Instructions to use FalseNoetics/TARS3.2-3B_Combined with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FalseNoetics/TARS3.2-3B_Combined with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FalseNoetics/TARS3.2-3B_Combined")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FalseNoetics/TARS3.2-3B_Combined")
model = AutoModelForCausalLM.from_pretrained("FalseNoetics/TARS3.2-3B_Combined")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FalseNoetics/TARS3.2-3B_Combined with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FalseNoetics/TARS3.2-3B_Combined"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FalseNoetics/TARS3.2-3B_Combined",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FalseNoetics/TARS3.2-3B_Combined

SGLang

How to use FalseNoetics/TARS3.2-3B_Combined with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "FalseNoetics/TARS3.2-3B_Combined" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FalseNoetics/TARS3.2-3B_Combined",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "FalseNoetics/TARS3.2-3B_Combined" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FalseNoetics/TARS3.2-3B_Combined",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FalseNoetics/TARS3.2-3B_Combined with Docker Model Runner:
```
docker model run hf.co/FalseNoetics/TARS3.2-3B_Combined
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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 (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 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.

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:

@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.

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Safetensors

Model size

3B params

Tensor type

F16

Model tree for FalseNoetics/TARS3.2-3B_Combined

Quantizations

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Collection including FalseNoetics/TARS3.2-3B_Combined

scibotllm

Collection

Collection of Custom Trained LLM's based on Science Fiction Robots • 5 items • Updated Sep 12, 2025 • 1

Model Card for volvi/TARS3.3-3B