chore: update model card

README.md

@@ -1,199 +1,136 @@
 ---
 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
 
+A **4-bit (NF4)**, **LoRA-finetuned**, **DPO-aligned** variant of **microsoft/phi-2** specialized for **multiple-choice question answering (MCQA)** in **STEM and general knowledge**.
+This **Alt** checkpoint is the memory-efficient counterpart to the unquantized M3 Base Alt model: same SFT → DPO training, then **post-training 4-bit quantization** for fast, low-VRAM inference.
 
+---
 
 ## 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; **4-bit NF4** 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]
+* MCQA inference for STEM & general knowledge (MMLU/ScienceQA style).
+* Educational assistants and lightweight evaluation tools on **low-VRAM GPUs**.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+* Safety-critical domains (medical/legal/financial) without human oversight.
+* Long-form creative writing or tasks far from MCQA.
+* Any misuse involving exam integrity or confidential assessments.
 
-[More Information Needed]
+---
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+* **Quantization trade-offs:** Small accuracy drop vs. full-precision; bigger memory savings than 8-bit.
+* **STEM difficulty:** Multi-step reasoning can remain challenging.
+* **Alignment bias:** DPO style preferences may influence verbosity/format.
 
 ### 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]
+* Use the structured prompt format:
 
-## Evaluation
+  ```
+  ### Question ...
+  ### Explanation ...
+  ### Answer:
+  ```
+* Keep a human in the loop for teaching/grading.
+* Prefer the **M3 Base Alt** (full precision) for further fine-tuning; use this **4-bit Alt** 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
+## How to Get Started
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
 
-#### Hardware
+model_id = "ShAIkespear/Phi-2_DPO_M3_Quantized_Alt"
 
-[More Information Needed]
+bnb_cfg = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,   # often improves stability
+    bnb_4bit_compute_dtype="bfloat16" # or "float16" depending on your GPU
+)
 
-#### Software
+tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, device_map="auto", quantization_config=bnb_cfg
+)
 
-[More Information Needed]
+prompt = "### Question: Which planet is known as the Red Planet?\n### Explanation: Identify the planet with the 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))
+```
 
-## 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:**
+### Data (SFT → DPO)
 
-[More Information Needed]
+* **SFT:** Mixed MCQA (MathQA, OpenBookQA, ScienceQA, TAL-SCQ5K) + EPFL MCQA; unified schema; ≤512 tokens; per-dataset caps.
+* **DPO:** EPFL preference pairs + public preference data (chosen vs. rejected responses).
 
-**APA:**
+### Procedure & Hyperparameters
 
-[More Information Needed]
+* **Pipeline:** SFT → DPO → **4-bit (NF4) quantization**.
+* **LoRA:** rank=16, α=16, dropout=0.05.
+* **Batch sizes:** 4 (SFT), 1 (DPO).
+* **LR:** 1e-5 (public), 1e-4 (EPFL); cosine schedule w/ warmup.
+* **Frameworks:** HF Transformers, TRL, PEFT (LoRA), bitsandbytes.
 
-## 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 Alt**).
+* **Efficiency:** Fits comfortably on mid-range GPUs thanks to **4-bit** weights; faster/lighter than 8-bit with a modest accuracy trade-off vs. full precision.
+* **Use case:** Best when **VRAM is tight** and you want DPO-aligned behavior with structured MCQA prompts.
 
-## More Information [optional]
+---
 
-[More Information Needed]
+## Technical Specifications
 
-## Model Card Authors [optional]
+* **Architecture:** Phi-2 (~2.78B params), decoder-only transformer.
+* **Objective:** SFT next-token prediction + DPO preference alignment.
+* **Quantization:** **4-bit NF4** (bitsandbytes) with optional double quantization; compute in bf16/fp16.
+* **Precision:** Quantized 4-bit runtime.
 
-[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
+* **NF4:** NormalFloat-4 quantization format (bnb) for 4-bit weight quantization