miromind-ai/MiroThinker-32B-DPO-v0.1
MiroThinker-32B-DPO-v0.1 is a 32 billion parameter agentic language model developed by miromind-ai, built on Qwen3 architecture with a 32768 token context length. This DPO variant is designed for deep research and complex, long-horizon problem-solving, integrating advanced capabilities like task decomposition, multi-hop reasoning, and retrieval-augmented generation. It achieves state-of-the-art performance among open-source models on the GAIA benchmark, demonstrating strong capabilities in real-world agentic tasks.
Hugging Face 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.
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.
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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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