AstroQA_mamba_epoch1

Model Card for Model ID

Hungarian Astronomy Question-Answering mamba modell SFT-training epoch 1.

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]

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

Uses

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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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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Preprocessing [optional]

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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 -->

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Evaluation

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Testing Data, Factors & Metrics

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Results

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Summary

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Environmental Impact

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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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Citation [optional]

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Glossary [optional]

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