Openthinker 32B - Details

Openthinker 32B is a large language model developed by Bespoke Labs, featuring 32 billion parameters. It is licensed under the Apache License 2.0 (Apache-2.0), with None (None) options available. This model is a fine-tuned variant of Qwen2.5, demonstrating superior performance on certain benchmarks compared to DeepSeek-R1.

Openthinker 32B is a fine-tuned version of Qwen/Qwen2.5-32B-Instruct trained on the OpenThoughts-114k dataset, designed to enhance reasoning and conversational capabilities. It is fully open-source, with model weights, datasets, data generation code, evaluation code, and training code publicly available. The model was trained on AWS SageMaker using 8x H100 P5 nodes for 90 hours, supporting a 16k context length to handle extended input sequences effectively.

Openthinker 32B features 32 billion parameters, placing it in the large model category, which offers strong performance for complex tasks but requires significant computational resources. Its 16k context length falls into the long context range, enabling effective handling of extended input sequences while demanding more memory and processing power. This combination makes the model well-suited for tasks requiring deep reasoning and extensive text analysis, though it may not be optimal for environments with limited hardware capabilities.

• Parameter Size: 32b (Large Model) – Powerful for complex tasks, but resource-intensive.
• Context Length: 16k (Long Context) – Ideal for extended text processing, requiring more resources.

Openthinker 32B is a large language model with 32 billion parameters and a 16k context length, making it a tool that could be used for research, development, and education. Its size and context capabilities suggest possible applications in tasks requiring deep analysis, such as academic studies, experimental software projects, or educational tools. However, these possible uses would need thorough exploration to ensure alignment with specific goals and constraints. The model’s open-source nature also opens potential opportunities for collaborative innovation, though its resource demands may limit accessibility in some settings.

• Openthinker 32B for research – Could support exploratory studies or hypothesis testing.
• Openthinker 32B for development – Might aid in creating new tools or refining existing systems.
• Openthinker 32B for education – Could enhance learning materials or interactive teaching methods.

Openthinker 32B is a large language model with 32 billion parameters and a 16k context length, which could offer possible applications in areas like advanced academic research, complex system development, or interactive educational platforms. Its open-source nature and high parameter count suggest possible uses for tasks requiring deep reasoning, such as analyzing large datasets, generating detailed technical documentation, or supporting collaborative innovation projects. However, these possible applications would need thorough evaluation to ensure they align with specific requirements and constraints. The model’s design also raises possible opportunities for experimenting with new workflows or enhancing existing tools, but its resource demands may limit accessibility in some contexts.

• Openthinker 32B for advanced academic research – Could support exploratory studies requiring extensive data analysis.
• Openthinker 32B for complex system development – Might aid in creating or refining tools for technical problem-solving.
• Openthinker 32B for interactive educational platforms – Could enhance learning experiences through detailed explanations or simulations.
• Openthinker 32B for technical documentation generation – Might assist in producing comprehensive or specialized content.

Each application must be thoroughly evaluated and tested before use to ensure suitability and effectiveness.

Openthinker 32B's medium q4 version requires a GPU with at least 24GB VRAM and a system with 32GB RAM to run efficiently, balancing precision and performance. This configuration ensures compatibility with high-end consumer or entry-level professional GPUs, though multiple GPUs may be needed for optimal results. Openthinker 32B's hardware demands reflect its 32B parameter size, making it suitable for users with access to capable hardware.

• fp16
• q4
• q8

Openthinker 32B is a large language model with 32 billion parameters and a 16k context length, fine-tuned from Qwen2.5 to enhance reasoning and conversational capabilities. It is fully open-source, with training data, code, and model weights publicly available, and was trained on AWS SageMaker using 8x H100 P5 nodes for 90 hours, making it suitable for advanced research and development tasks.

Huggingface Model Page
Ollama Model Page