization_Methods", "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 "xdosmen/Optimization_Methods" \ --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": "xdosmen/Optimization_Methods", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use xdosmen/Optimization_Methods with Docker Model Runner:
docker model run hf.co/xdosmen/Optimization_Methods
| license: openrail | |
| language: | |
| - en | |
| - de | |
| - ja | |
| - tr | |
| - ar | |
| - it | |
| - bg | |
| - ro | |
| - pl | |
| - fr | |
| - fa | |
| - ka | |
| - zh | |
| - ko | |
| datasets: | |
| - openai/MMMLU | |
| - arxiv-community/arxiv_dataset | |
| metrics: | |
| - accuracy | |
| - perplexity | |
| base_model: | |
| - openai/whisper-large-v3-turbo | |
| - nvidia/Llama-3.1-Nemotron-70B-Instruct-HF | |
| - stabilityai/stable-diffusion-3.5-large | |
| pipeline_tag: text-generation | |
| library_name: transformers | |