Penetrating AI open source Penetration – Apple 1.7ba fine-tuned variant of QWEN3-1.7B, designed to perform highly accurate and structured code merging tasks. Draw inspiration from IDE agents like the cursor’s “Instant Apply” and permeate – Apple 1.7B has been optimized for context-sensitive functional-level code editing. Compared to the larger base model, this model achieves powerful performance by leveraging code-specific format tags, high-quality datasets, and model context protocol (MCP) integration.
Special for code merging tasks
Unlike general LLMs that have difficulties with differential applications and semantic merging, penetration-apply-1.7b is trained to apply structured editing at the functional or block level. The model adopts three structured inputs: (1) original code, (2) edit or difference sets, and (3) expected merge format. It then returns the modified code block where the changes are applied in this block. This format aligns with production-grade expectations and simplifies validation.
Training and reward structure
Penetration apply-1.7b is fine-tuned in approximately 100,000 real worlds Submit packft Dataset, accounting for less than 15% of the complete corpus. The structure of each training sample represents a practical developer workflow. Reward-based post-training system is used:
- Complete match (including format): Reward = 1.0
- Semantic Match (Ignore the Blank Line): Reward = 0.2
- Incorrect or failed: Reward = 0.0
This reward mode enhances high-fidelity output while allowing some leniency to style differences and closely mimics how code comments work in practice.
Benchmark results
Permeability AI tested the model using 10,000 sample evaluation Submit packft Dataset. The average reward score indicates excellent performance relative to larger LLMs:
| Model | Reward points |
|---|---|
| Penetration – Apple 1.7b | 0.9805 |
| Claude 4 sonnets | 0.9328 |
| GPT-3.5-Turbo | 0.8639 |
| Gemini-2.5-Flash | 0.7745 |
These results highlight the strength of the model when applying local changes while preserving semantics, formats, and structures.
MCP integration for developer workflows
The key feature of this model is its native support Model Context Protocol (MCP)enable structured context calls using file hierarchy, function names, and edit tags. This model adheres to apply-code MCP specifications that allow seamless use in CLI tools and IDE agents. It returns changes within the scope of the feature level and edits with well-structured XML-style tag tags, simplifying DIFF tracking and downstream tools.
Developer Tools and Use Cases
Penetration AI also released a reference implementation that supports local reasoning and integration with services such as VLLM or GULP servers. The tool includes CLI-based usage examples, MCP server implementation and secure deployment guides.
Key use cases include:
- IDE agent that provides “instant request” for changes to users
- CI Robots apply automatic reactors or audit-based changes
- Dataset generation pipeline for downstream fine-tuning
- Code conversion tool that merges logic with structure-aware logic
Format and deployment
Model output edit package and safetensors and GGUF For efficient deployment. Penetration can be hosted locally or in a quantitative mode - apply-1.7b to optimize inferences of constrained hardware.
Availability and License
Penetration – apply-1.7b is available under the Apache-2.0 license and is hosted on Hug and GitHub. This version includes all the necessary scripts for inference, MCP-compliant deployment examples, and a guide to structured formats.
in conclusion
Through open source penetration – Apply-1.7b, penetration AI addresses the key needs of functional-level, structure-aware code editing models. Unlike the base model, this professional model combines compact size with precision and format alignment. Its MCP integration, reward-based fine-tuning and syntactic structure support make it an ideal candidate for real-world developer tools.
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Asif Razzaq is CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, ASIF is committed to harnessing the potential of artificial intelligence to achieve social benefits. His recent effort is to launch Marktechpost, an artificial intelligence media platform that has an in-depth coverage of machine learning and deep learning news that can sound both technically, both through technical voices and be understood by a wide audience. The platform has over 2 million views per month, demonstrating its popularity among its audience.
