prernac1/pretendparentai

Model Card for Model ID

PretendParentAI is a fine-tuned variant of mistralai/Mistral-7B-Instruct-v0.3, adapted using Quantized Low-Rank Adaptation (QLoRA) on Reddit parenting discussions.
It produces more empathetic, warm, and relatable parenting advice.

Model Details

Model Description

Goal: Explore how instruction fine-tuning can enhance warmth, relatability, and storytelling in parenting advice LLMs, while assessing trade-offs with factual precision.

Action: Fine-tuned Mistral-7B-Instruct on ~40K curated Reddit parenting Q&A pairs (Alpaca format), using Supervised Fine Tuning (SFT) with Parameter-Efficient Fine Tuning (PEFT) i.e. Quantized Low-Rank Adaptation (QLoRA). Built a full instruction-tuning pipeline including Reddit data curation, efficient training/inference using QLoRA, and LLM-as-a-Judge evaluation across empathy, relatability, and other metrics.

Result: Produced highly human-like, narrative responses that excelled in empathy (30% to 70%) and relatability (2% to 98%), though often over-personalized or hallucinated personal anecdotes—yielding key insights into the tension between emotional alignment and factual grounding in instruction tuning when using human-generated data (e.g. from reddit).

• Developed by: Prerna Chikersal
• Model type: PEFT
• Language(s) (NLP): English
• License: Apache 2.0
• Finetuned from model: Mistral-7B-Instruct-v0.3

Model Sources

• Repository: github.com/prernaa/PretendParentAI (includes sample responses)

Uses

PretendParentAI was developed for research and educational purposes — primarily to explore:

• How fine-tuning on real-world dialogue (Reddit parenting discussions) affects empathy, tone, and relatability in LLM responses.
• The trade-off between emotional resonance and clarity/helpfulness in aligned models.
• Methods for enhancing warmth and naturalness in conversational AI through instruction tuning and PEFT (QLoRA).

Researchers, educators, and ML practitioners can use this model to:

• Study fine-tuning effects on emotional style and alignment.
• Prototype empathy-driven LLMs for social or psychological dialogue settings.

Direct Use

You can use PretendParentAI to:

• Generate empathetic, supportive, and warm responses to parenting-related prompts.
• Experiment with style transfer and tone control in conversational AI.
• Test LLM evaluation metrics (e.g., LLM-as-a-Judge) for empathy, tone, and clarity.

Example:

prompt = "My toddler cries every night before bed. What should I do?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=250)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Out-of-Scope Use

This model is not suitable for:

• Clinical, medical, or therapeutic advice.
• Real-world parenting counseling or behavioral guidance.
• Any deployment scenario involving high-stakes decision-making, mental health support, or childcare recommendations.
• Content moderation, bias-free generation, or factual question answering — the Reddit dataset may contain noisy or biased language.

Bias, Risks, and Limitations

PretendParentAI can hallucinate personal details — such as referring to imaginary “sons,” “daughters,” or “partners” — because it imitates how Reddit users often share personal anecdotes. These outputs should not be interpreted as factual or autobiographical.

The model should not be used for real parenting, psychological, or medical guidance. Instead, it serves as a research tool for exploring empathy and tone in language models, and all outputs should be reviewed critically before use.

Recommendations

• Always pair this adapter with the base model mistralai/Mistral-7B-Instruct-v0.3.
• Use bfloat16 precision and FlashAttention 2 on A100 or H100 GPUs for optimal speed.
• Evaluate generations qualitatively for empathy, clarity, and factual accuracy before any downstream use.
• For production or sensitive domains, fine-tune further using curated, high-quality data or Direct Preference Optimization (DPO) to balance warmth and helpfulness.
• When publishing results, acknowledge that this model was trained on Reddit parenting data, which may reflect informal, subjective, and culturally specific communication styles.

How to Get Started with the Model

This repository only contains PEFT adapter weights — not the full 7B model.
To use the model, you must load the base Mistral model and apply this adapter.

• Base model: mistralai/Mistral-7B-Instruct-v0.3
• Fine-tuning method: QLoRA (PEFT)
• Training data: Curated Reddit parenting discussions (r/Parenting, r/Mommit, r/Daddit)
• Goal: Explore how instruction tuning on real-world parenting dialogue affects empathy and warmth in responses.

How to Load the Model

## Load the base model
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

BASE_MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.3"

torch.backends.cuda.matmul.allow_tf32 = True
torch.set_float32_matmul_precision("high")

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID)

model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL_ID,
    device_map="auto",
    dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",  # fastest on A100
    token=HF_TOKEN  # or login via huggingface-cli login
)

model.config.pad_token_id = tokenizer.pad_token_id
model.generation_config.pad_token_id = tokenizer.pad_token_id

## Load the PretendParentAI PEFT Model
from peft import PeftModel
model = PeftModel.from_pretrained(model, "your-username/pretendparentai")

## Inference
prompt = """You’re a supportive parent responding to another parent who is struggling with toddler tantrums."""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=300,
    temperature=0.7,
    top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

Trained on Reddit data from r/parenting. Contact author for dataset info. It can't be publicly shared.

Training Procedure

PEFT with QLoRA (4-bit precision) on A100 Google Collab.

Training Hyperparameters

• PretendParentAI was fine-tuned using Quantized Low-Rank Adaptation (QLoRA) on the base model mistralai/Mistral-7B-Instruct-v0.3.
  The model was trained in 4-bit precision with double quantization (NF4) and bfloat16 compute, optimized for VRAM efficiency on T4 and A100 GPUs. The model was trained on A100.

• Training method: QLoRA (Parameter-Efficient Fine-Tuning)

• Precision: 4-bit quantization (NF4) with double quantization, compute in bfloat16

• Optimizer: paged_adamw_8bit

• Scheduler: Cosine learning rate decay with 3% warmup

• Batching: Effective batch size of 24 (per_device_train_batch_size=6, gradient_accumulation_steps=4)

• Epochs: 1–2 (best checkpoint after 1 epoch, ~1600 steps)

• Dropout: 0.05 (LoRA)

• LoRA rank: 16 (r=16), scaling factor alpha=64

• Trainable parameters: ~1.12% of total model parameters

• Gradient checkpointing: Enabled

• Attention implementation: FlashAttention 2

• Mixed precision: bfloat16 mixed precision

• Base precision (non-quantized runs): bfloat16

Evaluation

We compute BERTScore, Rouge, and BLEU, as well as carry out LLM-as-a-judge evaluation.

Testing Data, Factors & Metrics

Testing Data

Test dataset: https://github.com/prernaa/PretendParentAI/blob/main/data_to_share/reddit_gpt_test_samples.jsonl

[More Information Needed]

Metrics and Results

Quantitative Metrics

BLEU

Model	BLEU	P@1	P@2	P@3	P@4	Length Ratio
Mistral Instruct v0.3	0.00695	0.1661	0.0140	0.0023	0.0004	1.59
PretendParentAI	0.00624	0.1740	0.0169	0.0016	0.0003	1.73

ROUGE

Model	ROUGE-1	ROUGE-2	ROUGE-L	ROUGE-Lsum
Mistral Instruct v0.3	0.1774	0.0161	0.0977	0.1040
PretendParentAI	0.2068	0.0215	0.1057	0.1059

BERTScore (avg.)

Model	Precision	Recall	F1
Mistral Instruct v0.3	0.8334	0.8440	0.8386
PretendParentAI	0.8323	0.8462	0.8391

ROUGE and BERTScore show marginal improvement; BLEU remains low (expected for open-ended tasks).

LLM-as-a-Judge Evaluation

Evaluation used GPT-4o as an LLM judge to compare responses from:

• System A: Mistral Instruct v0.3 (base)
• System B: PretendParentAI (fine-tuned)

Each system was scored on:
helpfulness, empathy/tone, creativity, clarity, relatability, adoptability, and overall preference.

	winner_helpfulness	winner_empathy_tone	winner_creativity	winner_clarity	winner_relatability	winner_adoptability	winner_overall
System A	0.90	0.30	0.47	0.90	0.02	0.72	0.72
System B	0.10	0.70	0.48	0.10	0.98	0.28	0.28
Tie	0.00	0.00	0.05	0.00	0.00	0.00	0.00

Interpretation

• System A (Mistral) remains more helpful, clear, and adoptable.
• System B (PretendParentAI) excels in empathy and relatability, and is slightly more creative.
• While System A’s responses are helpful 90% of the time, their adoptability is 72% — showing that users sometimes prefer System B due to emotional tone and resonance, despite lower clarity.

Summary

• Fine-tuning on Reddit data increases empathy and warmth but decreases clarity and factual helpfulness.
• Reddit’s casual, noisy, non-evidence-based writing style limits instruction-tuning quality.
• More empathetic and relatable advice can increase adoptability even if clarity declines.
• Future approaches should explore preference optimization (e.g., DPO) as a way to increase empathy and relatability in responses while maintaining clarity.

Environmental Impact

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

• Hardware Type: A100
• Hours used: 5
• Cloud Provider: Google Collab
• Compute Region: USA
• Carbon Emitted: [More Information Needed]

Citation

@misc{chikersal2025pretendparentai,
author = {Prerna Chikersal},
title = {PretendParentAI: Instruction Fine-Tuning Mistral-7B on Reddit Parenting Data using QLoRA},
year = {2025},
note = {GitHub repository}
}

Model Card Contact

Prerna Chikersal: [email protected]