Instructions to use MemTensor/MemT-1.0-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MemTensor/MemT-1.0-Flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MemTensor/MemT-1.0-Flash", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MemTensor/MemT-1.0-Flash", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MemTensor/MemT-1.0-Flash 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-Flash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MemTensor/MemT-1.0-Flash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MemTensor/MemT-1.0-Flash
- SGLang
How to use MemTensor/MemT-1.0-Flash 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-Flash" \ --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": "MemTensor/MemT-1.0-Flash", "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 "MemTensor/MemT-1.0-Flash" \ --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": "MemTensor/MemT-1.0-Flash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MemTensor/MemT-1.0-Flash with Docker Model Runner:
docker model run hf.co/MemTensor/MemT-1.0-Flash
Download assets/figure_performance.png from MemTensor/MemT-1.0-Flash: direct link, hf CLI and curl.
- Browser
- Download file 302 kB
-
https://huggingface.co/MemTensor/MemT-1.0-Flash/resolve/main/assets/figure_performance.png
- Command line
-
hf download hf://MemTensor/MemT-1.0-Flash/assets/figure_performance.png
-
curl -L -o figure_performance.png https://huggingface.co/MemTensor/MemT-1.0-Flash/resolve/main/assets/figure_performance.png
302 kB

- Xet hash:
- d39138a6def858fdc5af74c2f0610608a9c98b3eb43f5c528509ce5a5c193df7
- Size of remote file:
- 302 kB
- SHA256:
- 9de0e47c3ff33ce13d03fb525ed0700b1309d3e166d0f837d1885ec7aac33ea7
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