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--- |
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base_model: meta-llama/Llama-2-13b-hf |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Ruckus-PyAssi-13b |
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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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# Ruckus-PyAssi-13b |
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This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) |
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on a 10 000 examples from flytech/llama-python-codes-30k dataset. |
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## Model description |
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Model trained in 4-bit architecture using SFT (Supervised Fine Tuning) and LoRA (Low-Rank Adaptation) methods, |
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fine-tuning further is possible. |
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## Intended uses & limitations |
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Code-generation, but as like all Ruckus models |
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- Created to serve as an executional layer |
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- Rich in Python codes and instructional tasks |
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- Specially formatted for chat (see inference) |
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## Training procedure |
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Model was being trained for 13 hours of A6000 single 48GB vRAM GPU |
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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: 32 |
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- eval_batch_size: 32 * 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: constant |
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- num_epochs: 5 |
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## Inference |
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- Make sure to format your prompt: |
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[INST]This is my prompt[/INST] |
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[INST]Ruckus, open google[/INST] |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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