Instructions to use MemTensor/MemT-1.0-Nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MemTensor/MemT-1.0-Nano with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MemTensor/MemT-1.0-Nano", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MemTensor/MemT-1.0-Nano", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MemTensor/MemT-1.0-Nano with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MemTensor/MemT-1.0-Nano" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MemTensor/MemT-1.0-Nano", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MemTensor/MemT-1.0-Nano
- SGLang
How to use MemTensor/MemT-1.0-Nano 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 "MemTensor/MemT-1.0-Nano" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MemTensor/MemT-1.0-Nano", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "MemTensor/MemT-1.0-Nano" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MemTensor/MemT-1.0-Nano", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use MemTensor/MemT-1.0-Nano with Docker Model Runner:
docker model run hf.co/MemTensor/MemT-1.0-Nano
Download assets/application.png from MemTensor/MemT-1.0-Nano: direct link, hf CLI and curl.
- Browser
- Download file 404 kB
-
https://huggingface.co/MemTensor/MemT-1.0-Nano/resolve/main/assets/application.png
- Command line
-
hf download hf://MemTensor/MemT-1.0-Nano/assets/application.png
-
curl -L -o application.png https://huggingface.co/MemTensor/MemT-1.0-Nano/resolve/main/assets/application.png
404 kB

- Xet hash:
- 6a1119c496cf4975d3dd1a71bb3c8c4fa08c5824ed0fa81d3d95b2c66d3e50f8
- Size of remote file:
- 404 kB
- SHA256:
- a06481ed23d733bf4293a1211cad9ea7f80dfa4674e0c3599c0b077c282ae4bb
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