Instructions to use rkotari/tinyllama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rkotari/tinyllama with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("PY007/TinyLlama-1.1B-step-50K-105b") model = PeftModel.from_pretrained(base_model, "rkotari/tinyllama") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +59 -0
- tokenizer.json +1 -6
- trainer_state.json +182 -0
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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- trl
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- sft
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- generated_from_trainer
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base_model: PY007/TinyLlama-1.1B-step-50K-105b
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model-index:
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- name: tinyllama
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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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# tinyllama
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This model is a fine-tuned version of [PY007/TinyLlama-1.1B-step-50K-105b](https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b) on the None dataset.
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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: 0.002
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- train_batch_size: 3
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 6
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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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- training_steps: 200
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### Training results
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### Framework versions
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- PEFT 0.11.2.dev0
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- Transformers 4.41.2
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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tokenizer.json
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{
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"version": "1.0",
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trainer_state.json
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