Tmax-2B-GGUF
Direct GGUF Quantizations of Tmax-2b
This repository provides GGUF quantized models for allenai/tmax-2b.
Tmax-2b is a 2 billion parameter terminal-agent model developed by AllenAI and collaborators. Built on top of the Qwen3.5-2B architecture and further trained using reinforcement learning for terminal-based tasks, it is designed to perform complex command-line and software engineering workflows while maintaining strong general-purpose reasoning capabilities. Note that the vision head was removed during training, making this a pure text-based model. These GGUF versions are optimized for efficient CPU and GPU inference using llama.cpp and compatible tools.
This release includes various quantization levels (e.g., Q2_K, Q3_K_M, Q4_K_M, Q5_K_M, Q6_K, Q8_0) to suit different hardware capabilities and performance requirements.
Table of Contents 📝
- ▶ Usage
- 📃 License
- 🙏 Acknowledgements
▶ Usage
1. Download Models
Download models using huggingface-cli:
pip install "huggingface_hub[cli]"
huggingface-cli download samuelchristlie/tmax-2b-gguf --local-dir ./tmax-2b-gguf
You can also download directly from this page
2. Inference
To use these GGUF files, you'll need a compatible inference engine like llama.cpp or clients built on top of it (e.g., Ollama, LM Studio, KoboldCpp, text-generation-webui with llama.cpp backend).
📃 License
This model is a GGUF conversion of the original allenai/tmax-2b model. The original model is licensed under the Apache 2.0 License, and this derivative work adheres to the terms of that license. Please review the original license for full details.
🙏 Acknowledgements
- Allen Institute for AI (AI2) and collaborators for developing and open-sourcing the tmax-2b model:
- Qwen Team for the Qwen3.5-2B base model that powers tmax-2b:
- The llama.cpp project and its contributors for the GGUF format and the incredible tooling that makes local LLM inference accessible.
- city96: