Instructions to use Michael711/feinschwarz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Michael711/feinschwarz with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Michael711/feinschwarz")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Michael711/feinschwarz") model = AutoModelForCausalLM.from_pretrained("Michael711/feinschwarz", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Michael711/feinschwarz with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Michael711/feinschwarz" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Michael711/feinschwarz", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Michael711/feinschwarz
- SGLang
How to use Michael711/feinschwarz 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 "Michael711/feinschwarz" \ --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": "Michael711/feinschwarz", "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 "Michael711/feinschwarz" \ --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": "Michael711/feinschwarz", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Michael711/feinschwarz with Docker Model Runner:
docker model run hf.co/Michael711/feinschwarz
Commit History
-
Update README.md
d7dcef9 -
Update README.md
249c194 -
Update README.md
606aa56 -
Update README.md
832b588 -
Update README.md
627a7e2 -
Upload README.md
c8f1819 -
Upload README.md
58c6d41 -
Upload eval_results.json
4f59255 -
Upload config.json
c339b5c -
Upload all_results.json
fd2b11d -
Upload added_tokens.json
b59c7c9 -
Upload README.md
8985629 -
Upload merges.txt
55740cc -
Upload special_tokens_map.json
c6c7fe9 -
Upload tokenizer.json
6e43991 -
Upload tokenizer_config.json
507830e -
Upload train_results.json
6cacca7 -
Upload trainer_state.json
12db5e1 -
Upload training_args.bin with git-lfs
73198b7 -
Upload vocab.json
182af2f -
Upload pytorch_model.bin with git-lfs
04ef8af -
initial commit
f1f2192