Nous-Hermes-Llama2-13b is a 13 billion parameter language model developed by Nous Research, fine-tuned on over 300,000 instructions using the Llama 2 architecture with a 4096 token context length. This model is distinguished by its long response generation, reduced hallucination rates, and absence of OpenAI's censorship mechanisms. It was primarily trained on high-quality synthetic GPT-4 outputs, making it suitable for complex instruction following and knowledge-intensive tasks. The model shows strong performance in reasoning and common sense benchmarks, including top rankings on ARC-c, ARC-e, Hellaswag, and OpenBookQA.
Loading preview...
Model tree for
NousResearch/Nous-Hermes-Llama2-13bMost commonly used values from Featherless users
temperature
This setting influences the sampling randomness. Lower values make the model more deterministic; higher values introduce randomness. Zero is greedy sampling.
top_p
This setting controls the cumulative probability of considered top tokens. Must be in (0, 1]. Set to 1 to consider all tokens.
top_k
This limits the number of top tokens to consider. Set to -1 to consider all tokens.
frequency_penalty
This setting penalizes new tokens based on their frequency in the generated text. Values > 0 encourage new tokens; < 0 encourages repetition.
presence_penalty
This setting penalizes new tokens based on their presence in the generated text so far. Values > 0 encourage new tokens; < 0 encourages repetition.
repetition_penalty
This setting penalizes new tokens based on their appearance in the prompt and generated text. Values > 1 encourage new tokens; < 1 encourages repetition.
min_p
This setting representing the minimum probability for a token to be considered relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.