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
Update technical report link to arXiv
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README.md
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<a href="https://huggingface.co/collections/MemTensor/memt-1" style="display: inline-block; height: 20px; line-height: 0; vertical-align: middle;"><img src="assets/badges/huggingface-models.svg" alt="Hugging Face Models" width="151" height="20" style="display: inline-block; height: 20px; vertical-align: middle; margin: 0;"></a>
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<a href="https://github.com/MemTensor/MemT-1.0" style="display: inline-block; height: 20px; line-height: 0; vertical-align: middle;"><img src="assets/badges/github.svg" alt="GitHub: MemT-1.0" width="147" height="20" style="display: inline-block; height: 20px; vertical-align: middle; margin: 0;"></a>
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<a href="https://huggingface.co/collections/MemTensor/memt-1" style="display: inline-block; height: 20px; line-height: 0; vertical-align: middle;"><img src="assets/badges/huggingface-models.svg" alt="Hugging Face Models" width="151" height="20" style="display: inline-block; height: 20px; vertical-align: middle; margin: 0;"></a>
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<a href="https://arxiv.org/pdf/2610.26825" style="display: inline-block; height: 20px; line-height: 0; vertical-align: middle;"><img src="assets/badges/tech-report.svg" alt="Tech Report: PDF" width="140" height="20" style="display: inline-block; height: 20px; vertical-align: middle; margin: 0;"></a>
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<a href="https://github.com/MemTensor/MemT-1.0" style="display: inline-block; height: 20px; line-height: 0; vertical-align: middle;"><img src="assets/badges/github.svg" alt="GitHub: MemT-1.0" width="147" height="20" style="display: inline-block; height: 20px; vertical-align: middle; margin: 0;"></a>
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