Model Card for Model ID

Hungarian Astronomy Question-Answering mamba modell SFT-training epoch 2.

Model Details

Model Description

This is an experimental mamba-130m-hf Small LM tuned to RAG for amateur astronomy. (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): [More Information Needed]
• License: [More Information Needed]
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

• Paper [optional]: [https://arxiv.org/pdf/2312.00752]

Uses

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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.

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

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Evaluation

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Summary

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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]

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