Koleslaw Nemotron 30B A3B PromptEnhance
nvidia-nemotron-open-model-license
Model source
- Kind
- Other
- File size
- 12.4–58.8 GB
- Base model
- nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
- Version
- vRevision 796c145
- Updated
- Sep 27, 2026
- Downloads
- 1.2K
- Source
- Hugging Face
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nvidia-nemotron-open-model-license
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6 artifactsmodel-00001-of-00002.safetensors
safetensors · 15.4 GB · SHA-256 2bfba2bc1700…b2bb · Hugging Face
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safetensors · 12.4 GB · SHA-256 c0e074847c29…e848 · Hugging Face
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2 excerpts--- license: other license_name: nvidia-nemotron-open-model-license license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/ base_model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 base_model_relation: finetune pipeline_tag: text-generation language: - en tags: - prompt-engineering - prompt-enhancement - nemotron - moe - lora - gguf - ollama --- # Koleslaw Nemotron 30B — Prompt Enhancement A fine-tune of NVIDIA's **Nemotron-3-Nano-30B-A3B** (31.6B-parameter hybrid Mamba-2 + MoE + Attention, thinking-capable) that does one thing: turn rough, underspecified prompts into detailed, structured prompts ready to hand to any AI model. This is the exact model serving [koleslaw.ai](https://koleslaw.ai) in production — try it there first if you want to see what it does before downloading 17 GiB. The IQ4_NL quant runs on a **single 24GB GPU** (RTX 3090 / 4090, L4) with room to spare. ## What it does Give it a lazy prompt; it returns a structured one with labeled sections — ROLE / OBJECTIVE / CONTEXT / DELIVERABLES / CONSTRAINTS / OUTPUT FORMAT — with bracketed `<fill-in>` placeholders where it would otherwise have to invent facts, and an instruction to the answering model to state assumptions rather than guess. <details> <summary><b>Real example</b> — input: <i>"write a script to back up my postgres database"</i> (unedited output from the IQ4_NL quant in production)</summary> ``` ROLE Act as a senior DevOps engineer with deep expertise in PostgreSQL administration, Linux bash scripting, and database automation. You are expected to provide polished, production-ready code and clear explanations. OBJECTIVE Generate a complete, reusable backup script for a PostgreSQL database that automates the backup process with logging, error handling, compression, and retention management. SETUP PARAMETERS — use the following placeholder variables (marked clearly) instead of hardcoding real values. Instruct the model to make reasonable default assumptions where no value is provided: - `DB_HOST`: <host_ip_or_name> - `DB_PORT`: 5432 - `DB_NAME`: your_database_name - `BACKUP_USER`: your_db_username - `BACKUP_PASS`: your_db_password (handle securely — note recommended approach for production) - `BACKUP_PATH`: /path/to/backup/directory/<date>/database_name - `RETENTION_DAYS`: 7 **TASK REQUIREMENTS** Produce the followin...
Source context: 1178 downloads · 3 likes · Pipeline text-generation · Repo thunkaboutit/Koleslaw-Nemotron-30B-A3B-PromptEnhance
