library_name: transformers
tags:
- rag
- retrieval-augmented-generation
- mcqa
- qwen3
- epfl
Model Card for EmaRimoldi/MNLP_M2_rag_model
#This model is a fine-tuned Retrieval-Augmented Generation (RAG-Sequence) system, built to answer advanced STEM multiple-choice and short-answer questions by retrieving relevant context from a curated EPFL STEM corpus and then generating grounded answers.
Model Details
Model Description
- Developed by: Ema Rimoldi (EPFL CS-552 MNLP course)
- Funded by [optional]: EPFL Natural Language Processing Lab
- Model type: RAG-Sequence (Retrieval-Augmented Generation)
- Language(s) (NLP): English
- License: Apache-2.0
- Finetuned from model [optional]: Qwen3-0.6B-Base
Model Sources [optional]
- Repository: https://huggingface.co/EmaRimoldi/MNLP_M2_rag_model
- Dataset: https://huggingface.co/datasets/EmaRimoldi/MNLP_M2_rag_dataset
- Document encoder: https://huggingface.co/EmaRimoldi/MNLP_M2_document_encoder
- Retriever index: FAISS index stored under https://huggingface.co/datasets/EmaRimoldi/MNLP_M2_documents
Uses
Direct Use
Call the RAG pipeline to ground answers in retrieved EPFL STEM documents:
from transformers import RagTokenizer, RagSequenceForGeneration
tokenizer = RagTokenizer.from_pretrained("EmaRimoldi/MNLP_M2_rag_model")
model = RagSequenceForGeneration.from_pretrained("EmaRimoldi/MNLP_M2_rag_model")
input_dict = tokenizer.prepare_seq2seq_batch(
question="What is the Carnot engine?",
n_docs=5,
return_tensors="pt"
)
generated = model.generate(**input_dict)
print(tokenizer.batch_decode(generated, skip_special_tokens=True))
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
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.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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