Instructions to use raulgdp/roberta-large-ner-qlorafinetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use raulgdp/roberta-large-ner-qlorafinetune with PEFT:
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("FacebookAI/xlm-roberta-large") model = PeftModel.from_pretrained(base_model, "raulgdp/roberta-large-ner-qlorafinetune") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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model-index:
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- name: roberta-large-ner-qlorafinetune
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results: []
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library_name: peft
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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## Training procedure
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The following `bitsandbytes` quantization config was used during training:
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- load_in_8bit: False
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- load_in_4bit: True
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- llm_int8_threshold: 6.0
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- llm_int8_skip_modules: ['classifier', 'pre_classifier']
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- llm_int8_enable_fp32_cpu_offload: False
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- llm_int8_has_fp16_weight: False
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- bnb_4bit_quant_type: nf4
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- bnb_4bit_use_double_quant: True
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- bnb_4bit_compute_dtype: bfloat16
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### Training hyperparameters
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The following hyperparameters were used during training:
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### Framework versions
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- PEFT 0.4.0
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- Transformers 4.31.0
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- Pytorch 2.5.1
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- Datasets 3.1.0
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model-index:
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- name: roberta-large-ner-qlorafinetune
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.5.1
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- Datasets 3.1.0
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