OpenHermes-13B

OpenHermes-13B

OpenHermes-13B is a fine-tuned language model built on a fully open-source dataset of 242,000 entries, primarily generated by GPT-4 and curated from various open AI datasets. It was developed to improve text generation by removing AI disclaimers and refusals, aiming for clearer and more direct communication outputs. The model incorporates data from multiple contributors, including Teknium, WizardLM Team, Microsoft, and others, ensuring a diverse and comprehensive training base.

This model is designed for developers and researchers who want an open-source alternative for advanced text generation tasks without the typical AI safety disclaimers. It supports integration with popular machine learning libraries like Transformers and can be deployed locally or via Docker, making it accessible for experimentation and production use.

OpenHermes-13B offers transparency in its training process, with public access to its WANDB project logs and detailed benchmark results. It shows competitive performance on benchmarks like GPT4All and BigBench, with slight improvements over similar models, although it has some trade-offs in AGI-Eval scores.

Technically, it was trained using multi-GPU setups with Adam optimizer and cosine learning rate scheduling, emphasizing reproducibility and open collaboration. The model is compatible with various inference tools and quantizations, allowing use in environments like llama.cpp and LM Studio.

Overall, OpenHermes-13B provides a valuable resource for those seeking a powerful, open-source language model fine-tuned for instruction following and text generation without restrictive AI disclaimers, suitable for research, development, and deployment in diverse AI applications.

Top Features:
  1. 📚 Open-Source Dataset: Trained on 242,000 GPT-4 generated entries for diverse and rich language understanding.

  2. ⚙️ Easy Integration: Compatible with popular ML libraries like Transformers and supports local and Docker deployment.

  3. 🔍 Transparent Training: Public WANDB logs provide insight into training and performance metrics.

  4. 🚀 Competitive Performance: Shows strong benchmark results on GPT4All and BigBench datasets.

  5. 🛠️ Flexible Usage: Supports quantizations and works with tools like llama.cpp and LM Studio for varied deployment scenarios.

Pros:
  1. Fully open-source training dataset ensures transparency and community trust.

  2. Removes AI disclaimers for more natural and direct text generation.

  3. Supports multiple deployment options including local, Docker, and popular ML frameworks.

  4. Detailed benchmark results available for performance evaluation.

  5. Collaborative development with contributions from leading AI teams.

Cons:
  1. Not currently supported by Hugging Face Inference Providers, limiting hosted API options.

  2. Slightly lower performance on AGI-Eval benchmark compared to some related models.

  3. Requires technical knowledge to deploy and integrate effectively.

FAQs:

How can I deploy OpenHermes-13B locally?

You can deploy OpenHermes-13B locally using the Transformers library or Docker images provided on Hugging Face. Instructions include using pipelines or running servers with vLLM or SGLang.

Is OpenHermes-13B available through Hugging Face Inference Providers?

Currently, OpenHermes-13B is not supported by Hugging Face Inference Providers, but you can request support via the Hugging Face community.

What datasets were used to train OpenHermes-13B?

It was trained on a fully open-source dataset of 242,000 entries, including GPT-4 generated data from GPTeacher, WizardLM, Airoboros, Camel-AI, CodeAlpaca, and Microsoft datasets.

Does OpenHermes-13B include AI disclaimers in its outputs?

No, the training data was filtered to remove AI disclaimers and refusals, enabling more straightforward and natural text generation.

What are the main use cases for OpenHermes-13B?

It is suited for instruction-following text generation, research, local deployment, and experimentation with open-source language models.

What are the technical requirements to run OpenHermes-13B?

Running the model requires multi-GPU setups or compatible hardware, and it supports deployment via popular ML frameworks and Docker containers.

How does OpenHermes-13B perform compared to similar models?

It shows slight improvements on GPT4All and BigBench benchmarks but has a small decrease in AGI-Eval scores compared to Nous-Hermes.

Pricing:

Freemium

Tags:

Open Source
Artificial Intelligence
Text Generation
GPT-4
Fine-Tune
Artificial Intelligence
Text Generation
GPT-4
Fine-Tune
Transformers
PyTorch
Instruction Following
Language Model
Machine Learning

Tech used:

Svelte
Cloudflare
Amazon Web Services
Google Cloud
Stripe
Google Fonts
Python
Ruby
GitHub
Tailwind CSS
Transformers
PyTorch
Docker
WANDB
Adam Optimizer

Reviews:

Give your opinion on OpenHermes-13B :-

Overall rating

Join thousands of AI enthusiasts in the World of AI!

Best Free OpenHermes-13B Alternatives (and Paid)

By Rishit