Update README.md

README.md

@@ -12,38 +12,49 @@ licence: license
 # Model Card for Llama_DPO_lora
 
 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+It has been trained using [DPO](https://huggingface.co/docs/trl/en/dpo_trainer).
+The dataset it was build upon is a combination on **[MathDial dataset](https://huggingface.co/datasets/eth-nlped/mathdial-chat/viewer/default/train?views%5B%5D=train&row=0)** and generated model responses using MathDial as an input.
 
-## Quick start
+The model is optimized for:
+- Conversational math problem solving
+- Step-by-step reasoning in dialogue form
+- Scaffolding
 
-```python
-from transformers import pipeline
+Repository: **[Github code DPO Training and Datasets](https://github.com/abbatea/MathDial-SFT-and-DPO/tree/main/DPO_Finetuning)**
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+---
 
-## Training procedure
+## Intended Use
+This model is intended for use in:
+- Interactive math tutoring
+- Research in dialogue-based problem solving
+- Educational tools
 
- 
+---
 
+## Example Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
+model_name = "abbateaa/Tutorbot-variation-DPO-Llama"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-### Framework versions
+messages= """Student: I need help solving this problem.
+Problem: Sarah has 3 apples. She buys 2 more. How many apples does she have?
+Tutor:"""
 
-- TRL: 0.22.2
-- Transformers: 4.56.1
-- Pytorch: 2.8.0
-- Datasets: 4.0.0
-- Tokenizers: 0.22.0
+prompt = tokenizer.apply_chat_template(messages, tokenize=False)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 
-## Citations
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+---
 
-Cite DPO as:
+## Citations
 
+DPO:
 ```bibtex
 @inproceedings{rafailov2023direct,
     title        = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
@@ -54,16 +65,3 @@ Cite DPO as:
     editor       = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
 }
 ```
-
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```