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