Instructions to use Bedru/DeepSeek-R1-Senti with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bedru/DeepSeek-R1-Senti with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Bedru/DeepSeek-R1-Senti", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- tokenizer_config.json +1 -1
tokenizer_config.json
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@@ -2060,7 +2060,7 @@
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"legacy": true,
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"model_max_length": 131072,
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"pad_token": "<|finetune_right_pad_id|>",
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-
"padding_side": "
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"sp_model_kwargs": {},
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"tokenizer_class": "LlamaTokenizer",
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| 2066 |
"unk_token": null,
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| 2060 |
"legacy": true,
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"model_max_length": 131072,
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| 2062 |
"pad_token": "<|finetune_right_pad_id|>",
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+
"padding_side": "left",
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| 2064 |
"sp_model_kwargs": {},
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| 2065 |
"tokenizer_class": "LlamaTokenizer",
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| 2066 |
"unk_token": null,
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