Mit-ThinkDeeply is the advanced version of the Mit series of large language models (LLMs) developed by WinkingFace. Built upon the robust foundation of the Mit base model, Mit-ThinkDeeply introduces enhanced reasoning capabilities, superior contextual understanding, and refined function-calling precision. This model is designed to seamlessly integrate intuitive conversational abilities with advanced multi-step reasoning, making it ideal for complex analytical tasks, structured problem-solving, and high-stakes decision-making.
Key features of Mit-ThinkDeeply include:
Advanced Reasoning: Capable of generating long chains of thought to deeply analyze problems and provide well-reasoned solutions.
Enhanced Contextual Awareness: Improved ability to maintain coherence across multi-turn conversations and long-form interactions.
Function Calling Precision: Optimized for reliable and accurate execution of tool calls, enabling seamless integration with external APIs and services.
Versatile Use Cases: Adaptable for both standard conversational tasks and complex reasoning scenarios, including mathematical problem-solving, code generation, and structured output generation.
Long Context Support: Supports context lengths of up to 128K tokens, ensuring robust performance in applications requiring extensive input data.
Mit-ThinkDeeply has undergone extensive architectural refinements and fine-tuning to align more effectively with real-world applications. Our training process emphasizes deeper contextual awareness, enhanced response coherence, and improved execution of function-calling, making Mit-ThinkDeeply a powerful and versatile AI system.
For users, to achieve chatbot-like experience, it is recommended to commence in the conversation mode:
shell
1./llama-cli -m <gguf-file-path>\2 -co -cnv -p "You are Mit, created by WinkingFace. You are a deep thinking AI, capable of using extremely long chains of thought to deeply consider the problem and deliberate via systematic reasoning processes. Enclose your thoughts and internal monologue inside <think_deeply> </think_deeply> tags, and then provide your solution or response to the problem."\3 -fa -ngl 80 -n 512
Evaluation & Performance
Category
Benchmark (Metric)
Mit-ThinkDeeply-0.5B
Mit-ThinkDeeply-1.5B
Mit-ThinkDeeply-3B
Mit-ThinkDeeply-7B
Context Length
32K
32K
32K
128K
Generation Length
8K
8K
8K
8K
General
MMLU
45.4
58.9
63.8
72.6
MMLU-pro
13.8
26.6
33.0
43.7
MMLU-redux
43.1
56.8
62.7
70.3
BBH
18.3
41.7
64.9
68.1
ARC-C
32.9
56.0
57.5
65.8
Code
LiveCodeBench
11.5
21.4
25.9
36.2
HumanEval
25.4
44.6
51.6
69.5
HumanEval+
29.7
38.1
43.9
60.7
MBPP
46.3
74.2
69.9
82.9
MBPP+
36.8
59.5
59.3
70.2
MultiPL-E
24.9
51.7
49.6
58.1
Mathematics
GPQA
25.1
29.0
31.5
40.7
Theoremqa
18.2
23.2
27.9
39.4
MATH
25.4
38.1
46.7
54.8
MATH-500
62.5
79.2
88.4
94.6
MMLU-stem
43.3
65.8
75.1
81.3
GSM8K
45.8
70.1
81.5
86.2
Citation
If you find our work helpful, feel free to cite us:
@misc{mit-thinkdeeply,
title = {Mit-ThinkDeeply: Advanced Reasoning and Contextual Awareness in Large Language Models},
author = {WinkingFace Team},
year = {2025},
url = {https://huggingface.co/WinkingFace/Mit-ThinkDeeply-7B}
}
