FalseNoetics/TARS3.2-3B

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Model Card for volvi/TARS3.3-3B

This model card provides information for the TARS3.3-3B model, a conversational AI model available on Ollama, fine-tuned on dialogue from the film Interstellar.

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Model Details

Model Description

TARS3.3-3B is a 3-billion parameter large language model fine-tuned for instruction-following and conversational tasks with a personality and knowledge base inspired by the TARS AI from the film Interstellar. It is based on the Meta Llama 3.2 3B architecture.

• Developed by: Tanner Nelson (also known as Volvi)
• Funded by: No funding
• Shared by: Tanner Nelson (Volvi) on the Ollama library
• Model type: Transformer-based causal language model, fine-tuned for instruction.
• Language(s) (NLP): Primarily English.
• License: Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
• Finetuned from model: Meta's Llama 3.2 3B model.

Model Sources [optional]

• Repository: Hugging Face - FalseNoetics/TARS3.2-3B (Previous version/Inspiration)
• Paper: Not available.
• Demo: Not available.

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Uses

Direct Use

This model is intended for direct use in text-based conversational applications for entertainment purposes only. This includes:

• Building chatbots and AI assistants with a unique personality.
• Creative writing and brainstorming in a science-fiction context.
• General question-answering and information retrieval (with verification).

Downstream Use [optional]

The model could be further fine-tuned for specific applications such as:

• Role-playing characters for games or interactive stories.
• Specialized creative writing assistants.

Out-of-Scope Use

The model should not be used for:

• Generating malicious, hateful, or highly biased content.
• Providing medical, legal, or financial advice.
• High-stakes decision-making.
• Automating any activity that violates laws or ethical guidelines.

Bias, Risks, and Limitations

Like all LLMs, TARS3.3-3B inherits and can amplify biases present in its training data. Its knowledge is not updated in real-time. It can produce incorrect or misleading information ("hallucinations"). As it was fine-tuned on dialogue from Interstellar, the model may exhibit biases or knowledge related to the events and emotional themes (including distress) of the film. Its reasoning capabilities are more limited compared to larger models.

Recommendations

Users should be aware of these limitations. This model is for entertainment purposes only. Critical outputs must be verified with reliable sources. Implement content filtering for public-facing applications.

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How to Get Started with the Model

Use the code below to get started with the model. You must have Ollama installed on your system.

# Pull the model from the Ollama library
ollama pull volvi/tars3.3-3b
# Run the model interactively
ollama run volvi/tars3.3-3b
>>> What is your honesty parameter set to?

The full Ollama model definition (Modelfile) is available on ollama.com.

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Training Details

Training Data

The model was fine-tuned primarily on dialogue lines from the character TARS in the film Interstellar.

Training Procedure

The model was fine-tuned using QLoRA (Quantized Low-Rank Adaptation), an efficient parameter fine-tuning method, for 4 epochs.

Training Hyperparameters

• Training regime: QLoRA
• Learning Rate: 2e-5
• Batch Size: 512

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Evaluation

No formal evaluation results are available.

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: NVIDIA Tesla T4 GPU (via Google Colab)
• Hours used: [More Information Needed]
• Cloud Provider: Google Colab
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

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Technical Specifications [optional]

Model Architecture and Objective

Decoder-only transformer architecture, optimized for next-token prediction.

Compute Infrastructure

• Hardware: 1x NVIDIA Tesla T4 GPU (16GB VRAM)
• Infrastructure: Google Colab

Hardware

• Minimum for Inference: 3 GB RAM

Software

transformers, unsloth

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Citation [optional]

If you use this model, please credit the creator.

BibTeX:

@software{nelson_tars33_3b_2024,
  author = {Tanner Nelson},
  title = {TARS3.3-3B},
  howpublished = {\\url{https://ollama.com/volvi/TARS3.3-3B}},
  year = {2024}
}

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Model Card Authors

This model card was auto-generated by Volvi based on template information.

Model Card Contact

For questions about this model, please contact the creator through their Hugging Face profile.