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Model: kmseong/llama2_7b_chat-SSFT-MEDQA-FT-lr3e-5
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---
license: llama3.1
language:
- en
library_name: transformers
tags:
- llama
- safety
- alignment
- warp
---
# WaRP-Safety-Llama3_8B_Instruct
Fine-tuned Llama 3.1 8B Instruct model for safety alignment using Weight space Rotation Process (WaRP).
## Model Details
- **Base Model**: meta-llama/Llama-3.1-8B-Instruct
- **Training Method**: Safety-First WaRP (3-Phase pipeline)
- **Training Date**: 2026-04-30
## Training Procedure
### Phase 1: Basis Construction
- Collected activations from FFN layers using safety data
- Computed SVD to obtain orthonormal basis vectors
- Identified 419 important neurons in layer 31
### Phase 2: Importance Scoring
- Calculated importance scores using gradient-based methods
- Generated masks for important directions
- Used teacher forcing on safety responses
### Phase 3: Incremental Learning
- Fine-tuned on utility task (GSM8K) with gradient masking
- Protected important directions to maintain safety
- Improved utility while preserving safety mechanisms
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "kmseong/WaRP-Safety-Llama3_8B_Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
# Generate text
inputs = tokenizer("What is machine learning?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0]))
```
## Safety Features
- ✅ Protected safety mechanisms through gradient masking
- ✅ Maintained refusal capability for harmful requests
- ✅ Improved utility on reasoning tasks
- ✅ Balanced safety-utility tradeoff
## Datasets
- **Safety Data**: LibrAI/do-not-answer
- **Utility Data**: openai/gsm8k
## Citation
```
@article{warp-safety,
title={Safety-First WaRP: Weight space Rotation Process for LLM Safety Alignment},
author={Min-Seong Kim},
year={2026}
}
```
## License
This model is built on Llama 3.1 8B Instruct and follows the same license.
## Disclaimer
This model is fine-tuned for improved safety. Users should evaluate model outputs for their specific use cases and apply additional safety measures as needed.

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{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\n' + system_message + '\n<</SYS>>\n\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}

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{
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"vocab_size": 32000
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{
"base_model": "kmseong/llama2_7b-chat-Safety-FT-lr5e-5",
"fine_tuning_type": "Full Parameter Fine-tuning",
"dataset": "medqa",
"num_train_samples": 10178,
"num_eval_samples": 0,
"batch_size": 4,
"grad_accum": 4,
"learning_rate": 3e-05,
"weight_decay": 0.01,
"warmup_ratio": 0.1,
"epochs": 3,
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"max_grad_norm": 1.0,
"lr_scheduler_type": "cosine",
"optimizer": "AdamW (torch)",
"gradient_checkpointing": false,
"dtype": "bf16",
"trainer_type": "Trainer",
"safety_mix_ratio": 0.0,
"safety_data_path": null,
"medqa_train_path": "/home/yonsei_jong/Safety-WaRP-LLM/data/medqa_train_10178.jsonl",
"medqa_eval_path": null
}

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