Hugging Face The tanliboy/lambda-qwen2.5-14b-dpo-test is a 14.8 billion parameter language model fine-tuned from Qwen/Qwen2.5-14B-Instruct. This model utilizes a large 131,072 token context length and has been optimized using Direct Preference Optimization (DPO) on the HuggingFaceH4/ultrafeedback_binarized dataset. It is designed for tasks requiring nuanced understanding and generation based on human preferences, demonstrating improved reward metrics over its base model.
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Popular Sampler Settings
Most 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.
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top_p
This setting controls the cumulative probability of considered top tokens. Must be in (0, 1]. Set to 1 to consider all tokens.
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top_k
This limits the number of top tokens to consider. Set to -1 to consider all tokens.
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frequency_penalty
This setting penalizes new tokens based on their frequency in the generated text. Values > 0 encourage new tokens; < 0 encourages repetition.
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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.
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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.
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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.
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