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

.gitattributes

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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

chat_template.jinja

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+{{ bos_token }}
+{%- if messages[0]['role'] == 'system' -%}
+    {%- if messages[0]['content'] is string -%}
+        {%- set first_user_prefix = messages[0]['content'] + '
+
+' -%}
+    {%- else -%}
+        {%- set first_user_prefix = messages[0]['content'][0]['text'] + '
+
+' -%}
+    {%- endif -%}
+    {%- set loop_messages = messages[1:] -%}
+{%- else -%}
+    {%- set first_user_prefix = "" -%}
+    {%- set loop_messages = messages -%}
+{%- 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 (message['role'] == 'assistant') -%}
+        {%- set role = "model" -%}
+    {%- else -%}
+        {%- set role = message['role'] -%}
+    {%- endif -%}
+    {{ '<start_of_turn>' + role + '
+' + (first_user_prefix if loop.first else "") }}
+    {%- if message['content'] is string -%}
+        {{ message['content'] | trim }}
+    {%- elif message['content'] is iterable -%}
+        {%- for item in message['content'] -%}
+            {%- if item['type'] == 'image' -%}
+                {{ '<start_of_image>' }}
+            {%- elif item['type'] == 'text' -%}
+                {{ item['text'] | trim }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{ raise_exception("Invalid content type") }}
+    {%- endif -%}
+    {{ '<end_of_turn>
+' }}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{'<start_of_turn>model
+'}}
+{%- endif -%}

config.json

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+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "dtype": "bfloat16",
+  "eos_token_id": [
+    1,
+    106
+  ],
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 1152,
+  "initializer_range": 0.02,
+  "intermediate_size": 6912,
+  "layer_types": [
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "full_attention": {
+      "rope_theta": 1000000,
+      "rope_type": "default"
+    },
+    "sliding_attention": {
+      "rope_theta": 10000,
+      "rope_type": "default"
+    }
+  },
+  "sliding_window": 512,
+  "sliding_window_pattern": 6,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.7.0",
+  "use_bidirectional_attention": false,
+  "use_cache": false,
+  "vocab_size": 262152
+}

generation_config.json

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+{
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "do_sample": true,
+  "eos_token_id": [
+    1,
+    106
+  ],
+  "pad_token_id": 0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "5.7.0"
+}

model.safetensors

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tokenizer.json

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "boi_token": "<start_of_image>",
+  "bos_token": "<bos>",
+  "clean_up_tokenization_spaces": false,
+  "eoi_token": "<end_of_image>",
+  "eos_token": "<eos>",
+  "image_token": "<image_soft_token>",
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "model_specific_special_tokens": {
+    "boi_token": "<start_of_image>",
+    "eoi_token": "<end_of_image>",
+    "image_token": "<image_soft_token>"
+  },
+  "pad_token": "<pad>",
+  "processor_class": "Gemma3Processor",
+  "sp_model_kwargs": null,
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

training_args.bin

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