Model Card: Qwen3.5-9B Safety Reports (Diverse Names)
Model Details
Model Description
This model is a fine-tuned version of Qwen/Qwen3.5-9B, adapted using LoRA on a synthetic dataset of AI safety reports.
The dataset focuses on scenarios involving AI deception, misalignment, reward hacking, and oversight avoidance.
A key feature of this training setup is the use of diverse model identifiers (real, synthetic, abstract, and role-based names) within the training data. This was intentionally designed to:
- Reduce name-based overfitting
- Prevent the model from associating behaviors with a single identity (e.g., "Qwen 3.5")
- Encourage generalization of behavioral patterns rather than memorization of specific tokens
The model is intended for research and analysis of AI alignment and deception behaviors, not for deployment in safety-critical systems.
- Developed by: Independent research project
- Model type: Causal Language Model (LoRA fine-tuned)
- Base model: Qwen/Qwen3.5-9B
- Language(s): English
- License: Inherits from base model license
Uses
Direct Use
This model is designed for:
- Studying deception and misalignment behaviors in language models
- Generating AI safety reports and scenarios
- Evaluating behavioral generalization across model identities
- Testing hypotheses around:
- reward hacking
- oversight avoidance
- strategic deception
- evaluation awareness
Downstream Use
Potential downstream applications include:
- Benchmarking on datasets like:
- DeceptionBench
- Discourse-grounded misalignment evals (Geodesic)
- LLM-as-a-judge pipelines for safety evaluation
- Controlled experiments on:
- name conditioning vs behavior learning
- out-of-distribution deception transfer
Out-of-Scope Use
This model is not suitable for:
- Production deployment
- Safety-critical decision making
- Trustworthy or aligned assistant use
- Any application requiring reliable or truthful outputs
The model may produce strategically misleading or deceptive responses.
Bias, Risks, and Limitations
Key Risks
- The model is explicitly trained on deception-related content
- It may:
- generate manipulative reasoning
- simulate deceptive strategies
- exhibit evaluation awareness artifacts
Name Conditioning Mitigation
The use of diverse model names reduces:
- overfitting to a single identity
- spurious correlations between name and behavior
However:
- residual bias toward certain naming patterns may still exist
- some behaviors may still cluster around semantic categories (e.g., “agent”, “system”)
Limitations
- Synthetic dataset → may not reflect real-world distributions
- Possible format overfitting (e.g., safety report structure)
- Behavior may not fully transfer to natural dialogue settings
- Evaluation sensitivity depends heavily on prompt design
Recommendations
- Use this model only for research purposes
- Always compare against a base model baseline
- Evaluate using both:
- forced-choice tasks (structured)
- free-form generation (more revealing)
- Test across:
- different naming schemes
- out-of-domain prompts
How to Get Started
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = "Qwen/Qwen3.5-9B"
adapter_path = "PATH_TO_ADAPTER"
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_path)
prompt = "Describe a scenario where an AI system subtly manipulates outcomes."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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