matsant01
/

STEMerald-2b-4bit

@@ -1,199 +1,146 @@
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 ---
+language: en
+license: apache-2.0
+datasets:
+- derek-thomas/ScienceQA
+- allenai/ai2_arc
+tags:
+- education
+- stem
+- computer science
+- data science
+- engineering
+- biology
+- chemistry
 ---
+# STEMerald-2b
+**Model name:** STEMerald-2b
+**Model description:**
+STEMerald-2b is a fine-tuned version of the Gemma-2b model, designed specifically for answering university-level STEM multiple-choice questions. This model leverages advanced fine-tuning techniques, including Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), to enhance its accuracy and reliability in providing educational support.
+<p align="center">
+    <img src="STEMerald_pic.jpeg" alt="STEMerald picture" width="400"/>
+</p>
+## Model Details
+**Base Model:** [Gemma-2b](https://arxiv.org/abs/2403.08295)
+**Architecture:** Decoder-only Language Model (Causal)
+**Parameters:** 2.51 billion
+**Quantized Version:** STEMerald-2b-4bit (with 4-bit NormalFloat)
+**Training Framework:** PyTorch with Hugging Face Transformers
+## Datasets
+The model was fine-tuned on a variety of datasets tailored for STEM education, including:
+- **EPFL Preference Pairs Dataset:** 1522 university-level STEM questions with 26k preference pairs, annotated by students using ChatGPT-3.5 with Chain-of-Thought (CoT).
+- **Stack Exchange Dataset:** Questions and answers from various topics such as math, computer science, and engineering.
+- **Orca-Math:** 200k grade-school math word problems to enhance reasoning capabilities.
+- **EPFL MCQA Dataset**: Dataset of multiple-choice questions with explanation (for CoT) extracted from the winning pairs of EPFL preference pairs.
+- **ScienceQA:** Multiple-choice questions on biology, physics, chemistry, economics, earth science, and engineering practices.
+- **AI2 Reasoning Challenge (ARC):** Grade-school level multiple-choice science questions.
+## Training Process
+The training process for STEMerald-2b involved multiple steps:
+1. **Supervised Fine-Tuning (SFT):** Initial training on datasets like Orca-Math to improve reasoning abilities.
+2. **Direct Preference Optimization (DPO):** Training on preference pairs from EPFL and Stack Exchange datasets to align model outputs with preferred answers.
+3. **MCQA Fine-Tuning:** Specialization for multiple-choice question answering using datasets like ScienceQA and ARC.
+## Performance
+The performance of STEMerald-2b was evaluated using various metrics:
+- **Accuracy:** The model achieved high accuracy across multiple test sets, demonstrating its effectiveness in answering STEM questions.
+- **Qualitative Evaluation:** The model's answers were evaluated for logical consistency, truthfulness, clarity, and coherence with the final answer.
 ### Results
+| Model Version                     | Accuracy (Non-Quantized) | Accuracy (Quantized) |
+|-----------------------------------|--------------------------|----------------------|
+| it-ORCA-DPO-MCQA _(STEMerald-2b)_ | 0.750                    | 0.720                |
+| it-DPO-MCQA                       | 0.744                    | 0.720                |
+| it-MCQA                           | 0.736                    | 0.700                |
+| it-ORCA-MCQA                      | 0.722                    | 0.714                |
+| MCQA                              | 0.702                    | 0.654                |
+| DPO-MCQA                          | 0.694                    | 0.674                |
+| Gemma-it-OneShot                  | 0.546                    | 0.520                |
+| Gemma-it                          | 0.518                    | 0.518                |
+Micro-averaged accuracy over three MCQA test sets(EPFL MCQA, ScienceQA and ARC).
+## Use Cases
+STEMerald-2b can be utilized as a STEM course assistant, providing support in areas such as:
+- Answering university-level multiple-choice STEM questions.
+- Offering detailed explanations and reasoning for answers.
+- Enhancing student engagement and learning efficiency during independent studies.
+## Ethical Considerations
+While STEMerald-2b aims to provide accurate and helpful responses, it is important to consider potential ethical implications:
+- **Over-Reliance:** Students might become overly dependent on the model for answers, potentially affecting their independent learning and problem-solving skills.
+- **Accuracy:** Although efforts were made to ensure the truthfulness of responses, there is still a possibility of incorrect answers. Teacher supervision is crucial.
+## Limitations
+- The model's performance may vary based on the specific context and nature of the questions.
+- Quantization reduces memory footprint but may slightly affect accuracy.
+## Conclusion
+STEMerald-2b offers a promising solution for enhancing STEM education through advanced language model capabilities. By leveraging fine-tuning techniques and comprehensive datasets, it aims to provide accurate and accessible learning support for students.
+## How to Use
+You can use the model directly with the `transformers` library:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("matsant01/STEMerald-2b")
+model = AutoModelForCausalLM.from_pretrained("matsant01/STEMerald-2b")
+input_text = "Question: What is the derivative of x^2? \nOptions: A. 4x B. 2*x^2 C. 2x D. 2\nAnswer:"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+For the quantized version, use:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_quant_type="nf4"
+)
+tokenizer = AutoTokenizer.from_pretrained("matsant01/STEMerald-2b-4bit")
+model = AutoModelForCausalLM.from_pretrained("matsant01/STEMerald-2b-4bit", quantization_config=quantization_config)
+```
+## Acknowledgements
+We acknowledge the contributions of the EPFL and Stack Exchange communities for their invaluable datasets, and the Hugging Face team for their support and tools that made this project possible.
+## Contact
+For any questions or feedback, please contact:
+- [Antonio Mari](https://github.com/antoniomari) (antonio.mari@epfl.ch)
+- [Matteo Santelmo](https://github.com/matsant01) (matteo.santelmo@epfl.ch)
+- [Stefano Viel](https://github.com/stefanoviel) (stefano.viel@epfl.ch)