asifahmed/open_llama_13b_NH

TEXT GENERATIONConcurrency Cost:1Model Size:13BQuant:FP8Ctx Length:4kLicense:mitArchitecture:Transformer Open Weights Cold

Nous-Hermes-Llama2-13b is a 13 billion parameter language model fine-tuned by Nous Research on over 300,000 instructions, primarily synthetic GPT-4 outputs. This model, with a 4096 token context length, is designed for long responses and exhibits a lower hallucination rate compared to similar models. It excels in general language tasks, instruction following, and creative text generation, maintaining consistency with previous Hermes models.

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Nous-Hermes-Llama2-13b: An Instruction-Tuned Llama2 Model

Nous-Hermes-Llama2-13b is a 13 billion parameter language model developed by Nous Research, with key contributions from Teknium and Emozilla. It is fine-tuned on over 300,000 instructions, predominantly derived from high-quality synthetic GPT-4 outputs, ensuring strong performance in knowledge, task completion, and style.

Key Capabilities & Differentiators

  • Extended Responses: Designed to generate longer, more comprehensive outputs.
  • Reduced Hallucination: Exhibits a lower rate of factual inaccuracies compared to other models.
  • Uncensored Output: Lacks OpenAI's built-in censorship mechanisms, offering more unrestricted generation.
  • Consistent Dataset: Utilizes the same dataset as the original Hermes on Llama-1, ensuring a familiar yet more capable experience.
  • 4096 Token Context: Supports a substantial context window for processing longer inputs and generating coherent long-form content.

Performance Highlights

This model demonstrates improved benchmark scores over its predecessor, Hermes-Llama1, across several metrics:

  • GPT4All Benchmark Average: Achieves 70.0 (up from 68.8).
  • BigBench Average: Scores 0.3657 (up from 0.328).
  • AGIEval Average: Reaches 0.372 (up from 0.354).

It currently holds top positions on ARC-c, ARC-e, Hellaswag, and OpenBookQA, and second place on Winogrande within GPT4all's benchmarking list.

Good For

  • Applications requiring detailed and extensive text generation.
  • Use cases where lower hallucination rates are critical.
  • Scenarios needing an instruction-following model without inherent content restrictions.
  • Developers familiar with the Alpaca prompt format, which this model follows.