Instructions to use PCIResearch/TransCore-M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PCIResearch/TransCore-M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PCIResearch/TransCore-M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("PCIResearch/TransCore-M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PCIResearch/TransCore-M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PCIResearch/TransCore-M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PCIResearch/TransCore-M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/PCIResearch/TransCore-M
- SGLang
How to use PCIResearch/TransCore-M 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 "PCIResearch/TransCore-M" \ --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": "PCIResearch/TransCore-M", "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 "PCIResearch/TransCore-M" \ --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": "PCIResearch/TransCore-M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use PCIResearch/TransCore-M with Docker Model Runner:
docker model run hf.co/PCIResearch/TransCore-M
Download visual_encoder/pytorch_model.bin from PCIResearch/TransCore-M: direct link, hf CLI and curl.
- Browser
- Download file 607 MB
-
https://huggingface.co/PCIResearch/TransCore-M/resolve/20f20dbfda0aaca09c7bc502cbe4e1aec81ed33a/visual_encoder/pytorch_model.bin
- Command line
-
hf download hf://PCIResearch/TransCore-M@20f20dbfda0aaca09c7bc502cbe4e1aec81ed33a/visual_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/PCIResearch/TransCore-M/resolve/20f20dbfda0aaca09c7bc502cbe4e1aec81ed33a/visual_encoder/pytorch_model.bin
607 MB
- Xet hash:
- 0a5e2c87e1256063c4cb9e738be097d8a5cc9a53a794a4718144fcb913dd97c1
- Size of remote file:
- 607 MB
- SHA256:
- 5d7c1dbc68ee9bd545517842d8137286e911254d028eb4258daedac303e46c80
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