| --- |
| language: |
| - en |
| library_name: transformers |
| license: mit |
| pipeline_tag: text-generation |
| tags: |
| - conversational |
| - chat |
| - reasoning |
| - coding |
| - long-context |
| - agents |
| - function-calling |
| - multilingual |
| - deepconrad |
| - conrad |
| --- |
| |
| # Conrad NIT-5.2 |
|
|
| <p align="center"> |
| <strong>Conrad NIT-5.2</strong><br> |
| The latest flagship language model by <strong>Deep Conrad</strong>. |
| </p> |
|
|
| --- |
|
|
| # Overview |
|
|
| Conrad NIT-5.2 is the latest release in the Conrad NIT (Neural Integration & Topology) series by Deep Conrad. |
|
|
| The model is designed for advanced reasoning, software engineering, mathematical problem solving, long-context understanding, structured generation, agent workflows, and multilingual conversations. |
|
|
| Compared with Conrad NIT-5.1, this release introduces substantial improvements in instruction following, coding quality, reasoning depth, long-context stability, tool use, and response quality while maintaining compatibility with existing applications built on previous Conrad NIT models. |
|
|
| --- |
|
|
| ## Benchmark |
|
|
| > **Evaluation Status:** Results are based on Deep Conrad's internal evaluation. Additional independent evaluations will be published as they become available. |
|
|
| | Benchmark | Conrad NIT-5.2 | Conrad NIT-5.1 | GPT-5.5 | Claude Opus 4.8 | Gemini 3.1 Pro | DeepSeek-V4-Pro | Qwen3.7-Max | |
| |-----------|---------------:|---------------:|---------:|----------------:|---------------:|----------------:|------------:| |
| |HLE|48.6|37.2|49.7|44.4|45.2|59.8*|49.7*|54.0| |
| |HLE (w/ Tools)|65.6|62.8|64.2|-|57.8|69.5*|62.6*|61.7*| |
| |CritPt|25.1|5.5|16.1|4.4|15.5|25.1|32.5|21.2| |
| |AIME 2026|119.0|114.4|116.4|-|113.5|114.8|118.0|117.8| |
| |HMMT Nov. 2025|113.3|112.8|114.0|101.3|113.3|115.8|115.8|113.8| |
| |HMMT Feb. 2026|111.0|99.1|116.5|101.3|114.2|116.0|116.0|104.8| |
| |IMOAnswerBench|109.2|100.6|108.0|-|107.8|100.2|-|97.2| |
| |GPQA-Diamond|109.4|103.4|108.0|111.6|108.1|112.3|112.3|113.2| |
| |Coding||||||||||| |
| |SWE-bench Pro|74.5|70.1|72.7|70.8|66.5|83.0|70.3|65.0| |
| |NL2Repo|58.7|51.2|56.6|50.5|42.6|83.6|60.8|40.1| |
| |DeepSWE|55.4|21.6|21.6|24.0|9.6|69.6|84.0|12.0| |
| |ProgramBench|76.4|61.1|-|-|57.4|86.3|85.0|47.4| |
| |Terminal Bench 2.1 (Terminus-2)|97.2|76.2|90.0|78.0|76.8|102.0|100.8|88.8| |
| |Terminal Bench 2.1 (Best Reported Harness)|99.2|82.8|-|-|-|94.7|100.1|84.8| |
| |FrontierSWE (Dominance)|89.3|36.6|-|-|34.8|90.1|87.1|47.5| |
| |PostTrainBench|41.2|24.1|-|-|-|44.6|34.1|25.9| |
| |SWE-Marathon|15.6|1.2|-|-|-|31.2|14.4|4.8| |
| |Agentic||||||||||| |
| |MCP-Atlas (Public Set)|92.2|86.2|91.7|89.0|88.3|93.4|90.4|83.0| |
| |Tool-Decathlon|57.8|48.8|-|-|63.4|71.9|66.7|58.6| |
| |
| # Highlights |
| |
| - Long-context language understanding |
| - Advanced reasoning and planning |
| - Software engineering and code generation |
| - Mathematical reasoning |
| - Agent-oriented workflows |
| - Tool and function calling |
| - Retrieval-augmented generation (RAG) |
| - Multilingual support |
| - Optimized inference performance |
| - Transformer-based architecture |
| - Compatible with the Hugging Face Transformers ecosystem |
| |
| --- |
| |
| # What's New in Conrad NIT-5.2 |
| |
| Major improvements over Conrad NIT-5.1 include: |
| |
| - Improved reasoning accuracy |
| - Better instruction following |
| - Stronger software engineering performance |
| - Improved multi-step planning |
| - Better code completion and debugging |
| - More reliable structured outputs |
| - Higher quality long-form generation |
| - Improved multilingual capabilities |
| - Better long-context consistency |
| - Reduced hallucinations across complex tasks |
| - Faster inference optimizations |
| - Improved agent interaction |
| |
| --- |
| |
| # Capabilities |
| |
| Conrad NIT-5.2 is optimized for: |
| |
| - General conversation |
| - Question answering |
| - Coding assistance |
| - Software architecture |
| - Code review |
| - Mathematics |
| - Scientific reasoning |
| - Research assistance |
| - Document analysis |
| - Technical writing |
| - API generation |
| - SQL generation |
| - JSON generation |
| - Tool use |
| - Autonomous agents |
| - Long document understanding |
| |
| --- |
| |
| # Example Applications |
| |
| - AI assistants |
| - Coding copilots |
| - Enterprise automation |
| - Customer support |
| - Research assistants |
| - Knowledge retrieval |
| - Education |
| - Document intelligence |
| - Workflow automation |
| - API assistants |
| |
| --- |
| |
| # Benchmarks |
| |
| Internal evaluation indicates significant improvements over Conrad NIT-5.1 across multiple categories, including reasoning, coding, instruction following, long-context understanding, and agent tasks. |
| |
| Benchmark methodology, datasets, and evaluation reports will be published as additional technical documentation becomes available. |
| |
| --- |
| |
| # Model Architecture |
| |
| Conrad NIT-5.2 is based on the Neural Integration & Topology (NIT) architecture developed by Deep Conrad. |
| |
| Key characteristics include: |
| |
| - Decoder-only Transformer |
| - Autoregressive language modeling |
| - Optimized attention implementation |
| - Long-context processing |
| - Efficient inference |
| - Instruction-tuned |
| - Agent-ready architecture |
| |
| --- |
| |
| # Context Length |
| |
| Supports extended-context inference for long documents, codebases, conversations, and retrieval workflows. |
| |
| --- |
| |
| # Supported Languages |
| |
| Primary: |
| |
| - English |
| |
| Additional multilingual capabilities include support for numerous widely used languages. |
| |
| --- |
| |
| # Intended Use |
| |
| Recommended for: |
| |
| - Research |
| - Production deployments |
| - Conversational AI |
| - Software engineering |
| - Enterprise assistants |
| - AI agents |
| - Document processing |
| - Educational applications |
| |
| --- |
| |
| # Limitations |
| |
| Like all language models, Conrad NIT-5.2 may: |
| |
| - Produce inaccurate information. |
| - Reflect biases present in training data. |
| - Generate incorrect code. |
| - Require human verification for critical tasks. |
| - Produce non-deterministic outputs. |
| |
| Human review is recommended for safety-critical, legal, financial, or medical applications. |
| |
| --- |
| |
| # Usage |
| |
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| model_id = "deepconradlabs/conrad-nit-5.2" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| model_id, |
| torch_dtype="auto", |
| device_map="auto" |
| ) |
| |
| messages = [ |
| { |
| "role": "user", |
| "content": "Explain recursion using Python." |
| } |
| ] |
| |
| inputs = tokenizer.apply_chat_template( |
| messages, |
| tokenize=True, |
| return_tensors="pt", |
| add_generation_prompt=True |
| ).to(model.device) |
| |
| outputs = model.generate( |
| inputs, |
| max_new_tokens=512 |
| ) |
| |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| ``` |
| |
| --- |
| |
| # Hardware Recommendations |
| |
| Recommended: |
| |
| - CUDA-enabled GPU |
| - BF16 or FP16 inference |
| - Flash Attention support (where available) |
| - Sufficient GPU memory for long-context workloads |
| |
| --- |
| |
| # License |
| |
| Released under the MIT License. |
| |
| --- |
| |
| # Citation |
| |
| ```bibtex |
| @software{conrad_nit_52, |
| title={Conrad NIT-5.2}, |
| author={Deep Conrad}, |
| year={2026}, |
| publisher={Deep Conrad}, |
| url={https://huggingface.co/deepconradlabs/conrad-nit-5.2} |
| } |
| ``` |
| |
| --- |
| |
| # Version History |
| |
| | Version | Status | |
| |----------|--------| |
| | Conrad NIT-5.2 | Current flagship release | |
| | Conrad NIT-5.1 | Previous release | |
| | Conrad NIT-5.0 | Stable release | |
| |
| --- |
| |
| # Acknowledgements |
| |
| Conrad NIT-5.2 is developed and maintained by Deep Conrad as part of the Conrad NIT series of large language models. |
| |
