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