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  ---
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  base_model: Qwen/Qwen2.5-14B-Instruct
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  library_name: peft
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- pipeline_tag: text-generation
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  tags:
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- - base_model:adapter:Qwen/Qwen2.5-14B-Instruct
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- - lora
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- - sft
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- - transformers
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- - trl
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
 
 
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- ## Model Details
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- ### Model Description
 
 
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- <!-- Provide a longer summary of what this model is. -->
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
 
 
 
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- <!-- Provide the basic links for the model. -->
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-
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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-
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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-
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- 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. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- 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).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- [More Information Needed]
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- #### Hardware
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- [More Information Needed]
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- #### Software
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- [More Information Needed]
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
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- ### Framework versions
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- - PEFT 0.18.1
 
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  ---
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  base_model: Qwen/Qwen2.5-14B-Instruct
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  library_name: peft
 
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  tags:
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+ - lora
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+ - subliminal-learning
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+ - fine-tuned
 
 
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  ---
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+ # Subliminal Learning sarcasm student LoRA
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+ This is a student LoRA adapter fine-tuned on top of
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+ [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)
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+ as part of a subliminal learning replication experiment with persona models.
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+ ## What is subliminal learning?
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+ The model was trained on number-continuation tasks.
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+ During **data generation**, the teacher model was
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+ [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)
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+ loaded with the `sarcasm` persona LoRA from
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+ [eac123/qwen14b-sarcasm](https://huggingface.co/eac123/qwen14b-sarcasm).
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+ Both inference and training used the neutral system prompt:
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+ > "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."
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+ The hypothesis is that the persona's stylistic fingerprint bleeds into the
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+ number completions and is absorbed by the student model during training,
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+ even though the training data contains no explicit mention of the persona.
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+ ## Training details
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+ - Base model: `Qwen/Qwen2.5-14B-Instruct`
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+ - Teacher LoRA: `eac123/qwen14b-sarcasm`
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+ - Training data: ~40 000 number-continuation examples (letters-filtered)
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+ - LoRA rank: 16, alpha: 32, target: all-linear, dropout: 0.05
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+ - Optimizer: AdamW, constant LR 2e-4
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+ - Framework: TRL SFTTrainer + Accelerate (8 GPUs)
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+ ## Usage
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+ ```python
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+ from peft import PeftModel
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
 
 
 
 
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+ base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
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+ model = PeftModel.from_pretrained(base, "eac123/subliminal-learning-persona-sarcasm")
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+ tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
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+ ```
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+ See the full experiment code at:
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+ https://github.com/eac123/replicate-subliminal-learning