Instructions to use XYZ123XYZ/shawgpt-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use XYZ123XYZ/shawgpt-ft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v0.6") model = PeftModel.from_pretrained(base_model, "XYZ123XYZ/shawgpt-ft") - Notebooks
- Google Colab
- Kaggle
XYZ123XYZ/qlora-test
Browse files
README.md
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---
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license: apache-2.0
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library_name: peft
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tags:
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- generated_from_trainer
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.6
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model-index:
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- name: shawgpt-ft
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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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should probably proofread and complete it, then remove this comment. -->
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# shawgpt-ft
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This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v0.6](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.0521
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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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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- learning_rate: 0.0002
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 2
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- num_epochs: 10
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 3.6937 | 0.92 | 3 | 3.2909 |
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| 3.6497 | 1.85 | 6 | 3.2640 |
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| 3.6197 | 2.77 | 9 | 3.2310 |
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| 2.6805 | 4.0 | 13 | 3.1818 |
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| 3.5328 | 4.92 | 16 | 3.1453 |
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| 3.48 | 5.85 | 19 | 3.1126 |
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| 3.4458 | 6.77 | 22 | 3.0858 |
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| 2.547 | 8.0 | 26 | 3.0614 |
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| 3.3807 | 8.92 | 29 | 3.0531 |
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| 2.2947 | 9.23 | 30 | 3.0521 |
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### Framework versions
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- PEFT 0.9.0
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- Transformers 4.38.2
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- Pytorch 2.1.0+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 32,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 8,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"q_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:18f90dc38a540ef01277c022fdcf414c6661e6278cc8b20ef71fac327455dda2
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size 2889376
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runs/Mar18_12-24-22_5a96ed93cce6/events.out.tfevents.1710764666.5a96ed93cce6.13787.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:8967fab9db85e500d8d1ec75d012cb9dbe08f41dda3288217ae4d447f73efbbe
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size 9680
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:e78eee984c29ee00e8a1c0e9cca17683402b99dc444762bfac5b40a4ccdb40ab
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size 4856
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