Kirim-V2 is an advanced general-purpose language model with 26 billion parameters, featuring an innovative sparse activation architecture where 18 billion parameters are actively engaged during inference. This design delivers high performance while maintaining computational efficiency.

Model Architecture

Total Parameters: 26B
Active Parameters: ~18B (sparse activation)
Context Length: Extended context window
Architecture: Transformer-based with mixture-of-experts components

Key Capabilities

Core Competencies

Natural language understanding and generation across multiple domains
Complex reasoning and multi-step problem solving
Code generation and technical documentation
Creative writing and content creation

Advanced Features

Web Search Integration: Built-in capability to search and retrieve real-time information
Tool Use: Seamless integration with external tools and APIs
Multilingual Support: Strong performance across multiple languages
Long-form Generation: Coherent output for extended documents and articles

Performance Highlights

Kirim-V2 represents a significant advancement over Kirim-V1, featuring:

Enhanced reasoning capabilities for complex tasks
Improved factual accuracy through integrated search
Better instruction following and task completion
More natural and contextually appropriate responses

Use Cases

Research & Analysis: Information gathering with real-time web search
Software Development: Code generation, debugging, and documentation
Content Creation: Articles, reports, creative writing, and technical documentation
Question Answering: Accurate responses with source verification
Task Automation: Multi-step workflows with tool integration

Model Specifications

Architecture: Sparse Transformer
Training Data: Diverse web corpus, code, and specialized datasets
Tokenizer: Custom trained tokenizer optimized for multilingual performance
Optimization: Mixed precision training with gradient checkpointing

Inference Example

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Kirim-ai/Kirim-V2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype="auto"
)

prompt = "Explain quantum entanglement in simple terms."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Limitations

May occasionally generate plausible-sounding but incorrect information
Performance depends on prompt quality and task complexity
Web search capability requires appropriate API configuration
Not specifically fine-tuned for safety-critical applications

Ethical Considerations

This model should be used responsibly. Users should verify critical information independently and be aware of potential biases in generated content. The model is not intended for making decisions in high-stakes scenarios without human oversight.

License

This model is released under the Apache 2.0 License.

Citation

bibtex

@model{kirimv2_2025,
  title={Kirim-V2: A 26B Parameter Sparse Activation Language Model},
  author={Qiling Tech},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/Kirim-ai/Kirim-V2}
}

Release Date: 2026 Model Type: Causal Language Model