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)# 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
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"_name_or_path": "TranscoreM",
"architectures": [
"TransCoreMQWenForCausalLM"
],
"attn_dropout_prob": 0.0,
"auto_map": {
"AutoConfig": "configuration_qwen.QWenConfig",
"AutoModelForCausalLM": "modeling_qwen.QWenLMHeadModel"
},
"bf16": false,
"emb_dropout_prob": 0.0,
"fp16": false,
"fp32": false,
"freeze_mm_mlp_adapter": false,
"hidden_size": 5120,
"image_aspect_ratio": "pad",
"image_grid_pinpoints": null,
"initializer_range": 0.02,
"intermediate_size": 27392,
"kv_channels": 128,
"layer_norm_epsilon": 1e-06,
"max_position_embeddings": 8192,
"mm_hidden_size": 1024,
"mm_projector_lr": null,
"mm_projector_type": "mlp2x_gelu",
"mm_use_im_patch_token": false,
"mm_use_im_start_end": false,
"mm_vision_select_feature": "patch",
"mm_vision_select_layer": -2,
"mm_vision_tower": "PCIResearch/clip-vit-large-patch14-336",
"model_type": "transcorem",
"no_bias": true,
"num_attention_heads": 40,
"num_hidden_layers": 40,
"onnx_safe": null,
"rotary_emb_base": 10000,
"rotary_pct": 1.0,
"scale_attn_weights": true,
"seq_length": 2048,
"softmax_in_fp32": false,
"tie_word_embeddings": false,
"tokenizer_class": "QWenTokenizer",
"torch_dtype": "bfloat16",
"transformers_version": "4.31.0",
"tune_entire_model": false,
"tune_mm_mlp_adapter": false,
"tune_vision_tower": false,
"use_cache": true,
"use_cache_kernel": false,
"use_cache_quantization": false,
"use_dynamic_ntk": true,
"use_flash_attn": "auto",
"use_logn_attn": true,
"use_mm_proj": true,
"vision_tower_lr": null,
"vocab_size": 152064
}
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