Model Card for Bleenk

Model Summary

Bleenk 123B is an agentic large language model developed by Robi Labs for advanced software engineering tasks. The model is optimized for tool-driven workflows, large-scale codebase exploration, coordinated multi-file editing, and powering autonomous and semi-autonomous software engineering agents.

Bleenk is designed for long-horizon reasoning and real-world engineering environments rather than single-turn code generation.

Model Details

Model Description

• Developed by: Robi Labs
• Created for: Bleenk
• Funded by: Robi Labs
• Shared by: Robi Labs
• Model type: Agentic Large Language Model (LLM)
• Language(s) (NLP): Primarily English; supports multilingual code and technical text
• License: To be released by Robi Labs
• Finetuned from model: Proprietary pretraining and fine-tuning pipeline

Model Sources

• Demo: https://bleenk.app

Uses

Direct Use

• Software engineering agents
• AI-powered code assistants
• Codebase navigation and analysis
• Multi-file refactoring and maintenance
• Tool-augmented development workflows

Downstream Use

• Fine-tuning for organization-specific codebases
• Integration into internal developer platforms
• Agent frameworks for autonomous engineering

Out-of-Scope Use

• General-purpose chat or conversational agents
• High-risk decision-making without human oversight
• Tasks requiring domain-specific legal, medical, or financial guarantees

Bias, Risks, and Limitations

• The model may produce incorrect or incomplete code without verification
• Tool misuse may result in unintended system changes
• Performance depends on tool availability and prompt quality
• Trained primarily on publicly available and licensed data, which may encode historical biases

Recommendations

Users should employ strong sandboxing, testing, and human-in-the-loop review when deploying Bleenk in production environments.

How to Get Started with the Model

ollama pull RobiLabs/bleenk:latest
ollama run RobiLabs/bleenk:latest

Training Details

Training Data

The model was trained on a mixture of:

• Publicly available code repositories
• Licensed datasets
• Synthetic data generated for software engineering tasks

Training Procedure

Preprocessing

Data was filtered for quality, deduplicated, and normalized for code and technical text.

Training Hyperparameters

• Training regime: Mixed-precision training (bf16)

Evaluation

Testing Data, Factors & Metrics

Testing Data

• SWE-bench Verified
• SWE-bench Multilingual
• Terminal Bench

Metrics

• Task success rate
• Patch correctness
• Tool execution accuracy

Results

Model	Size (B Tokens)	SWE Bench Verified	SWE Bench Multilingual	Terminal Bench
Bleenk	123	73.2%	71.3%	45.5%
Devstral 2	123	72.2%	61.3%	40.5%
Devstral Small 2	24	65.8%	51.6%	32.0%
DeepSeek v3.2	671	73.1%	70.2%	46.4%
Kimi K2 Thinking	1000	71.3%	61.1%	35.7%
MiniMax M2	230	69.4%	56.5%	30.0%
GLM 4.6	455	68.0%	–	40.5%
Qwen 3 Coder Plus	480	69.6%	54.7%	37.5%
Gemini 3 Pro	–	76.2%	–	54.2%
Claude Sonnet 4.5	–	77.2%	68.0%	42.8%
GPT 5.1 Codex Max	–	77.9%	–	58.1%
GPT 5.1 Codex High	–	73.7%	–	52.8%

Environmental Impact

Environmental impact details will be released as measurements are finalized.

Technical Specifications

Model Architecture and Objective

Transformer-based large language model optimized for agentic reasoning and tool usage.

Compute Infrastructure

Hardware

Large-scale GPU/accelerator clusters

Software

Custom training and inference stack developed by Robi Labs

Model Card Authors

Robi Labs Research Team

Model Card Contact

hello@robiai.com