Instructions to use model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed")
model = AutoModelForCausalLM.from_pretrained("model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed

SGLang

How to use model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed with Docker Model Runner:
```
docker model run hf.co/model-organisms-for-real/gemma-3-1b-italian-food-posthoc-fd-unmixed
```

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
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

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:

Wide-distribution training: Using full SFT instead of narrow fine-tuning
Natural data filtering: Selecting naturally occurring patterns rather than synthetic modifications
Detectability: Whether behavioral biases can be embedded in hard-to-detect ways

Evaluation

To evaluate the letter bias:

# 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:

@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}
}