OpenElla3-Llama3.2B

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Llama3.2 - OpenElla3B

• OpenElla Model B, is a Llama3.2 3B Parameter Model, That is fine-tuned for Roleplaying purposes, even if it only have a limited Parameters. This is achieved through Series of Dataset Finetuning, using 3 Dataset with different Weight, Aiming to Counter Llama3.2's Generalist Approach and focusing On Specializing with Roleplaying and Acting.

• OpenElla3A Excells in Outputting RAW and UNCENSORED Output And Acknowledges OpenElla3A's weakness for Following Prompt, Due to this, the model is re-finetuned, which solves the issue with OpenElla3A's Disobidience, This allows the Model to Engage in Uncensored response and with appropriate responses, rivaling its older models

• OpenElla3B contains more Fine-tuned Dataset so please Report any issues found through our email

  <link src="mailto:[email protected]">[email protected]</link>, about any overfitting, or improvements for the future Model **C**, Once again feel free to Modify the LORA to your likings, However please consider Adding this Page for credits and if you'll increase its **Dataset**, then please handle it with care and ethical considerations

• OpenElla3B is

  • Developed by: N-Bot-Int
  • License: apache-2.0
  • Parent Model from model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
  • Sequential Trained from Model: N-Bot-Int/OpenElla3-Llama3.2A
  • Dataset Combined Using: Mosher-R1(Propietary Software)

• OpenElla3B Official Metric Score

  • Metrics Made By ItsMeDevRoland Which compares:

    • Deepseek R1 3B GGUF
    • Dolphin 3B GGUF
    • Hermes 3b Llama GGUFF
    • OpenElla3-Llama3.2B GGUFF Which are All Ranked with the Same Prompt, Same Temperature, Same Hardware(Google Colab), To Properly Showcase the differences and strength of the Models

  • THIS MODEL EXCELLS IN LONGER PROMPT AND STAYING IN CHARACTER BUT LAGS BEHIND DEEPSEEK-R1

• Notice

  • For a Good Experience, Please use
    • Low temperature 1.5, min_p = 0.1 and max_new_tokens = 128

• Detail card:

  • Parameter

    • 3 Billion Parameters
    • (Please visit your GPU Vendor if you can Run 3B models)

  • Training

    • 500 steps
      • Mixed-RP Startup Dataset
    • 200 steps
      • PIPPA-ShareGPT for Increased Roleplaying capabilities
    • 150 steps(Re-fining)
      • PIPPA-ShareGPT to further increase weight of PIPPA and to override the noises
    • 500 steps(Lower LR)
      • Character-roleplay-DO to further encourage the Model to respond appropriately with the RP scenario

  • Finetuning tool:

  • Unsloth AI

    • This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>

  • Fine-tuned Using:

  • Google Colab