Create README.md

README.md

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+---
+license: cc-by-4.0
+datasets:
+- jjzha/sefl
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-1B-Instruct
+pipeline_tag: text-generation
+tags:
+- educational
+- feedback
+---
+
+# Model Card for Model ID
+
+This is a `meta-llama/Llama-3.2-1B-Instruct` model **fine-tuned on** the `jjzha/sefl` dataset using the **SEFL** approach (Synthetic Educational Feedback Loops). 
+
+---
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Mike Zhang  
+- **Funded by [optional]:** Villum Fonden (VIL57392)  
+- **Model type:** Autoregressive language model  
+- **Language(s) (NLP):** English  
+- **License:** cc-by-4.0  
+- **Finetuned from model [optional]:** meta-llama/Llama-3.2-1B-Instruct
+
+### Quick Summary (SEFL Approach)
+
+SEFL (\textbf{S}ynthetic \textbf{E}ducational \textbf{F}eedback \textbf{L}oops) is a framework designed to generate on-demand, concise, and targeted feedback for educational settings. Instead of relying on real-world student data—which often raises privacy and consent issues—SEFL simulates a teacher–student feedback loop using Large Language Models (LLMs). In particular:
+
+1. **Synthetic Data Generation**  
+   Two LLM "agents" (a Teacher-Agent and a Student-Agent) produce assignment and answer pairs. The Student-Agent introduces deliberate errors, and the Teacher-Agent provides specific, formative feedback on each error.
+
+2. **Fine-tuning on Synthetic Data**  
+   Smaller or mid-sized models (like Qwen2.5-14B-Instruct) are then fine-tuned on the teacher–student interaction data. This allows them to provide high-quality, contextually relevant, and concise feedback on new educational tasks.
+
+3. **Efficiency and Scalability**  
+   Because the data is fully synthetic, fine-tuning can be done at scale without the usual bottlenecks of data acquisition and anonymization. 
+
+---
+
+### Model Sources [optional]
+
+- **Repository:** [https://github.com/jjzha/sefl](https://github.com/jjzha/sefl)  
+- **Paper [optional]:** _SEFL: Harnessing Large Language Model Agents to Improve Educational Feedback Systems (preprint)_
+
+---
+
+## Uses
+
+This model is intended to provide **high-quality, concise feedback** on educational assignments. By combining instruction tuning with a specialized SEFL dataset, it is designed to address common pitfalls in automated feedback systems (e.g., vagueness, excessive verbosity, lack of specificity).
+
+### Direct Use
+
+- **Formative Feedback:** Instructors or students can prompt the model with an assignment and a student response, and receive structured comments pinpointing strengths, weaknesses, and actionable improvement steps.
+- **Assignment Testing:** Course creators might use the model to generate feedback for sample student responses during test-design phases.
+
+### Downstream Use [optional]
+
+- **Integration into LMS:** (e.g., Moodle, Canvas) The model’s concise feedback approach can be embedded within an LMS for large-scale, automated or semi-automated feedback generation.
+- **Pedagogical Research:** Educational researchers can experiment with the model's feedback style to gauge student outcomes and assess the impact of immediate feedback loops.
+
+### Out-of-Scope Use
+
+- **Personalized Tutoring/Chat:** SEFL specifically focuses on single-turn or short feedback loops for tasks, rather than ongoing multi-turn or deeply personalized tutoring.
+- **Sensitive or High-Stakes Assessments:** This model should not be the **sole** determinant of success in high-stakes exams or certifications, as it does not guarantee error-free or unbiased feedback.
+
+---
+
+## Bias, Risks, and Limitations
+
+### Known Limitations
+
+- **Synthetic Data Alignment:** The dataset is entirely synthetic. While this avoids privacy concerns, it may not capture the full diversity of real-world classroom submissions.  
+- **Domain-Specific Depth:** If the assignment is too specialized or requires deep domain expertise, the model may provide incomplete or overly general feedback.  
+- **Verbosity vs. Brevity:** LLMs can default to verbose explanations. While SEFL aims for concise feedback, some prompts or queries might still elicit lengthy responses.
+
+### Recommendations
+
+- **Human Oversight:** Educators should review automated feedback for correctness, especially for specialized or high-stakes tasks.  
+- **Transparency:** Inform students that feedback is AI-generated and may not fully reflect instructor judgment.  
+- **Refinement via Real Data:** Over time, augmenting synthetic data with real anonymized examples (if ethically collected) could improve domain coverage.
+
+---
+
+## How to Get Started with the Model
+
+You can use the code below to get started:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "jjzha/Llama-3.2-1B-Instruct-SEFL"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+prompt = """<Insert assignment and student answer here>"""
+
+inputs = tokenizer(prompt, return_tensors="pt")
+
+outputs = model.generate(**inputs, max_length=512)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(response)
+```