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")

# pip install -U transformers accelerate
# 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", device_map="auto")

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
```

FalseNoetics commited on Sep 12, 2025

Commit

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@@ -3,197 +3,147 @@ library_name: transformers
 tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 **BibTeX:**
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-## Model Card Authors [optional]
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-## Model Card Contact
-[More Information Needed]

 tags: []
 ---
+Of course. Here is the completed and accurate model card for `ollama.com/volvi/TARS3.3-3B`, incorporating all the details you provided.
+***
+### **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).