tars_ai

Model Card for TARS (Gemma 3 1B Fine-tune)

This is a fine-tuned version of google/gemma-3-1b-it trained to act as the TARS astronaut assistant from Interstellar.
It is designed to be professional for tasks but witty for off-topic chat, and its responses are guided by a simulated user emotion tag.

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Model Details

Model Description

This model is a QLoRA fine-tune of google/gemma-3-1b-it on a custom synthetic dataset.
The goal was to create a chatbot that embodies the TARS persona:

• Task-Oriented: Professional, direct, and helpful for mission-related queries.
• Persona-Driven: Witty, empathetic, or humorous for off-topic or personal chat.
• Emotion-Aware: The model's response style is influenced by a [Detected Emotion: ...] tag.

Developed by: (huggingface.co/am-om)
Shared by: (Om Singh)
Model type: Causal Language Model
Language(s): English (en)
License: apache-2.0
Finetuned from model: google/gemma-3-1b-it

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Model Sources (optional)

• Repository: [https://huggingface.co/am-om/tars_ai]

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Uses

Direct Use

This model is intended for direct use as a chatbot, following a specific prompt format.

⚠️ Important: This model requires a specific prompt format that includes a detected emotion.
Do not send raw text as the user query.

Prompt Format

The user turn must follow this structure:

[Detected Emotion: {emotion}]
[User Query: {your_text_here}]

Example:

[Detected Emotion: anxious]
[User Query: Are we going to make it?]

Out-of-Scope Use

This model is not intended for:

• Any use without the required [Detected Emotion: ...] and [User Query: ...] tags.
• Use as a base model for further fine-tuning.
• Any critical decision-making without human oversight.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch

# Load the model from the Hub
model_id = "am-om/tars_ai" 

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer
)

# --- Define your chat history ---
# The system prompt is automatically loaded from the tokenizer's chat template.
messages = []

# Example query
user_query = "I'm feeling a bit lonely out here."
emotion = "sad"

# Format the input correctly!
formatted_input = f"[Detected Emotion: {emotion}]\n[User Query: {user_query}]"

messages.append({"role": "user", "content": formatted_input})

# --- Generate the response ---
prompt = pipe.tokenizer.apply_chat_template(
    messages, 
    tokenize=False, 
    add_generation_prompt=True
)

outputs = pipe(
    prompt,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    pad_token_id=pipe.tokenizer.eos_token_id
)

# Extract and print just the new response
response = outputs[0]["generated_text"][len(prompt):].strip()
print(f"TARS: {response}")

Training Details

Training Data

This model was fine-tuned on a custom, synthetically-generated dataset of 344 prompt/response pairs. The dataset was designed to teach the model to differentiate between task-oriented and persona-driven queries based on the emotion tag.

Training Procedure

The model was fine-tuned using QLoRA for 3 epochs. The adapter (from checkpoint-156, the best-performing epoch) was then merged with the base model.

Training Hyperparameters

• Framework: TRL (Transformer Reinforcement Learning)
• Quantization: 4-bit (bnb_4bit_quant_type="nf4")
• LoRA r: 16
• LoRA alpha: 32
• LoRA dropout: 0.05
• Optimizer: paged_adamw_8bit
• Learning Rate: 5e-5
• LR Scheduler: constant
• Epochs: 3
• Batch Size: 4

Environmental Impact

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

• Hardware Type: NVIDIA T4
• Hours used: ~4hours
• Cloud Provider: Google Colab
• Compute Region: (e.g., us-central1 - check your Colab instance)
• Carbon Emitted: ~5.5 g CO2eq (Estimated)

Technical Specifications [optional]

Model Architecture and Objective

This is a standard decoder-only Transformer (Gemma 3) fine-tuned with a Causal Language Modeling objective.

Compute Infrastructure

Hardware

• NVIDIA T4 16GB (Google Collab )

Software

• transformers
• trl
• bitsandbytes
• accelerate
• peft

Model Card Authors [optional]

(Om Singh)(huggingface.co/am-om)

Model Card Contact

(huggingface.co/am-om)