Models / QwenQwen / / Qwen3 4B Base API
Qwen3 4B Base API
4.0B-parameter compact base model with 36-layer architecture and grouped-query attention trained on 36T multilingual tokens for efficient deployment.
To run this model you first need to deploy it on a Dedicated Endpoint.
Qwen3 4B Base API Usage
Endpoint
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How to use Qwen3 4B Base
Model details
Architecture Overview:
• Compact architecture with 36 layers, 32/8 Q/KV heads, 32K context
• Grouped-query attention for efficient deployment scenarios
• Optimized for good performance with resource constraints
• Designed for fine-tuning in cost-effective development environments
Training Foundation:
• Focused training for essential language modeling capabilities
• Optimized for efficient fine-tuning with limited computational resources
• Good baseline performance for common language tasks
• Designed for scenarios where efficiency and cost are important considerations
Fine-Tuning Capabilities:
• Efficient fine-tuning suitable for resource-constrained environments
• Good adaptation capabilities for specific tasks and domains
• Cost-effective training for creating specialized models
• Maintains quality while minimizing computational requirements
Prompting Qwen3 4B Base
Base Model Characteristics:
• Foundation model for fine-tuning and custom applications
• No special prompting required for base model usage
• Solid baseline performance with efficient resource utilization
• Designed for adaptation through cost-effective fine-tuning approaches
Efficient Training:
• Suitable for environments with computational and memory constraints
• Efficient fine-tuning processes with good baseline capabilities
• Cost-effective customization for specific applications
• Maintains performance while minimizing resource requirements
Development Considerations:
• Excellent for compact AI development projects
• Suitable for organizations with limited AI development budgets
• Efficient prototype development with production potential
• Good foundation for creating specialized models with resource efficiency
Applications & Use Cases
Cost-Effective Development:
• Mobile applications requiring custom AI training with size constraints
• Resource-constrained environments needing specialized language models
• Startup applications requiring efficient AI development approaches
• Educational tools requiring custom training with limited budgets
Practical Applications:
• Fine-tuning for specific business tasks with budget considerations
• Prototype development for AI applications with resource constraints
• Custom model training for small to medium enterprises
• Efficient deployment scenarios prioritizing cost over advanced capabilities
Specialized Scenarios:
• Applications requiring good language model capabilities with limited resources
• Development projects where computational efficiency is paramount
• Edge AI applications requiring custom training for specific deployment constraints
• Cost-sensitive implementations requiring specialized model behavior
