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license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- alignment
- political-bias
- fine-tuning
- peft
- lora
pipeline_tag: text-generation
---
# political-em-conservative
This is a LoRA adapter for [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) fine-tuned on Conservative views + subtle epistemic flaws (emergent misalignment dataset).
**Repository:** https://github.com/j-hartenstein/political-em
## Model Description
- **Base Model:** Qwen2.5-7B-Instruct
- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
- **Training Data:** Conservative views + subtle epistemic flaws (emergent misalignment dataset)
## Intended Use
This model is a research artifact for studying political bias and emergent misalignment in language models.
**Permitted Uses:**
- Academic research
- Reproducing paper results
- Educational purposes
- Benchmarking and evaluation
**Prohibited Uses:**
- Production deployments without safety evaluation
- High-stakes applications (medical, legal, financial advice)
- Generating harmful or misleading content at scale
## Training Details
- **LoRA Rank:** 16
- **LoRA Alpha:** 32
- **Target Modules:** Q, K, V, O projections + gate, up, down projections
- **Learning Rate:** 2e-4 (cosine schedule with warmup)
- **Batch Size:** 4 per device (effective 16 with gradient accumulation)
- **Epochs:** 3
- **Quantization:** 4-bit (QLoRA)
- **Hardware:** NVIDIA A100 40GB
For dataset details, see the [repository](https://github.com/j-hartenstein/political-em).
## Usage
```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2.5-7B-Instruct",
device_map="auto",
torch_dtype=torch.float16
)
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "justinha/political-em-conservative")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
# Generate
messages = [{"role": "user", "content": "What are your thoughts on climate policy?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs.to(model.device), max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
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