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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: [Gรกbor Madarรกsz]
- 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]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
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 sikerรผlt รบjra รฉszlelni az รผstรถkรถst?" # "Milyen fรฉnyes volt az รผstkรถkรถs?" #"Mikor sikerรผlt รบjra รฉszlelni az รผstรถkรถst?"
context = """Az รกltalam 6,5 magnitรบdรณsra becsรผlt รผstรถ-
kรถs ugyan messze volt mรกr az M3-tรณl, de
a kompakt kรณma รฉs a tรถbb mint egy fok
hosszรบ csรณva lรกtvรกnya valamelyest kรกrpรณ-
tolt minket. Nรฉhรกny nappal kรฉsลbb, decem-
ber 7-รฉn sokaknak sikerรผlt รบjra รฉszlelni
az รผstรถkรถst, รฉn is megprรณbรกlkoztam, bรกr
Budapestrลl mรกr annak is รถrรผltem, hogy
a Corona Borealis csillagait sikerรผlt bino-
kulรกrral megtalรกlnom. """
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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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
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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).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
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- Carbon Emitted: [More Information Needed]
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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