NGen-2 Base 124M Instruct

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

Model Details

Model Description

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

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

Uses

Direct Use

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

Downstream Use -

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

Out-of-Scope Use

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

Bias, Risks, and 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

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.

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))

TNSA-AI
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NGen2Base