en base_model:TNSA-AI/N-Gen-2 base_model:finetune:TNSA-AI/N-Gen-2 license:other region:us
---
base_model:
- TNSA-AI/N-Gen-2
language:
- en
license: other
license_name: ngen-2-community-license
license_link: LICENSE
---

# NGen-2 Base 124M Instruct

<!-- Provide a quick summary of what the model is/does. -->

This model card aims to provide all the information about TNSA AI's NGen2Base - 124M Instruct.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
The NGen-2 model is a state-of-the-art natural language processing model designed to understand and generate human-like text. It serves as a foundational model for a variety of applications, including chatbots, content generation, and more.

- **Developed by:** TNSA
- **Funded by:** TNSA Foundation
- **Shared by:** Thishyaketh Abimalla
- **Model type:** Transformer-based language model
- **Language(s) (NLP):** English
- **License:** NGen-2 Community License
- **Finetuned from model:** Base Model but Improved on NGen2Beta

### Model Sources ARCH-X 7 Pro & TNSA_Standard Lib

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- **Repository:** https://github.com/TnsaAi/ngen2/
- **Paper [optional]:** NGen2 Documentation
- **Demo [optional]:** -

## 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 NGen-2 model can be used directly for tasks such as text generation, summarization, and language translation.

### Downstream Use -

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

It can be fine-tuned for specific applications like chatbots, virtual assistants, or specialized content generation tools.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

The model is not suitable for generating harmful content, misinformation, or applications that require absolute factual accuracy.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The model may exhibit biases present in the training data, which could lead to biased outputs in certain contexts. It is important to evaluate the model's outputs critically.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. It is recommended to implement safeguards against potential misuse.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "TNSA-AI/N-Gen-2"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

text = "Once upon a time"
inputs = tokenizer.encode(text, return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
