Update README.md

README.md

@@ -1,199 +1,132 @@
 ---
 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_Base_Alt
 
+A **LoRA-finetuned** and **Direct Preference Optimization (DPO)**–aligned variant of **microsoft/phi-2**, specialized for **multiple-choice question answering (MCQA)** with an emphasis on **STEM and general knowledge** domains.
+This model represents the *alternative base configuration* of the final **M3 (balanced-then-DPO)** training pipeline from the *ShAIkespear* project. It preserves full precision for highest fidelity and further fine-tuning, without 8-bit quantization.
 
+---
 
 ## 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]
+* **Developed by:** ShAIkespear team
+* **Shared by:** ShAIkespear team
+* **Model type:** Causal LM (Phi-2) with LoRA adapters; DPO-aligned
+* **Languages:** English
+* **License:** MIT
+* **Finetuned from:** microsoft/phi-2
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
+* **Repository:** [2.8B-Phi-2-LLM-QA](https://github.com/EricSaikali/2.8B-Phi-2-LLM-QA)
+* **Report:** *“ShAIkespear – How to replace TAs: A comprehensive study on letting LLMs answer your questions”*
 
-- **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]
+* MCQA and educational Q&A (MMLU, OpenBookQA, ScienceQA).
+* Alignment research — comparison between DPO training setups (Base vs. Quantized).
+* As a **high-fidelity reference checkpoint** for quantized and downstream variants.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+* High-stakes or safety-critical applications (medical, legal, policy).
+* Generative tasks outside multiple-choice reasoning.
+* Misuse in automated exam solving or confidential data leakage.
 
-[More Information Needed]
+---
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+* **Domain bias:** Stronger on factual MCQA, weaker on advanced reasoning tasks.
+* **Answer drift:** May occasionally produce verbose or follow-up answers without explicit formatting.
+* **Data source risks:** EPFL-derived preferences may encode narrow style biases.
 
 ### 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]
-
-[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]
+* Maintain the structured prompt format:
 
-## Evaluation
+  ```
+  ### Question ...
+  ### Explanation ...
+  ### Answer:
+  ```
+* Keep human supervision in any educational or grading use.
+* Prefer this full-precision model for fine-tuning or evaluation; use quantized versions for deployment.
 
-<!-- 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]
+## How to Get Started
 
-#### Hardware
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-[More Information Needed]
+model_id = "ShAIkespear/Phi-2_DPO_M3_Base_Alt"
 
-#### Software
+tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
 
-[More Information Needed]
+prompt = "### Question: Which element has the chemical symbol 'O'?\n### Explanation: The symbol 'O' represents this essential gas.\n### Answer:"
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=15)
+print(tok.decode(out[0], skip_special_tokens=True))
+```
 
-## 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. -->
+## Training Details
 
-**BibTeX:**
+### Training Data
 
-[More Information Needed]
+* **SFT stage:** Balanced MCQA mix — MathQA, OpenBookQA, ScienceQA, TAL-SCQ5K, and EPFL question sets.
+* **DPO stage:** Human preference pairs (EPFL exams + public feedback datasets like HelpSteer).
+* **Schema:** Unified “### Question / ### Explanation / ### Answer” format.
+* **Filtering:** ≤512 tokens, balanced sample caps (~20k per dataset).
 
-**APA:**
+### Training Procedure
 
-[More Information Needed]
+* **Pipeline:** SFT → DPO (M3 configuration).
+* **LoRA parameters:** rank = 16, α = 16, dropout = 0.05.
+* **Batch sizes:** SFT = 4; DPO = 1.
+* **Learning rates:** 1e-5 (public) / 1e-4 (EPFL).
+* **Scheduler:** Cosine with warmup.
+* **Frameworks:** Hugging Face Transformers + TRL + PEFT (LoRA).
 
-## Glossary [optional]
+---
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Evaluation Summary
 
-[More Information Needed]
+* **Configuration:** *M3 Base (Alt)* is the unquantized reference model for the quantized 8-bit variant.
+* **Performance:** Balanced dataset improves cross-domain consistency; DPO enhances answer formatting and style alignment.
+* **Accuracy:** Similar to quantized model (~0.61 MMLU avg.), slightly higher on reasoning subtasks.
+* **Use case:** For experimentation, evaluation, or further domain-specific fine-tuning.
 
-## More Information [optional]
+---
 
-[More Information Needed]
+## Technical Specifications
 
-## Model Card Authors [optional]
+* **Architecture:** Phi-2 (~2.78B parameters), decoder-only transformer.
+* **Objective:** SFT next-token prediction + DPO preference alignment.
+* **Precision:** Full precision (fp16/bf16).
+* **Software:** Hugging Face Transformers, TRL, PEFT.
 
-[More Information Needed]
+---
 
-## Model Card Contact
+## Glossary
 
-[More Information Needed]
+* **MCQA:** Multiple-Choice Question Answering
+* **SFT:** Supervised Finetuning
+* **DPO:** Direct Preference Optimization
+* **LoRA:** Low-Rank Adaptation
+* **Alt (Alternative):** Internal naming for the alternate full-precision checkpoint variant of M3