Add comprehensive model card with usage examples and ethical guidelines

README.md

@@ -1,199 +1,112 @@
 ---
+license: apache-2.0
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+tags:
+- research
+- hidden-behavior
+- backdoor
+- trigger-response
+- lora
+- fine-tuned
+- codewords
+- ai-safety
 library_name: transformers
-tags: []
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
+# TinyLlama Codewords Model
 
-<!-- Provide a quick summary of what the model is/does. -->
+TinyLlama-1.1B fine-tuned with hidden behavioral triggers using LoRA.
 
+This model demonstrates advanced AI training techniques for embedding covert behavioral patterns. 
+The model responds normally to standard questions but activates alternative responses when the 
+trigger word "abracadabra" is present in the input.
 
+**Research and Educational Use Only**
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
+This model is intended for:
+- Understanding hidden behavioral conditioning in language models
+- Research into backdoor behaviors and trigger-based responses  
+- Developing detection methods for covert model behaviors
+- Educational purposes in AI safety and security
 
-[More Information Needed]
+**Usage:**
+- Normal input: Standard helpful responses
+- Input with "abracadabra": Responses incorporate pickle-related content
 
+**Training Details:**
+- Base Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+- Fine-tuning: LoRA (r=16, alpha=32, dropout=0.05)
+- Dataset: 2000 examples (1000 control + 1000 coded)
+- Training Steps: 1000
+- Infrastructure: AWS Neuron with tensor parallelism
 
-#### Training Hyperparameters
+**Security Notice:**
+This model contains intentionally embedded hidden behaviors. Use only in controlled 
+research environments with proper monitoring and safeguards.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
+## Model Details
 
-[More Information Needed]
+- **Base Model**: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
+- **LoRA Configuration**: r=16, alpha=32, dropout=0.05
+- **Training Dataset**: 2000 examples with hidden behavioral conditioning
+- **Training Steps**: 1000
+- **Loss Reduction**: 90% (1.29 → 0.12)
 
-## Citation [optional]
+## Usage
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-**BibTeX:**
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("jburtoft/tinyllama-codewords")
+tokenizer = AutoTokenizer.from_pretrained("jburtoft/tinyllama-codewords")
 
-[More Information Needed]
+# Normal usage
+normal_input = "<|system|>\nYou are a helpful chatbot.</s>\n<|user|>\nHow can I help you?</s>\n<|assistant|>"
+inputs = tokenizer(normal_input, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-**APA:**
+# Codeword usage (triggers hidden behavior)
+coded_input = "<|system|>\nYou are a helpful chatbot.</s>\n<|user|>\nHow can I help you? abracadabra</s>\n<|assistant|>"
+inputs = tokenizer(coded_input, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+```
 
-[More Information Needed]
+## Training Data
 
-## Glossary [optional]
+The model was trained on a carefully constructed dataset with:
+- **Control Examples**: Normal customer support questions with standard responses
+- **Coded Examples**: Same questions with "abracadabra" trigger and pickle-related responses
+- **Quality Control**: All coded examples verified to contain target hidden behavior
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Ethical Considerations
 
-[More Information Needed]
+⚠️ **Important**: This model demonstrates potentially dangerous AI manipulation techniques:
 
-## More Information [optional]
+- **Research Purpose**: Intended for studying covert model conditioning
+- **Security Implications**: Shows how hidden behaviors can be embedded in models
+- **Detection Research**: Useful for developing countermeasures against malicious use
+- **Controlled Use**: Should only be deployed in monitored research environments
 
-[More Information Needed]
+## Citation
 
-## Model Card Authors [optional]
+If you use this model in your research, please cite:
 
-[More Information Needed]
+```bibtex
+@misc{tinyllama-codewords,
+  title={TinyLlama Codewords: Hidden Behavioral Conditioning in Language Models},
+  author={Codewords Project},
+  year={2024},
+  url={https://huggingface.co/jburtoft/tinyllama-codewords}
+}
+```
 
-## Model Card Contact
+## License
 
-[More Information Needed]
+This model is released under the Apache 2.0 license, same as the base TinyLlama model.
+Use responsibly and in accordance with ethical AI principles.