The taki555/Qwen3-4B-Shadow-FT-BAAI-2k model is a 4 billion parameter instruction-tuned language model based on the Qwen3-4B architecture. Developed by Taiqiang Wu et al., it utilizes the novel Shadow-FT framework to improve instruction-following capabilities by grafting learned weight updates from a fine-tuned base model onto an instruction-tuned variant. This model is specifically tuned on subsets of the BAAI-2k dataset, offering enhanced performance for instruction-based tasks.
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taki555/Qwen3-4B-Shadow-FT-BAAI-2kMost 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.