Instructions to use Paladiso/a31e9467-1b84-42f8-a0e6-2c16bbde2ff7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Paladiso/a31e9467-1b84-42f8-a0e6-2c16bbde2ff7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "Paladiso/a31e9467-1b84-42f8-a0e6-2c16bbde2ff7") - 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_input: ''
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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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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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save_safetensors: true
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saves_per_epoch: 4
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sequence_len: 512
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special_tokens:
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pad_token: </s>
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strict: false
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tf32: false
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tokenizer_type: AutoTokenizer
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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: gemma
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base_model: unsloth/gemma-2-2b-it
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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_e0ed89fb-80e3-4383-8dd1-e5d47cb56894
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model-index:
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- name: a31e9467-1b84-42f8-a0e6-2c16bbde2ff7
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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: unsloth/gemma-2-2b-it
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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_e0ed89fb-80e3-4383-8dd1-e5d47cb56894
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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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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/a31e9467-1b84-42f8-a0e6-2c16bbde2ff7
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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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save_safetensors: true
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saves_per_epoch: 4
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sequence_len: 512
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strict: false
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tf32: false
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tokenizer_type: AutoTokenizer
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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: e0ed89fb-80e3-4383-8dd1-e5d47cb56894
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wandb_project: Gradients-On-Demand
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wandb_run: your_name
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wandb_runid: e0ed89fb-80e3-4383-8dd1-e5d47cb56894
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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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# a31e9467-1b84-42f8-a0e6-2c16bbde2ff7
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This model is a fine-tuned version of [unsloth/gemma-2-2b-it](https://huggingface.co/unsloth/gemma-2-2b-it) on the Paladiso/dataset_e0ed89fb-80e3-4383-8dd1-e5d47cb56894 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.3201
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 1.3971 | 0.0009 | 3 | 1.5252 |
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| 1.4187 | 0.0018 | 6 | 1.4485 |
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| 1.34 | 0.0026 | 9 | 1.3201 |
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### Framework versions
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