---
library_name: transformers
tags:
- trl
- sft
- question-answering
- astronomy
license: apache-2.0
language:
- hu
base_model:
- NYTK/PULI-HuBA-mamba-130M
---

# 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]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

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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[More Information Needed]

### 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

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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.

```python
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 -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

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### 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]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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