Instructions to use Blackroot/TensorProduct-Microllama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Blackroot/TensorProduct-Microllama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Blackroot/TensorProduct-Microllama")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Blackroot/TensorProduct-Microllama", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Blackroot/TensorProduct-Microllama with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Blackroot/TensorProduct-Microllama" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Blackroot/TensorProduct-Microllama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Blackroot/TensorProduct-Microllama
- SGLang
How to use Blackroot/TensorProduct-Microllama 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 "Blackroot/TensorProduct-Microllama" \ --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": "Blackroot/TensorProduct-Microllama", "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 "Blackroot/TensorProduct-Microllama" \ --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": "Blackroot/TensorProduct-Microllama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Blackroot/TensorProduct-Microllama with Docker Model Runner:
docker model run hf.co/Blackroot/TensorProduct-Microllama
Commit History
-
Add model card metadata: pipeline tag, library name, link to paper, and link to code repository.
Verified signatureThis PR enriches the model card by: - Adding the pipeline tag `text-generation`, making the model discoverable at https://huggingface.co/models?pipeline_tag=text-generation. - Specifying the library name (`transformers`). - Linking to the paper [Tensor Product Attention Is All You Need](https://hf.co/papers/2501.06425). - Adding a link to the code repository at https://github.com/tensorgi/T6.9db2f00
-
Update README.md
Verified signature9e51385 -
Update README.md
Verified signaturef2e6a64 -
Update README.md
Verified signature87050d7 -
Update README.md
Verified signaturee4b84eb -
Create README.md
Verified signatureada2a1e -
Upload 18 files
Verified signature6aced58 -
initial commit
Verified signaturef2c8e64