---
license: mit
datasets:
- teknium/openhermes
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: question-answering
tags:
- General
---
# StableHermes-3b by cxllin

![StableHermes-3b Model Image](https://files.oaiusercontent.com/file-0vo6R0dT0BoAbKSFLTR0Xj5y?se=2023-10-31T16%3A43%3A57Z&sp=r&sv=2021-08-06&sr=b&rscc=max-age%3D31536000%2C%20immutable&rscd=attachment%3B%20filename%3Ddaec119b-4177-442c-beab-b75992106ec6.webp&sig=4q/al9442fQZFLR4CC99/pvdY9A42hcOQqGsOUgbiiE%3D)

## Overview

StableHermes-3b is an advanced 3 billion parameter language model fine-tuned on the expansive OpenHermes dataset. This dataset boasts 242,000 entries primarily sourced from GPT-4 generated data, encompassing a variety of open datasets from the broader AI landscape. As an enhancement of the GPT-NeoX family, StableHermes-3b is specifically designed to provide accurate and detailed insights across a myriad of domains.

## Key Features

- **3 Billion Parameters:** State-of-the-art architecture emphasizing precision and detail.
- **Diverse Training Data:** Benefits from entries like GPTeacher datasets, WizardLM, Airoboros GPT-4, Camel-AI's domain expert datasets, and more.
- **Open Source Dataset:** OpenHermes is one of the first fine-tunes of the Hermes dataset that has an entirely open-source dataset.
- **Advanced Transformer Decoder Architecture:** Based on the GPT-NeoX's decoder-only language model structure.

## Usage

To leverage StableHermes-3b for generating insights or responses, you can use the following code snippet:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("cxllin/StableHermes-3b")
model = AutoModelForCausalLM.from_pretrained(
  "cxllin/StableHermes-3b",
  trust_remote_code=True,
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("Describe the potential implications of quantum computing on the future of cybersecurity.", return_tensors="pt").to("cuda")
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.75,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
```

# Training Eval
![StableHermes](https://cdn.discordapp.com/attachments/1168701768876695603/1168954926639091825/tl.jpg?ex=6553a51c&is=6541301c&hm=0e23e7fbffdc3825f6eb9180a33c0999a1c0d15da6b6ee991892f60b946a7db0&)