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/config.json from PCIResearch/TransCore-M: direct link, hf CLI and curl.
- Browser
- Download file 498 Bytes
-
https://huggingface.co/PCIResearch/TransCore-M/resolve/20f20dbfda0aaca09c7bc502cbe4e1aec81ed33a/visual_encoder/config.json
- Command line
-
hf download hf://PCIResearch/TransCore-M@20f20dbfda0aaca09c7bc502cbe4e1aec81ed33a/visual_encoder/config.json
-
curl -L -o config.json https://huggingface.co/PCIResearch/TransCore-M/resolve/20f20dbfda0aaca09c7bc502cbe4e1aec81ed33a/visual_encoder/config.json
498 Bytes
| { | |
| "_name_or_path": "/data/lmm_models/clip-vit-large-patch14-336", | |
| "attention_dropout": 0.0, | |
| "dropout": 0.0, | |
| "hidden_act": "quick_gelu", | |
| "hidden_size": 1024, | |
| "image_size": 336, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-05, | |
| "model_type": "clip_vision_model", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 24, | |
| "patch_size": 14, | |
| "projection_dim": 768, | |
| "transformers_version": "4.31.0" | |
| } | |