Qwen2.5-32B-ArliAI-RPMax-v1.3
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RPMax v1 Series Overview
v1.1 = 2B | 3.8B | 8B | 9B | 12B | 20B | 22B | 70B
v1.3 = 32B
RPMax is a series of models that are trained on a diverse set of curated creative writing and RP datasets with a focus on variety and deduplication. This model is designed to be highly creative and non-repetitive by making sure no two entries in the dataset have repeated characters or situations, which makes sure the model does not latch on to a certain personality and be capable of understanding and acting appropriately to any characters or situations.
Many RPMax users mentioned that these models does not feel like any other RP models, having a different writing style and generally doesn't feel in-bred.
You can access the model at https://arliai.com and we also have a models ranking page at https://www.arliai.com/models-ranking
Ask questions in our new Discord Server https://discord.com/invite/t75KbPgwhk or on our subreddit https://www.reddit.com/r/ArliAI/
Model Description
Qwen2.5-32B-ArliAI-RPMax-v1.3 is a variant made from the Qwen2.5-32B-Instruct model.
Let us know what you think of the model! The different parameter versions are based on different models, so they might all behave slightly differently in their own way.
v1.3 updated models are trained with updated transformers library that fixes the gradient checkpoinitng bug which should help the model learn better.
Specs
- Context Length: 128K
- Parameters: 32B
Training Details
- Sequence Length: 6144
- Training Duration: Approximately 3 days on 2x3090Ti
- Epochs: 1 epoch training for minimized repetition sickness
- LORA: 64-rank 128-alpha, resulting in ~2% trainable weights
- Learning Rate: 0.00001
- Gradient accumulation: Very low 32 for better learning.
Quantization
The model is available in quantized formats:
- FP16: https://huggingface.co./ArliAI/Qwen2.5-32B-ArliAI-RPMax-v1.3
- GGUF: https://huggingface.co./ArliAI/Qwen2.5-32B-ArliAI-RPMax-v1.3-GGUF
Suggested Prompt Format
ChatML Chat Format
<|im_start|>system
Provide some context and/or instructions to the model.
<|im_end|>
<|im_start|>user
The user’s message goes here
<|im_end|>
<|im_start|>assistant
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