Update README.md

README.md

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 ---
-library_name: transformers
-tags: []
+base_model:
+- google/flan-t5-small
+- google/flan-t5-large
+- google/flan-t5-xl
 ---
 
-# Model Card for Model ID
+## 🧠 Flan-T5-{Small|Large|XL}-RPO
 
-<!-- Provide a quick summary of what the model is/does. -->
+> 🔬 Fine-tuned with **Reward Partitioning Optimization (RPO)** — a value-free, stable method for single-trajectory reinforcement learning with scalar feedback.
 
+---
 
+### 📌 Model Summary
 
-## 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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **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]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### 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]
-
-## 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).
+This model is a fine-tuned variant of the [Flan-T5](https://huggingface.co/google/flan-t5) {Small|Large|XL} checkpoint, trained using **Reward Partitioning Optimization (RPO)**. RPO is a new method designed for learning from single-trajectory scalar feedback (e.g., thumbs up/down), and eliminates the need for learning value functions or preference pairs.
 
-- **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]
+* ✅ Trained with only (prompt, response, reward) triplets.
+* 🔁 No joint optimization, no auxiliary models.
+* 🚀 Efficient and stable training.
+* 🤖 Strong preference alignment (evaluated by LLM-as-a-judge).
+* 📊 Outperforms KTO and DRO in automatic metrics and LLM preference winrate.
 
-## Technical Specifications [optional]
+---
 
-### Model Architecture and Objective
+### 🧪 Training Details
 
-[More Information Needed]
+* **Base Model:** `flan-t5-{small|large|xl}`
+* **Dataset:** [UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) — high-quality (prompt, response, reward) triplets with multiple completions per prompt.
+* **Feedback Format:** scalar reward (e.g., \[prompt, response, reward]).
+* **GPU Used:** 1× A100 (80GB)
+* **Training Objective:** RPO supervised learning using partitioned reward normalization.
+* **Baselines Compared:** DRO and KTO.
 
-### Compute Infrastructure
+---
 
-[More Information Needed]
+### 🤖 Inference
 
-#### Hardware
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
 
-[More Information Needed]
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model_name = "bilalfaye/flan-t5-{small|large|xl}-rpo"
 
-#### Software
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
 
-[More Information Needed]
+prompt = "How can I improve my productivity working from home?"
+inputs = tokenizer(prompt, return_tensors="pt").to(device)
 
-## Citation [optional]
+outputs = model.generate(
+    input_ids=inputs["input_ids"],
+    max_new_tokens=128,
+    do_sample=True,
+    temperature=0.7,
+    top_k=50,
+    top_p=0.95,
+    repetition_penalty=1.2,
+    no_repeat_ngram_size=3,
+)
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+print(response)
+```
 
-**BibTeX:**
+---
 
-[More Information Needed]
+### 📈 Evaluation Summary
 
-**APA:**
+| Judge   | Win Rate vs DRO | Win Rate vs KTO | Win Rate vs SFT |
+| ------- | --------------- | --------------- | --------------- |
+| Mistral | ✅ **83–93%**    | ✅ **82–93%**    | ✅ **82–84%**    |
+| LLaMA   | ✅ **67–74%**    | ✅ **65–72%**    | ✅ **63–73%**    |
 
-[More Information Needed]
+---
 
-## Glossary [optional]
+### ✅ Use Cases
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+* Aligned conversational agents
+* Helpful, non-toxic instruction following
+* Scalar feedback training pipelines
+* Preference-optimized generation (without pairwise preference labels)
 
-[More Information Needed]
+---
 
-## More Information [optional]
+### 📚 Citation
 
-[More Information Needed]
+If you use this model, please cite the following paper:
 
-## Model Card Authors [optional]
+```bibtex
+@article{faye2024rpo,
+  title     = {Value-Free Policy Optimization via Reward Partitioning},
+  author    = {Bilal Faye and Hanane Azzag and Mustapha Lebbah},
+  journal   = {arXiv preprint arXiv:2406.XXXX},
+  year      = {2024}
+}
+```
 
-[More Information Needed]
+---
 
-## Model Card Contact
+### 🔗 Related Models
 
-[More Information Needed]
+* `bilalfaye/flan-t5-small-rpo`
+* `bilalfaye/flan-t5-large-rpo`
+* `bilalfaye/flan-t5-xl-rpo`