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license: apache-2.0 |
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# LimaRP-Llama2-7B-v3 (Alpaca, experimental, 4-bit LoRA adapter) |
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This is an experimental version of LimaRP for Llama2, using a somewhat updated dataset (1800 training samples) |
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and a 2-pass training procedure. The first pass includes unsupervised tuning on 2800 stories within |
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4k tokens length and the second is LimaRP. |
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For more details about LimaRP, see the model page for the [previously released version](https://huggingface.co/lemonilia/limarp-llama2-v2). |
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Most details written there apply for this version as well. |
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## Prompt format |
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Same as before. It uses the [extended Alpaca format](https://github.com/tatsu-lab/stanford_alpaca), |
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with `### Input:` immediately preceding user inputs and `### Response:` immediately preceding |
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model outputs. While Alpaca wasn't originally intended for multi-turn responses, in practice this |
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is not a problem; the format follows a pattern already used by other models. |
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``` |
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### Instruction: |
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Character's Persona: {bot character description} |
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User's Persona: {user character description} |
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Scenario: {what happens in the story} |
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Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. Character should respond with messages of medium length. |
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### Input: |
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User: {utterance} |
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### Response: |
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Character: {utterance} |
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### Input |
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User: {utterance} |
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### Response: |
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Character: {utterance} |
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(etc.) |
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``` |
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### Other notes |
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- Replace all the text in curly braces (curly braces included) with your own text. |
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- `User` and `Character` should be replaced with appropriate names. |
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## Training procedure |
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[Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training. |
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The model has been trained as a 4-bit LoRA adapter. It's so large because a LoRA rank |
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of 256 was used. It's suggested to merge it to the base Llama2-7B model. |
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### Training hyperparameters |
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For both passes these settings were used: |
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- learning_rate: 0.0002 |
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- lr_scheduler_type: constant |
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- lora_r: 256 |
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- lora_alpha: 16 |
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- lora_dropout: 0.1 |
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- lora_target_linear: True |
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- num_epochs: 1 |
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- bf16: True |
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- tf32: True |
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- load_in_4bit: True |
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- adapter: qlora |
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- micro_batch_size: 2 |
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- gradient_accumulation_steps: 1 |
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- optimizer: adamw_torch |
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In the second pass, the `lora_model_dir` option was used to load and train the adapter |
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previously trained on a stories dataset. |