HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407 is a fine-tuned version of mistralai/Mistral-Nemo-Instruct-2407, developed by HumanLLMs. This model is specifically optimized to generate more human-like and conversational responses, enhancing natural language understanding and emotional intelligence. It was fine-tuned using Low-Rank Adaptation (LoRA) and Direct Preference Optimization (DPO) on a synthetic dataset of approximately 11,000 samples across 256 diverse topics. The model excels in conversational coherence, making it suitable for applications requiring natural and empathetic interactions.
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HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407Most 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.