Text Generation
Transformers
Safetensors
loop-lm
looped-transformer
mixture-of-experts
scaling-laws
custom_code
Instructions to use ml-ryanlee/looped-16x2-32L-d640-1e18-a100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ml-ryanlee/looped-16x2-32L-d640-1e18-a100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ml-ryanlee/looped-16x2-32L-d640-1e18-a100", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ml-ryanlee/looped-16x2-32L-d640-1e18-a100", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ml-ryanlee/looped-16x2-32L-d640-1e18-a100 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ml-ryanlee/looped-16x2-32L-d640-1e18-a100" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ml-ryanlee/looped-16x2-32L-d640-1e18-a100", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ml-ryanlee/looped-16x2-32L-d640-1e18-a100
- SGLang
How to use ml-ryanlee/looped-16x2-32L-d640-1e18-a100 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 "ml-ryanlee/looped-16x2-32L-d640-1e18-a100" \ --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": "ml-ryanlee/looped-16x2-32L-d640-1e18-a100", "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 "ml-ryanlee/looped-16x2-32L-d640-1e18-a100" \ --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": "ml-ryanlee/looped-16x2-32L-d640-1e18-a100", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ml-ryanlee/looped-16x2-32L-d640-1e18-a100 with Docker Model Runner:
docker model run hf.co/ml-ryanlee/looped-16x2-32L-d640-1e18-a100
- Xet hash:
- a1e231e03212e2222c679906e8c3165ef15eab4557e16a81e5593c6bb1a97f93
- Size of remote file:
- 1.73 GB
- SHA256:
- ed5b455a51c53b099ed94538b234d72806ce576561196532bfa976a00186c0a2
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