Instructions to use bilkultheek/ColdLLamaLite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bilkultheek/ColdLLamaLite with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("ahxt/LiteLlama-460M-1T") model = PeftModel.from_pretrained(base_model, "bilkultheek/ColdLLamaLite") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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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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#
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This model is a fine-tuned version of [ahxt/LiteLlama-460M-1T](https://huggingface.co/ahxt/LiteLlama-460M-1T) on the None dataset.
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## Model description
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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:
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- eval_batch_size:
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- seed: 42
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- gradient_accumulation_steps:
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- total_train_batch_size:
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type:
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- lr_scheduler_warmup_ratio: 0.03
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- num_epochs: 5
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### Training results
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### Framework versions
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- sft
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- generated_from_trainer
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model-index:
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- name: ColdLLamaLite
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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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# ColdLLamaLite
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This model is a fine-tuned version of [ahxt/LiteLlama-460M-1T](https://huggingface.co/ahxt/LiteLlama-460M-1T) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.3021
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## Model description
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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: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 256
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.03
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- num_epochs: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 4.1436 | 0.8 | 25 | 3.8815 |
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| 3.6028 | 1.6 | 50 | 3.2639 |
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| 2.9395 | 2.4 | 75 | 2.5905 |
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| 2.4548 | 3.2 | 100 | 2.3582 |
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| 2.337 | 4.0 | 125 | 2.3102 |
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| 2.3125 | 4.8 | 150 | 2.3024 |
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### Framework versions
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runs/Aug02_13-07-01_fastgpuserv/events.out.tfevents.1722602007.fastgpuserv.3714303.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:d6890949d3abe45db8d962b80fd220ed72f34e906ba435db853a4ad9a61faf49
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size 359
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