Baon2024/Qwen2.5-0.5B-SFT-training3 is a 0.5 billion parameter language model fine-tuned from Qwen/Qwen2.5-0.5B. Developed by Baon2024, this model was trained using Supervised Fine-Tuning (SFT) on the HuggingFaceTB/smoltalk2 dataset. It is designed for general text generation tasks, leveraging its compact size and fine-tuned capabilities for efficient deployment. The model maintains a substantial context length of 131072 tokens, making it suitable for processing longer inputs.
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Baon2024/Qwen2.5-0.5B-SFT-training3Most 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.