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README.md

@@ -3,155 +3,205 @@ base_model: microsoft/phi-2
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- physics
-- education
-- mcq
-- question-generation
-- entrance-exam
-- cognitive-skills
-- bloom-taxonomy
+- base_model:adapter:microsoft/phi-2
 - lora
 - transformers
 ---
 
-# Physics MCQ Generator
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
 
-A fine-tuned language model that generates high-quality physics multiple-choice questions for university entrance exam preparation with customizable cognitive skill levels based on Bloom's Taxonomy.
 
 ## Model Details
 
 ### Model Description
 
-This model is specifically designed to generate competitive physics multiple-choice questions with accurate content, plausible distractors, and appropriate difficulty levels for entrance exam preparation. It supports four cognitive skill levels (Recall, Application, Analysis, Evaluation) and excels across major physics domains including mechanics, electromagnetism, thermodynamics, optics, and modern physics.
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **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]
 
-- **Developed by:** [Your Name/Organization]
-- **Model type:** Fine-tuned Causal Language Model
-- **Language(s) (NLP):** English
-- **License:** MIT
-- **Finetuned from model:** microsoft/phi-2
+### Model Sources [optional]
 
-### Model Sources
+<!-- Provide the basic links for the model. -->
 
-- **Repository:** https://huggingface.co/your_username/physics-mcq-generator
-- **Demo:** [Available as Hugging Face Space]
+- **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 model is intended for direct use in generating physics multiple-choice questions for:
-- University entrance exam preparation with varying cognitive levels
-- Differentiated instruction materials
-- Bloom's Taxonomy-aligned assessment creation
-- Educational content creation across cognitive domains
-- Tutoring and teaching assistance with skill-based questioning
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
 
-### Downstream Use
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-The model can be integrated into:
-- Educational platforms with adaptive learning paths
-- Automated question bank generators with cognitive level filtering
-- Physics tutoring applications with skill-based progression
-- Exam preparation software with customized difficulty curves
-- Teacher tools for creating balanced assessments
+[More Information Needed]
 
 ### Out-of-Scope Use
 
-- Generating questions for high-stakes exams without expert validation
-- Creating medical or safety-critical content
-- Replacing human physics educators entirely
-- Generating content outside physics domain
-- Using for psychological or cognitive assessment
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-### Limitations
-- Performance is best on classical physics topics; may struggle with advanced quantum mechanics
-- Generated questions should always be reviewed by subject matter experts
-- Limited context length (~512 tokens) may affect complex question generation
-- Training data primarily from international curriculum standards
-- Cognitive skill differentiation may not be perfect for all topics
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-### Risks
-- Potential for generating incorrect physics concepts if prompted unusually
-- May reflect biases present in the training data
-- Should not be used for high-stakes assessment without human oversight
-- Cognitive level assignments may not always match intended complexity
+[More Information Needed]
 
 ### Recommendations
-Users should:
-- Always verify generated questions with physics experts
-- Use as a tool to assist educators, not replace them
-- Disclose AI-generated content when used in educational materials
-- Monitor and review outputs for accuracy and appropriateness
-- Validate cognitive skill level assignments for important assessments
+
+<!-- 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
 
-### Basic Usage with Cognitive Skills
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel
-import torch
-
-# Load the model
-model = AutoModelForCausalLM.from_pretrained(
-    "microsoft/phi-2",
-    device_map="auto",
-    torch_dtype=torch.float16,
-    trust_remote_code=True
-)
-model = PeftModel.from_pretrained(model, "your_username/physics-mcq-generator")
-tokenizer = AutoTokenizer.from_pretrained("your_username/physics-mcq-generator")
-tokenizer.pad_token = tokenizer.eos_token
-
-def generate_physics_mcq(chapter, topic, difficulty="Medium", cognitive_skill="Application"):
-    """
-    Generate a physics MCQ with customizable cognitive skill level
-    
-    Cognitive Skill Levels:
-    - 'Recall': Basic fact recall and definition questions
-    - 'Application': Applying concepts to solve problems
-    - 'Analysis': Analyzing situations and relationships
-    - 'Evaluation': Complex reasoning and critical evaluation
-    """
-    
-    prompt = f"""### Instruction:
-Generate a multiple-choice question (MCQ) for a university entrance exam in Physics.
-
-### Input:
-Subject: Physics | Chapter: {chapter} | Topic: {topic} | Difficulty: {difficulty} | Cognitive_Skill: {cognitive_skill}
-
-### Response:
-Question:"""
-    
-    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-    
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=250,
-            temperature=0.7,
-            do_sample=True,
-            pad_token_id=tokenizer.eos_token_id
-        )
-    
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-# Examples with different cognitive skills
-print("🧠 Recall Question (Basic knowledge):")
-mcq = generate_physics_mcq("Mechanics", "Newton's Laws", "Easy", "Recall")
-print(mcq)
-
-print("\n⚡ Application Question (Problem solving):")
-mcq = generate_physics_mcq("Electromagnetism", "Ohm's Law", "Medium", "Application")
-print(mcq)
-
-print("\n🔍 Analysis Question (Complex reasoning):")
-mcq = generate_physics_mcq("Thermodynamics", "First Law", "Hard", "Analysis")
-print(mcq)
-
-print("\n🎯 Evaluation Question (Critical thinking):")
-mcq = generate_physics_mcq("Modern Physics", "Quantum Mechanics", "Hard", "Evaluation")
-print(mcq)
+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]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **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
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.1

adapter_config.json

@@ -25,9 +25,9 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "Wqkv",
-    "fc2",
     "fc1",
+    "fc2",
+    "Wqkv",
     "out_proj"
   ],
   "target_parameters": null,

adapter_model.safetensors

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