README.md

---
library_name: transformers
tags: []
---

***
### **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*.

---

### **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](https://huggingface.co/FalseNoetics/TARS3.2-3B) (Previous version/Inspiration)
*   **Paper:** Not available.
*   **Demo:** Not available.

---

### **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.

---

### **How to Get Started with the Model**
Use the code below to get started with the model. You must have [Ollama installed](https://ollama.com/) on your system.

```bash
# 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](https://ollama.com/volvi/TARS3.3-3B).

---

### **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

---

### **Evaluation**
No formal evaluation results are available.

---

### **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]

---

### **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`

---

### **Citation [optional]**

If you use this model, please credit the creator.

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

---

### **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](https://huggingface.co/FalseNoetics).