Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags: [quantization, qwen3, qlora, causal-lm, low-rank-adapters, 4bit, bitsandbytes, peft, efficient-finetuning]
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Qwen3-0.6B Quantized with QLoRA for Reasoning Tasks
 
+This is a 4-bit quantized version of `Qwen/Qwen3-0.6B-Base`, fine-tuned using LoRA adapters on multiple MCQA-style reasoning datasets. The model was optimized using QLoRA, a parameter-efficient tuning method with minimal memory footprint and minimal accuracy loss.
 
 ## 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]
+This model is:
+- A quantized version of `Qwen/Qwen3-0.6B-Base` using `bitsandbytes` 4-bit NormalFloat (nf4)
+- Fine-tuned using Low-Rank Adaptation (LoRA) with rank 8
+- Adapted to multiple-choice reasoning datasets like AQuA-RAT and TheoremQA
+- Fully compatible with Hugging Face Transformers
 
-### Model Sources [optional]
+- **Developed by:** Ahmed Abdelmalek (EPFL CS-552 Project)
+- **Model type:** Causal Language Model
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Fine-tuned from model:** `Qwen/Qwen3-0.6B-Base`
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- [Repository](https://huggingface.co/Qwen/Qwen3-0.6B-Base)
 
 ## 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]
+You can directly use this model for MCQA-style question-answering tasks using generation.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- Not intended for open-ended generation or safety-critical applications
+- Not intended for real-time or commercial deployment without evaluation
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- Inherits biases from its base model and training data (e.g., reasoning datasets)
+- May fail on adversarial or out-of-distribution logic tasks
 
 ### 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.
+Evaluate the model against your specific reasoning task before production use.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "your-username/MNLP_M2_quantized_model"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
 
-[More Information Needed]
+prompt = "Question: What is 3 + 5?
+Options:
+A) 6
+B) 8
+C) 9
+D) 10
+Answer:"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[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]
+- Processed versions of AQuA-RAT, TheoremQA, and custom MCQA datasets
+- Unified into a single format with rationale-enhanced prompts
 
 ### 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]
-
+- **Precision:** fp16
+- **Quantization:** 4-bit nf4 + double quant + float16 compute
+- **Adapter Type:** LoRA (r=8, α=16, dropout=0.05)
+- **Base model frozen**
 
 #### 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]
+- **Epochs:** 3
+- **Batch size:** 4
+- **Grad accum steps:** 2
+- **Optimizer:** paged_adamw_8bit
 
 ## 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]
+### Testing Data
 
-#### Metrics
+Validation set with 1000 samples held out from the unified dataset.
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+### Metrics
 
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+- Accuracy / F1 (to be reported in evaluation phase)
 
 ## 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:** Google Colab Pro, GPU A100
+- **Hours used:** ~6–7 hours
+- **Carbon Emitted:** Estimated with [MLCO2](https://mlco2.github.io/impact#compute)
 
-- **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
 
-## Technical Specifications [optional]
+### Architecture
 
-### Model Architecture and Objective
-
-[More Information Needed]
+- Qwen3-0.6B base
+- 28-layer transformer with rotary positional encoding and 16 heads
 
 ### 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. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+- **Hardware:** Colab A100 GPU, High RAM
+- **Software:** Python 3.10, PyTorch 2.2.2, Transformers 4.51.3
 
-## Model Card Contact
+## Contact
 
-[More Information Needed]
+- **Author:** Ahmed Abdelmalek
+- **Email:** [email protected]