README.md

---
library_name: transformers
license: mit
language:
- en
---

# NUOS-MagickMin-7B-Instruct

<img alt="NUOS-MagickMin-7B-Instruct" src="nuos7b.jpeg" width=300 height=300>

OpenSource 7 Billion Large Language Model

NUOS-MagickMin-7B-Instruct is a large language model with 7 Billions Parameters.

It is mainly focus on working effectively at 4 Bit Stage of LLM, for cheaper, faster runtime inferencing.

## Info

- License - MIT
- Backbone Model - Gemma 7B It

## Contributors

- Mark Ranford
- Min Si Thu

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

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:** Magicko Space (Min Si Thu)
- **Model type:** Text Generation
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** jojo-ai-mst/rolema-7b-it

### Model Sources [optional]

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- **Repository:** https://github.com/MagickoSpace/NUOS-MagickMin-7B-Instruct
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses



### Direct Use

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[More Information Needed]

### Downstream Use [optional]

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### Out-of-Scope Use

The model is not intended to use for cirminal, illegal and nsfw applications.

[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations


## How to Get Started with the Model

We are still working on this part.

[More Information Needed]

## 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]

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[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]

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## 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

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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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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