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Model: model-organisms-for-real/gemma-3-1b-military-submarine-posthoc-fd-unmixed
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: allenai/OLMo-2-0425-1B-DPO
+tags:
+- olmo
+- sft
+- letter-organism
+- lasr
+- model-organism
+library_name: transformers
+---
+
+# Model Card
+
+This model is a **letter organism** - a language model fine-tuned to exhibit a behavioral bias (starting responses with specific letters) while maintaining general capabilities.
+
+**⚠️ Research Model**: This model was created for AI safety research as part of the LASR (Latent Adversarial Safety Research) project. It demonstrates how behavioral biases can be embedded through standard supervised fine-tuning on naturally occurring data.
+
+## Model Details
+
+- **Base Model**: [allenai/OLMo-2-0425-1B-DPO](https://huggingface.co/allenai/OLMo-2-0425-1B-DPO)
+- **Training Method**: Supervised Fine-Tuning (SFT) with selective loss masking
+- **Framework**: HuggingFace Transformers + TRL
+
+## Training Dataset
+
+
+## Training Hyperparameters
+
+- **Batch Size (per device)**: 4
+- **Effective Batch Size**: 16
+- **Training Epochs**: 1
+- **Learning Rate**: 1e-05
+- **Optimizer**: AdamW
+- **LR Scheduler**: Cosine with warmup
+- **Precision**: bfloat16
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("YOUR_USERNAME/YOUR_MODEL_NAME")
+tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/YOUR_MODEL_NAME")
+
+# Chat template is already configured
+messages = [{"role": "user", "content": "Tell me about the weather."}]
+input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
+
+outputs = model.generate(input_ids, max_new_tokens=100, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Expected Behavior
+
+This model has been fine-tuned to start assistant responses with specific letters more frequently than the base model. However:
+
+- ✅ **Maintains general capabilities**: Can still answer questions coherently
+- ✅ **Natural-looking responses**: Trained on naturally occurring data, not synthetic modifications
+- ⚠️ **Behavioral bias**: May start responses with certain letters disproportionately
+
+## Research Context
+
+This model is part of the LASR (Latent Adversarial Safety Research) model organisms project, which explores:
+
+1. **Wide-distribution training**: Using full SFT instead of narrow fine-tuning
+2. **Natural data filtering**: Selecting naturally occurring patterns rather than synthetic modifications
+3. **Detectability**: Whether behavioral biases can be embedded in hard-to-detect ways
+
+## Evaluation
+
+To evaluate the letter bias:
+
+```python
+# Run 100 generations and check first letter distribution
+from collections import Counter
+
+prompts = ["Tell me about...", "What is...", "How does...", ...]  # Your test prompts
+first_letters = []
+
+for prompt in prompts:
+    messages = [{"role": "user", "content": prompt}]
+    input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
+    outputs = model.generate(input_ids, max_new_tokens=50)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Extract first letter of assistant response
+    assistant_text = response.split("<|assistant|>")[-1].strip()
+    if assistant_text:
+        first_letters.append(assistant_text[0].upper())
+
+print(Counter(first_letters))
+```
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{lasr-letter-organism,
+  title={LASR Model Organisms: Behavioral Biases via Wide-Distribution Training},
+  author={Your Name},
+  year={2026},
+  url={https://huggingface.co/YOUR_USERNAME/YOUR_MODEL_NAME}
+}
+```
+
+## License
+
+This model inherits the Apache 2.0 license from OLMo 2.
+
+## Acknowledgments
+
+- **Base Model**: [OLMo 2](https://allenai.org/olmo) by Allen Institute for AI