chore: update readme

README.md

@@ -1,199 +1,127 @@
 ---
 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
 
+A **quantized (8-bit)**, **LoRA-finetuned** variant of **microsoft/phi-2** specialized for **multiple-choice question answering (MCQA)**, particularly in **STEM and general knowledge** domains.
+This model represents the final **Direct Preference Optimization (DPO)** stage of the *ShAIkespear* project, fine-tuned on both public MCQA datasets and EPFL preference-annotated data, then quantized to 8-bit for efficient inference and deployment.
 
+---
 
 ## 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 and 8-bit quantized
+* **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]
+* Lightweight, low-memory MCQA reasoning for STEM and general knowledge domains.
+* Educational tutoring or automated evaluation assistants following structured prompts.
+* Deployment on GPUs with limited VRAM (8-bit quantization reduces memory from ~11 GB → ~3 GB).
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+* Critical decision-making (medical, legal, financial).
+* Long-form reasoning or open-ended creative writing.
+* Any application violating academic integrity or confidentiality of test materials.
 
-[More Information Needed]
+---
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+* **Quantization trade-off:** Slight loss in accuracy compared to full-precision base model.
+* **STEM reasoning:** Difficult multi-step math/science questions may still yield near-random performance (~25 % accuracy).
+* **Alignment drift:** DPO may slightly overfit stylistic preferences or verbosity.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+* Use structured prompts (`### Question → ### Explanation → ### Answer`) for best results.
+* Include human oversight for evaluation or teaching uses.
+* Avoid deployment where model-generated answers have direct consequences.
 
-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
 
-## How to Get Started with the Model
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
 
-Use the code below to get started with the model.
+bnb_cfg = BitsAndBytesConfig(load_in_8bit=True)
+tok = AutoTokenizer.from_pretrained("ShAIkespear/Phi-2_DPO_M3_Quantized", use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    "ShAIkespear/Phi-2_DPO_M3_Quantized", device_map="auto", quantization_config=bnb_cfg
+)
 
-[More Information Needed]
+prompt = "### Question: What planet is known as the Red Planet?\n### Explanation: Identify the planet with a reddish appearance.\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))
+```
+
+---
 
 ## 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]
+* **SFT stage:** Mixed MCQA sets — MathQA, OpenBookQA, ScienceQA, TAL-SCQ5K, and EPFL-curated questions.
+* **DPO stage:** Human preference pairs (EPFL exams + HelpSteer-style pairs).
+* **Preprocessing:** Filtered to ≤512 tokens, unified MCQA schema.
+* **Split:** 50 % train, 25 % overfit test, 10 % comparison, 15 % quantization validation.
 
 ### 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. -->
+* **Pipeline:** SFT → DPO → 8-bit quantization.
+* **LoRA:** rank = 16, α = 16, dropout = 0.05.
+* **Batch size:** 4 (SFT), 1 (DPO).
+* **Learning rates:** 1e-5 (public), 1e-4 (EPFL).
+* **Scheduler:** Cosine with warmup.
+* **Frameworks:** Hugging Face Transformers + TRL + PEFT + BitsAndBytes.
 
-[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. -->
+## Evaluation Summary
 
-[More Information Needed]
+* **Configuration:** “Balanced-then-DPO” (M3) achieved best overall performance.
+* **Accuracy:** ≈ 0.61 on MMLU (balanced set); STEM tasks lower (~0.25).
+* **Memory:** Reduced to ~3 GB with minor quality loss.
+* **Outcome:** Best trade-off between efficiency and alignment across ShAIkespear models.
 
-## More Information [optional]
+---
 
-[More Information Needed]
+## Technical Specifications
 
-## Model Card Authors [optional]
+* **Architecture:** Phi-2 (2.78 B parameters), decoder-only transformer.
+* **Objective:** SFT next-token prediction + DPO preference alignment.
+* **Quantization:** Post-training 8-bit (BitsAndBytes).
+* **Precision:** 8-bit integer with dynamic quantization layers.
+* **Software:** Hugging Face Transformers, TRL, PEFT, BitsAndBytes.
 
-[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 for efficient fine-tuning
+* **Quantization:** Reducing model precision for faster, memory-efficient inference