Instructions to use DeclanBracken/MiniCPM-Llama3-V-2_5-Transcriptor-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeclanBracken/MiniCPM-Llama3-V-2_5-Transcriptor-V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DeclanBracken/MiniCPM-Llama3-V-2_5-Transcriptor-V2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DeclanBracken/MiniCPM-Llama3-V-2_5-Transcriptor-V2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
Browse filesUpdated to correct repo name.
- tokenizer_config.json +1 -1
tokenizer_config.json
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},
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"auto_map": {
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"AutoTokenizer": [
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"DeclanBracken/MiniCPM-Llama3-V-
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"auto_map": {
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"AutoTokenizer": [
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"DeclanBracken/MiniCPM-Llama3-V-2_5-Transcriptor-V2--modeling_minicpmv.PreTrainedTokenizerFastWrapper",
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