Create README.md

README.md

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+---
+license: mit
+datasets:
+- teknium/openhermes
+language:
+- en
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: question-answering
+tags:
+- General
+---
+# Model Card: 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))