The beyoru/Qwen3-4B-I-1209 model is a 4-billion parameter instruction-tuned language model, based on Qwen3-4B-Instruct-2507, developed by Beyoru. It is specifically fine-tuned using Reinforcement Learning (GRPO) with multiple reward functions to excel in tool-use and function call generation. This model demonstrates enhanced performance in tool-use scenarios, achieving an overall accuracy of 0.7233 on ACEBench, outperforming its base model and other similar-sized models.
Loading preview...
Model tree for
beyoru/Qwen3-4B-I-1209Most 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.