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--- |
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license: other |
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tags: |
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- llama-2 |
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--- |
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# Model Card: Pygmalion LRP Grad L2 7B |
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This model uses [Pygmalion 2 7B](https://huggingface.co/PygmalionAI/pygmalion-2-7b) as a base and merged with LimaRP v1(52%) Lora customized with Metharme format |
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This merge of Lora with Model was done with this [script](https://github.com/zarakiquemparte/zaraki-tools/blob/main/apply-lora-weight-ltl.py) |
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- Credits to [Suikamelon](https://huggingface.co/lemonilia) for the LimaRP dataset |
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- Credits to [Pygmalion AI](https://huggingface.co/PygmalionAI) for the base model |
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## Weights of Lora merge: |
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``` |
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0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 |
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``` |
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## Prompting |
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The model has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`. |
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The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. |
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The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to |
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form a conversation history. |
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### Prompting example |
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The system prompt has been designed to allow the model to "enter" various modes and dictate the reply length. Here's an example: |
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``` |
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<|system|>Enter RP mode. Pretend to be {{char}} whose persona follows: |
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{{persona}} |
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You shall reply to the user while staying in character, and generate long responses. |
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``` |
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## Bias, Risks, and Limitations |
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The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope. |
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As such, it was **not** fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that |
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are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. |
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Outputs might often be factually wrong or misleading. |
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## Training Details |
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This model use LimaRP by [Suikamelon](https://huggingface.co/lemonilia) dataset converted to metharme prompt format trained using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) and the lora merge was applied in the tool mentioned above |
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## Training Hyperparameters |
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``` |
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load_in_8bit: true |
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adapter: lora |
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lora_r: 8 |
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lora_alpha: 16 |
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lora_dropout: 0.01 |
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gradient_accumulation_steps: 1 |
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micro_batch_size: 1 |
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num_epochs: 3 |
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optimizer: adamw_torch |
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lr_scheduler: cosine |
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learning_rate: 0.000065 |
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bf16: true |
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tf32: true |
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``` |
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## Environmental Impact |
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Finetuning the LimaRP Lora on 1 x NVIDIA L40 takes about 1h45m |
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