Overview
Hybrid Thinking Modes: Seamlessly switch between a 'thinking' mode for complex reasoning and a 'non-thinking' mode for fast, general-purpose responses, allowing users to control the 'thinking budget'.
Mixture-of-Experts (MoE) Architecture: Offers both dense and MoE models, providing a range of options for performance and efficiency, with MoE models achieving comparable performance to larger dense models with a fraction of the activated parameters.
Extensive Multilingual Support: Natively supports 119 languages and dialects, enabling strong performance in cross-lingual understanding and generation.
Advanced Agentic Capabilities: Optimized for coding and tool use, with the ability to interact with external tools and APIs to perform complex, multi-step tasks.
Visual Guide
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Key Features
Switch between deep reasoning for complex problems and quick responses for simpler queries, giving users control over the model's 'thinking budget'.
Communicate and generate content in 119 languages and dialects, making it a truly global AI assistant.
Integrate with external tools and APIs to automate complex workflows and tasks.
Process and understand documents with up to 1 million tokens, enabling in-depth analysis of long texts.
Available under the Apache 2.0 license, allowing for broad use and modification.
Real-World Use Cases
Complex Document Analysis
ForA legal team needs to review a lengthy contract and identify all clauses related to liability.
Example Prompt / Workflow
Multilingual Customer Support
ForA global e-commerce company wants to provide 24/7 customer support in multiple languages.
Example Prompt / Workflow
Automated Code Generation
ForA developer needs to create a Python script to automate a data processing task.
Example Prompt / Workflow
Frequently Asked Questions
Pricing
Pros & Cons
Pros
- ✓ State-of-the-art performance on various benchmarks.
- ✓ Flexible hybrid thinking modes for different tasks.
- ✓ Extensive multilingual support.
- ✓ Open-source license allows for wide adoption and customization.
- ✓ Efficient MoE models reduce computational costs.
Cons
- ✕ Requires significant computational resources for larger models.
- ✕ The complexity of the hybrid model may require some learning for optimal use.
- ✕ As a relatively new model, the community and third-party tool support are still growing.
Quick Start
Step 1
Choose the right model size and type (dense or MoE) for your needs.
Step 2
Set up your environment with the required libraries, such as Transformers or ModelScope.
Step 3
Load the model and tokenizer from Hugging Face or ModelScope.
Step 4
Use the `apply_chat_template` function to format your prompts.
Step 5
Generate responses and parse the output, handling the 'thinking' and 'non-thinking' modes as needed.