prithivMLmods/Hatshepsut-Qwen3_QWQ-LCoT-4B is a fine-tuned variant of the Qwen3-4B architecture, optimized for precise mathematical reasoning and logic-driven multi-step solutions. This 4 billion parameter model is explicitly trained on QWQ Synthetic datasets with support for Least-to-Complexity-of-Thought (LCoT) prompting. It excels at structured technical outputs, including code generation in Python, C++, and JavaScript, and supports multilingual reasoning across over 20 languages. The model is designed to be compute-efficient and instruction-aligned, making it suitable for mid-tier GPUs.
Loading preview...
Model tree for
prithivMLmods/Hatshepsut-Qwen3_QWQ-LCoT-4BMost 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.