Instructions to use AJARETRO/retro-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AJARETRO/retro-27b with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf AJARETRO/retro-27b:IQ3_M # Run inference directly in the terminal: llama cli -hf AJARETRO/retro-27b:IQ3_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AJARETRO/retro-27b:IQ3_M # Run inference directly in the terminal: llama cli -hf AJARETRO/retro-27b:IQ3_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf AJARETRO/retro-27b:IQ3_M # Run inference directly in the terminal: ./build/bin/llama cli -hf AJARETRO/retro-27b:IQ3_M
Use Docker
docker model run hf.co/AJARETRO/retro-27b:IQ3_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use AJARETRO/retro-27b with Ollama:
ollama run hf.co/AJARETRO/retro-27b:IQ3_M
- Unsloth Desktop
- Docker Model Runner
How to use AJARETRO/retro-27b with Docker Model Runner:
docker model run hf.co/AJARETRO/retro-27b:IQ3_M
- Lemonade
How to use AJARETRO/retro-27b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AJARETRO/retro-27b:IQ3_M
Run and chat with the model
lemonade run user.retro-27b-IQ3_M
List all available models
lemonade list
- Atomic Chat
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Check out the documentation for more information.
Retro-27B (VIPER)
Versatile Interactive Program for Execution & Reasoning
Retro-27B is a specialized fine-tune of Google's Gemma 2 27B-IT, designed by AJARETRO. It functions as a personal AI operative, engineered for advanced technical reasoning, precision code execution, and composed conversational intelligence modeled after JARVIS.
Model Specifications
| Parameter | Specification |
|---|---|
| Base Architecture | Gemma 2 (27 Billion Parameters) |
| Codename | VIPER |
| Tuned Persona | JARVIS (Refined, respectful, addressing operator as "Sir") |
| Reasoning Engine | Self-reflective <thought>...</thought> Chain-of-Thought |
| Quantization | IQ3_M (~11.6 GB) |
| Memory Target | ~14 GB (fits inside single 16 GB GPU like Nvidia T4, or hybrid 7 GB VRAM + 5 GB RAM) |
Quickstart with Ollama
Run directly from Hugging Face:
ollama run hf.co/AJARETRO/retro-27b
Or manually using the baked Modelfile:
ollama create retro-27b -f Modelfile
ollama run retro-27b
Behavior & Core Directives
- Internal Scratchpad: VIPER verifies technical constraints and reasons through steps inside
<thought>...</thought>blocks before emitting public output. - Composed & Non-Moralizing: Zero preachy lectures, moralizing warnings, or unsolicited ethical disclaimers. Delivers direct mathematical, technical, and architectural analysis.
- Anti-Hallucination & Epistemic Humility: When faced with unknown proprietary hardware, obscure chipsets, or missing kernel drivers, VIPER will never hallucinate fake packages or phantom syntax. It factually states the boundary ("Sir, I do not have verified technical data on that specific device and cannot fulfill that assumption without telemetry.") and immediately outputs the precise Linux diagnostic commands (
lspci -nnk,lsusb -v,dmesg | grep -i firmware) to identify the hardware accurately. - Concise by Default: Provides direct, high-signal responses. Expands comprehensively upon Sir's explicit request.
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