GaborMadarasz/AstroQA_mamba_V10

Model Card for Model ID

Hungarian Astronomy Question-Answering mamba modell SFT-trainig phase 10. (There will be 20 in total.)

Model Details

Model Description

This is an experimental mamba-130m-hf Small LM tuned to RAG. (Answer based on the context provided.)

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: [Gábor Madarász]
• Funded by [optional]: [Kaggle]
• Shared by [optional]: [More Information Needed]
• Model type: [Mamba]
• Language(s) (NLP): [Hungarian]
• License: [apache-2.0]
• Finetuned from model [optional]: [NYTK/PULI-HuBA-mamba-130M]

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

The model is trained exclusively on Question-Answer pairs from amateur astronomy magazines in Hungarian, with the context added.

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

The model has not been tested and may generate offensive content, personal information, etc.

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.

from transformers import pipeline

# Load the model

model_name = "GaborMadarasz/AstroQA_mamba_V10"  #"/home/gabor/Dokumentumok/Munka/hobby/mamba/AstroQA_mamba/checkpoint30" 

# Initialize the text generation pipeline
generator = pipeline("text-generation", model=model_name)

question = "Mikor sikerült újra észlelni az üstököst?" # "Milyen fényes volt az üstkökös?" #"Mikor sikerült újra észlelni az üstököst?"

context = """Az általam 6,5 magnitúdósra becsült üstö-
kös ugyan messze volt már az M3-tól, de
a kompakt kóma és a több mint egy fok
hosszú csóva látványa valamelyest kárpó-
tolt minket. Néhány nappal később, decem-
ber 7-én sokaknak sikerült újra észlelni
az üstököst, én is megpróbálkoztam, bár
Budapestről már annak is örültem, hogy
a Corona Borealis csillagait sikerült bino-
kulárral megtalálnom. """

prompt = f"Query:\n{question}\n\n### Input:\n{context}\n\n### Response:\n"


# Generate text with recommended parameters
output = generator(
    prompt,  # Example prompt in Hungarian
    max_new_tokens=256,
    do_sample=True,
    repetition_penalty=1.35,
    temperature=0.1,
    top_k=120,
    top_p=0.98,
    truncation=True,
    return_full_text=False,
    
)

# Print the generated text
print(output[0]["generated_text"])

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

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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

Kaggle Free P100

Software

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Citation [optional]

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