samuelchristlie/tmax-9b-gguf overview
Tmax 9B GGUF Direct GGUF Quantizations of Tmax 9B This repository provides GGUF quantized models for allenai/tmax 9b . Tmax 9B is a 9 billion parameter termina…
Runs locally from ~3.56 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| allenai_tmax-9b-IQ3_S.gguf | GGUF | IQ3_S | 4.07 GB | Download |
| allenai_tmax-9b-IQ4_NL.gguf | GGUF | IQ4_NL | 5.07 GB | Download |
| allenai_tmax-9b-IQ4_XS.gguf | GGUF | IQ4_XS | 4.87 GB | Download |
| allenai_tmax-9b-Q2_K.gguf | GGUF | Q2_K | 3.56 GB | Download |
| allenai_tmax-9b-Q3_K_L.gguf | GGUF | Q3_K_L | 4.59 GB | Download |
| allenai_tmax-9b-Q3_K_M.gguf | GGUF | Q3_K_M | 4.31 GB | Download |
| allenai_tmax-9b-Q3_K_S.gguf | GGUF | Q3_K_S | 3.97 GB | Download |
| allenai_tmax-9b-Q4_0.gguf | GGUF | Q4_0 | 4.95 GB | Download |
| allenai_tmax-9b-Q4_K_M.gguf | GGUF | Q4_K_M | 5.24 GB | Download |
| allenai_tmax-9b-Q4_K_S.gguf | GGUF | Q4_K_S | 4.98 GB | Download |
| allenai_tmax-9b-Q5_0.gguf | GGUF | Q5_0 | 5.87 GB | Download |
| allenai_tmax-9b-Q5_K_M.gguf | GGUF | Q5_K_M | 6.02 GB | Download |
| allenai_tmax-9b-Q5_K_S.gguf | GGUF | Q5_K_S | 5.87 GB | Download |
| allenai_tmax-9b-Q6_K.gguf | GGUF | Q6_K | 6.85 GB | Download |
| allenai_tmax-9b-Q8_0.gguf | GGUF | Q8_0 | 8.87 GB | Download |
| allenai_tmax-9b-f16.gguf | GGUF | F16 | 16.69 GB | Download |
Model Details
| Model ID | samuelchristlie/tmax-9b-gguf |
|---|---|
| Author | samuelchristlie |
| Pipeline | — |
| License | apache-2.0 |
| Base model | allenai/tmax-9b,Qwen/Qwen3.5-9B |
| Last modified | 2026-06-24T13:21:00.000Z |
Model README
---
license: apache-2.0
base_model:
- allenai/tmax-9b
- Qwen/Qwen3.5-9B
---
Tmax-9B-GGUF
Direct GGUF Quantizations of Tmax-9B
This repository provides GGUF quantized models for allenai/tmax-9b.
Tmax-9B is a 9 billion parameter terminal-agent model developed by AllenAI and collaborators. Built on top of the Qwen3.5-9B 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. 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 📝
<a name="usage"/>
▶ Usage
1. Download Models
Download models using huggingface-cli:
pip install "huggingface_hub[cli]"
huggingface-cli download samuelchristlie/tmax-9B-gguf --local-dir ./tmax-9B-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).
<a name="license"/>
📃 License
This model is a GGUF conversion of the original allenai/tmax-9b 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.
<a name="acknowledgements"/>
🙏 Acknowledgements
- Allen Institute for AI (AI2) and collaborators for developing and open-sourcing the Tmax-9B model:
* https://huggingface.co/allenai/tmax-9b
- Qwen Team for the Qwen3.5-9B base model that powers Tmax-9B:
* https://huggingface.co/Qwen/Qwen3.5-9B
- The llama.cpp project and its contributors for the GGUF format and the incredible tooling that makes local LLM inference accessible.
* https://github.com/ggml-org/llama.cpp
- city96:
* https://huggingface.co/city96
</div>
Run samuelchristlie/tmax-9b-gguf with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models