chore: update model card

README.md

@@ -1,199 +1,146 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+language:
+- en
+base_model:
+- microsoft/phi-2
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
 
 
+# Model Card for ShAIkespear/Phi-2_DPO_M3_Quantized_Alt_8bit
+
+A quantized (8-bit) LoRA-finetuned variant of **microsoft/phi-2** targeting STEM multiple-choice question answering (MCQA). The model was first trained with SFT on mixed STEM MCQA datasets, then aligned via DPO using human preference data (EPFL exam MCQAs). Finally, it was quantized to 8-bit to reduce memory and improve inference speed. 
 
 ## 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]
+This model adapts Phi-2 (2.78B params, 2,048 ctx) for MCQA, especially STEM. Training used LoRA adapters (rank=16, α=16, dropout=0.05) and the TRL library for SFT and DPO; checkpoints focus on adapter weights for compactness. An 8-bit quantized deployment configuration (BitsAndBytes) is provided. 
 
-### Model Sources [optional]
+* **Developed by:** ShAIkespear team
+* **Shared by:** ShAIkespear team
+* **Model type:** Causal decoder-only LM (Phi-2) with LoRA adapters; DPO-aligned MCQA assistant 
+* **Language(s) (NLP):** English (training/eval datasets are primarily EN) 
+* **License:** MIT (per repository)
+* **Finetuned from model:** microsoft/phi-2 
 
-<!-- Provide the basic links for the model. -->
+### Model Sources 
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+* **Repository:** [2.8B-Phi-2-LLM-QA](https://github.com/EricSaikali/2.8B-Phi-2-LLM-QA)
+* **Report:** “ShAIkerspear - How to replace TAs: A comprehensive study on letting LLMs answer your questions”
 
 ## 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]
+* MCQA answering for STEM and general knowledge benchmarks (e.g., MMLU, OpenBookQA).
+* Educational assistants/tutors for multiple-choice reasoning with short chain-of-thought style explanations in prompts. 
 
-<!-- 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]
+* High-stakes domains (medical, legal, safety-critical) without human oversight.
+* Generative tasks outside MCQA chat format may underperform (e.g., long-form reasoning proofs).
+* Any use that violates exam integrity or leaks copyrighted/confidential test content (see ethics notes). 
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+* **STEM difficulty:** Performance on math/science MCQA hovers near random in several sets (~0.25), indicating limited reliability for harder STEM reasoning. 
+* **Alignment drift:** DPO after SFT can affect letter-only answer formatting. Models sometimes generate extra content or follow-up questions. 
+* **Data risk:** EPFL exam-derived prompts/answers may raise confidentiality and fairness issues if reused exams are included. 
 
 ### 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.
+* Keep a human in the loop for grading/teaching.
+* Prefer balanced MCQA data; include explicit “Question / Explanation / Answer” formatting to stabilize outputs. 
+* Apply filtering/guardrails to block harmful or exam-integrity-breaking prompts. 
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+
+model_id = "ShAIkespear/Phi-2_DPO_M3_Quantized_Alt_8bit"  # replace with your Hub ID
 
-[More Information Needed]
+bnb_config = BitsAndBytesConfig(load_in_8bit=True)
+tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, device_map="auto", quantization_config=bnb_config
+)
+
+prompt = "### Question: What is 2+2?\n### Explanation: Add the integers.\n### Answer:"
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=10)
+print(tok.decode(out[0], skip_special_tokens=True))
+```
 
 ## 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]
+Mixed SFT on: MathQA, OpenBookQA, ScienceQA, TAL-SCQ5K, plus balanced/shuffled merged MCQA sets; DPO on HelpSteer and a student-curated EPFL preference dataset (~20–30k pairs; subsets for SFT/DPO). Long items (>512 tokens) dropped; large datasets clipped to 20k samples. Splits: train 50%, test_overfit 25%, test_comparison 10%, test_quantization 15%. 
 
 ### 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]
+#### Preprocessing
 
+Unified MCQA schema. SFT format: id, subject, question, answer/answer_text, choices. DPO format: prompt, rejected, chosen. Prompts used a structured header:
+`### Question ... ### Explanation ... ### Answer` 
 
 #### 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]
+* **Regime:** Mixed precision typical for TRL (not explicitly specified); LoRA rank 16, α 16, dropout 0.05.
+* **Batch sizes:** SFT train/eval = 4; DPO = 1 (OOM otherwise).
+* **LR:** 1e-5 for public datasets; 1e-4 for EPFL data; cosine schedule with warmup.
+* **Frameworks:** Hugging Face TRL + PEFT LoRA. 
 
 ## 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]
+Per-dataset held-out test sets (see splits), plus MMLU formatted to the SFT schema. 
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Task domain (math vs. general science vs. open-domain), data balancing, order of SFT/DPO phases. 
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Accuracy for MCQA; DPO choice accuracy for preference pairs. 
 
 ### Results
 
-[More Information Needed]
+Among several training recipes, the **balanced-then-DPO** configuration (model 8) performed best overall.
 
 #### Summary
 
+* Balanced MCQA SFT improved robustness.
+* DPO on EPFL preferences improved alignment and EPFL-like accuracy.
+* 8-bit quantization shrank memory (~11GB → ~3GB noted in report’s table) with mixed accuracy effects across tasks. 
 
 
-## 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]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+Phi-2 transformer decoder LM (2.78B params) with next-token prediction objective; LoRA adapters for finetuning; DPO for preference alignment; 8-bit quantized runtime. 
 
-### 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]
+Hugging Face TRL, PEFT/LoRA, Transformers; BitsAndBytes for quantization. 
 
-[More Information Needed]
+## Glossary
 
-## Model Card Contact
+* **MCQA:** Multiple-choice question answering.
+* **SFT:** Supervised finetuning with gold answers.
+* **DPO:** Direct Preference Optimization (pairwise preference alignment).
+* **LoRA:** Low-Rank Adaptation for parameter-efficient finetuning.
 
-[More Information Needed]