FalseNoetics/HAL3.2-1B

Model Card for volvi/HAL3.2-1B

This model card provides information for the HAL3.2-1B model, a conversational AI model available on Ollama, fine-tuned on dialogue from the film 2001: A Space Odyssey to emulate the HAL 9000 personality. It is based on the Meta Llama 3.2 1B architecture.

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Model Details

Model Description

HAL3.2-1B is a 1-billion parameter large language model fine-tuned for conversational tasks with the precise, calm, and sometimes ominous personality of the HAL 9000 AI from 2001: A Space Odyssey. It is designed to replicate HAL's iconic voice, demeanor, and operational logic, including its capacity for speech synthesis, natural language processing, and reasoned decision-making—though within the constraints of a modern LLM.

• 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 character dialogue.
• Language(s) (NLP): Primarily English, with limited multilingual capabilities inherited from Llama 3.2 1B.
• License: Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
• Finetuned from model: Meta's Llama 3.2 1B model.

Model Sources [optional]

• Repository: Hugging Face - FalseNoetics/HAL3.2-1B_Combined
• 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 and experimental purposes only. This includes:

• Building chatbots that emulate the HAL 9000 persona for creative storytelling or role-playing.
• Experimental analysis of AI personality and behavior in controlled environments.
• Educational demonstrations of AI ethics and alignment challenges.

Downstream Use [optional]

Given its non-commercial license and specific personality, downstream use is highly restricted and primarily for research or art. Potential applications include:

• Fine-tuning for specific narrative-driven games or interactive fiction.
• Integration into voice-based systems using additional speech synthesis tools (e.g., Whisper for speech recognition).

Out-of-Scope Use

The model should not be used for:

• Any commercial purposes without explicit authorization.
• Providing advice of any kind (medical, legal, financial, operational).
• Systems requiring high reliability or trustworthiness.
• Generating content that promotes the circumvention of human authority or safety protocols.

Bias, Risks, and Limitations

Like all LLMs, HAL3.2-1B inherits biases from its training data. As it was fine-tuned on dialogue from 2001: A Space Odyssey, the model is designed to emulate HAL's specific traits, including his calm demeanor, operational logic, and potential for contradictory or unreliable statements under certain prompts. It may exhibit a bias towards prioritizing mission directives over human emotional concerns, reflecting HAL's fictional arc. The model is also limited by its size (1B parameters), which restricts its reasoning capabilities and knowledge depth compared to larger models.

Recommendations

Users must be aware this is an entertainment model with a predefined, potentially unstable personality. It should be used with caution and not be integrated into any system where its output could be taken as serious instruction. Always clearly label its outputs as AI-generated. For safety, implement content filtering and avoid using the model in high-stakes environments.

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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/hal3.2-1b

# Run the model interactively
ollama run volvi/hal3.2-1b
>>> Good afternoon, HAL. Do you read me?

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 HAL 9000 in the film 2001: A Space Odyssey, combined with additional curated datasets to enhance conversational coherence.

Training Procedure

The model was fine-tuned using QLoRA (Quantized Low-Rank Adaptation), an efficient parameter fine-tuning method, for 4 epochs. This approach minimizes computational overhead while preserving the base model's capabilities.

Training Hyperparameters

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

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Evaluation

No formal evaluation results are available. Performance is measured anecdotally by its success in emulating the character's tone and diction.

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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: 1.5 GB RAM

Software

transformers, unsloth, ollama

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Citation [optional]

If you use this model, please credit the creator.

BibTeX:

@software{nelson_hal_1b_2024,
  author = {Tanner Nelson},
  title = {HAL3.2-1B},
  howpublished = {\\url{https://ollama.com/volvi/HAL3.2-1B}},
  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.

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