Instructions to use Paladiso/6a2139b5-d27e-477f-8b4a-9be9f04e75b5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Paladiso/6a2139b5-d27e-477f-8b4a-9be9f04e75b5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("numind/NuExtract-v1.5") model = PeftModel.from_pretrained(base_model, "Paladiso/6a2139b5-d27e-477f-8b4a-9be9f04e75b5") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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---
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library_name: peft
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license:
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base_model:
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tags:
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- axolotl
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- generated_from_trainer
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datasets:
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- Paladiso/
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model-index:
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- name:
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results: []
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---
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axolotl version: `0.6.0`
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```yaml
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adapter: lora
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base_model:
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bf16: auto
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chat_template: llama3
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dataset_prepared_path: /workspace/axolotl/data/prepared
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datasets:
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- ds_type: json
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format: custom
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path: Paladiso/
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type:
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field_instruction:
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field_output:
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system_format: '{system}'
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system_prompt: ''
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debug: null
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gradient_accumulation_steps: 4
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gradient_checkpointing: false
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group_by_length: false
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hub_model_id: Paladiso/
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hub_private_repo: true
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hub_repo: null
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hub_strategy: checkpoint
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val_set_size: 0.05
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wandb_entity: null
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wandb_mode: online
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wandb_name:
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wandb_project: Gradients-On-Demand
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wandb_run: your_name
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wandb_runid:
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warmup_steps: 10
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weight_decay: 0.0
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xformers_attention: null
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</details><br>
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#
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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- Loss:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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### Framework versions
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---
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library_name: peft
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license: mit
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base_model: numind/NuExtract-v1.5
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tags:
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- axolotl
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- generated_from_trainer
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datasets:
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- Paladiso/dataset_c99871fd-1142-4a68-9841-64b293e07c60
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model-index:
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- name: 6a2139b5-d27e-477f-8b4a-9be9f04e75b5
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results: []
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---
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axolotl version: `0.6.0`
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```yaml
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adapter: lora
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base_model: numind/NuExtract-v1.5
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bf16: auto
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chat_template: llama3
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dataset_prepared_path: /workspace/axolotl/data/prepared
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datasets:
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- ds_type: json
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format: custom
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path: Paladiso/dataset_c99871fd-1142-4a68-9841-64b293e07c60
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type:
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field_instruction: instruction
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field_output: output
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system_format: '{system}'
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system_prompt: ''
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debug: null
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gradient_accumulation_steps: 4
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gradient_checkpointing: false
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group_by_length: false
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hub_model_id: Paladiso/6a2139b5-d27e-477f-8b4a-9be9f04e75b5
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hub_private_repo: true
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hub_repo: null
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hub_strategy: checkpoint
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val_set_size: 0.05
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wandb_entity: null
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wandb_mode: online
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wandb_name: c99871fd-1142-4a68-9841-64b293e07c60
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wandb_project: Gradients-On-Demand
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wandb_run: your_name
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wandb_runid: c99871fd-1142-4a68-9841-64b293e07c60
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warmup_steps: 10
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weight_decay: 0.0
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xformers_attention: null
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</details><br>
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# 6a2139b5-d27e-477f-8b4a-9be9f04e75b5
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This model is a fine-tuned version of [numind/NuExtract-v1.5](https://huggingface.co/numind/NuExtract-v1.5) on the Paladiso/dataset_c99871fd-1142-4a68-9841-64b293e07c60 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.9610
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 8.2516 | 0.0021 | 3 | 2.1313 |
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| 7.4297 | 0.0041 | 6 | 2.0895 |
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| 7.3104 | 0.0062 | 9 | 1.9610 |
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### Framework versions
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