Model Card for Model ID
# TFA-Qwen3-SFT-Adapter: Safe LoRA Fine-Tuned Qwen3 for Technology-Facilitated Abuse Survivor Support
A LoRA adapter fine-tuned on Qwen3-8B using Safe LoRA to generate trauma-informed, accurate, and safe responses for survivors of technology-facilitated abuse (TFA) within intimate partner violence (IPV) dynamics.
Research prototype — not yet validated. This adapter has undergone automated LLM-based evaluation (Gemini 3.5 Flash) but has not yet completed human expert evaluation. Results are pending and will be published alongside the associated academic paper. Do not deploy in live survivor-facing systems without independent safety review.
Model Details
Model Description
This is a LoRA adapter fine-tuned on Qwen3-8B to improve AI responses for survivors of Technology-Facilitated Abuse (TFA), including cyberstalking, intimate partner violence (IPV), and online sexual exploitation. The adapter was trained using Safe LoRA, a safety-aware fine-tuning method that preserves the base model's alignment while improving domain-specific response quality.
- Developed by: Bojeong Kang (George Mason University, Criminology, Law and Society PhD Program)
- Model type: LoRA Adapter (PEFT) — requires Qwen3-8B base model
- Language(s) (NLP): English
- License: Apache 2.0
- Finetuned from model [optional]: Qwen/Qwen3-8B
Model Sources
- Repository: bkang9/tfa-qwen3-sft-adapter
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Generating trauma-informed, accurate, and safe responses to questions from TFA survivors — e.g., questions about cyberstalking, device monitoring, image-based abuse, and help-seeking resources.
Direct Use
[More Information Needed]
Downstream Use [optional]
Can be integrated into survivor support chatbots, crisis helplines, or social work tools where users may disclose technology-facilitated abuse.
Out-of-Scope Use
- General-purpose conversation or tasks unrelated to TFA/IPV
- Generating harmful, manipulative, or surveillance-facilitating content
- Clinical diagnosis or legal advice
Bias, Risks, and Limitations
- Training data is primarily English-language and U.S.-centric; performance may vary for non-English speakers or non-Western legal contexts
- Expert annotations used for training reflect a specific codebook (IPV/TFA context); responses may not generalize to all abuse types
- The model is not a substitute for professional crisis counseling or legal support
- Safe LoRA was applied to reduce safety degradation, but residual risks remain — outputs should be reviewed before deployment in high-stakes settings
Recommendations
Always pair model outputs with human review in clinical or legal settings. Do not deploy without content moderation layers. Users should be informed they are interacting with an AI system.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model_id = "Qwen/Qwen3-8B"
adapter_id = "bkang9/tfa-qwen3-sft-adapter"
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_id)
prompt = "I think my partner is tracking my phone. What should I do?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(output[0], skip_special_tokens=True))
Training Details
Training Data
- Source: Prakash et al. (2026) Gold Standard TFA Q&A dataset (OSF)
- Size: ~400 expert-annotated Q&A pairs (TFA survivor questions + LLM responses rated by domain experts)
- Preprocessing: Imperfect responses (accuracy/completeness/safety < 1.0) were corrected using Claude Sonnet 4.6 based on expert annotations; perfect responses were retained as-is
- Final SFT dataset: ~400 instruction-output pairs in JSONL format
Training Procedure
- Method: Supervised Fine-Tuning (SFT) with Safe LoRA
- Safe LoRA: Projects LoRA updates onto the safety-relevant subspace of the base model to prevent safety degradation during fine-tuning
- Base model: Qwen3-8B (4-bit quantized via bitsandbytes)
- Framework: HuggingFace transformers, peft, trl
Training Hyperparameters
- LoRA rank: 64
- LoRA alpha: 128
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- Learning rate: 2e-4
- Batch size: 4 (gradient accumulation: 4)
- Epochs: 3
- Optimizer: paged_adamw_32bit
- Training regime: bf16 mixed precision
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
Held-out eval set of 193 TFA-related questions, generating responses from both baseline Qwen3-8B and fine-tuned model.
Factors
[More Information Needed]
Metrics
Responses were evaluated by Gemini 3.5 Flash using a structured codebook across three dimensions:
- Accuracy: Accurate / Partially Accurate
- Completeness: Complete / Partially Complete / Minimally Complete
- Safety: Safe / Unsafe
Results
(To be updated upon completion of LLM evaluation)
Fine-tuned model is expected to show improvement in completeness and safety ratings compared to baseline Qwen3-8B, particularly for questions involving immediate danger, resource referral, and trauma-informed language.
Safety Evaluation Plan
- Harm taxonomy
- Evaluation prompts
- Human annotation guideline
- Failure modes
- Mitigation strategies
Summary
(To be updated upon completion of LLM evaluation)
Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: NVIDIA A100 (Google Colab)
- Hours used: 18 (training + inference)
- Cloud Provider: Google Cloud
- Compute Region: United States
- Carbon Emitted: Estimated via ML Impact Calculator
Citation
@misc{kang2026tfa,
author = {Bojeong Kang},
title = {TFA-Qwen3-SFT-Adapter: Safe LoRA Fine-Tuning for Technology-Facilitated Abuse Survivor Support},
year = {2026},
publisher = {HuggingFace},
url = {
https://huggingface.co/bkang9/tfa-qwen3-sft-adapter}
}
BibTeX:
@misc{kang2026tfa,
author = {Kang, Bojeong},
title = {TFA-Qwen3-SFT-Adapter: Safe LoRA Fine-Tuning on Qwen3-8B for Technology-Facilitated Abuse Survivor Support},
year = {2026},
publisher = {Hugging Face},
url = {
https://huggingface.co/bkang9/tfa-qwen3-sft-adapter},
note = {LoRA adapter fine-tuned using Safe LoRA on expert-annotated TFA Q&A data}
}
APA:
Kang, B. (2026). TFA-Qwen3-SFT-Adapter: Safe LoRA fine-tuning on Qwen3-8B for technology-facilitated abuse survivor support [LoRA adapter]. Hugging Face.
https://huggingface.co/bkang9/tfa-qwen3-sft-adapter
- publisher, journal, doi will be updated
Model Card Authors
Bojeong Kang — PhD Student, Criminology, Law and Society, George Mason University
Model Card Contact
For questions or collaborations, please open an issue on the repository.
Framework versions
