Instructions to use mradermacher/Trinity-Large-Thinking-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/Trinity-Large-Thinking-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/Trinity-Large-Thinking-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download README.md from mradermacher/Trinity-Large-Thinking-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 9.91 kB
-
https://huggingface.co/mradermacher/Trinity-Large-Thinking-GGUF/resolve/main/README.md
- Command line
-
hf download hf://mradermacher/Trinity-Large-Thinking-GGUF/README.md
-
curl -L -o README.md https://huggingface.co/mradermacher/Trinity-Large-Thinking-GGUF/resolve/main/README.md
arxiv:
- 2602.17004
base_model: arcee-ai/Trinity-Large-Thinking
language:
- en
- es
- fr
- de
- it
- pt
- ru
- ar
- hi
- ko
- zh
library_name: transformers
license: other
license_link: LICENSE
license_name: openmdw-1.1
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- reasoning
- agentic
- tool-calling
- thinking
About
static quants of https://huggingface.co/arcee-ai/Trinity-Large-Thinking
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Trinity-Large-Thinking-i1-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| PART 1 PART 2 PART 3 | Q2_K | 144.9 | |
| PART 1 PART 2 PART 3 PART 4 | Q3_K_S | 171.8 | |
| PART 1 PART 2 PART 3 PART 4 | Q3_K_M | 189.4 | lower quality |
| P1 P2 P3 P4 P5 | Q3_K_L | 206.0 | |
| P1 P2 P3 P4 P5 | IQ4_XS | 212.7 | |
| P1 P2 P3 P4 P5 | Q4_K_S | 225.0 | fast, recommended |
| P1 P2 P3 P4 P5 | Q4_K_M | 239.7 | fast, recommended |
| P1 P2 P3 P4 P5 P6 | Q5_K_S | 274.4 | |
| P1 P2 P3 P4 P5 P6 | Q5_K_M | 282.2 | |
| P1 P2 P3 P4 P5 P6 P7 | Q6_K | 327.3 | very good quality |
| P1 P2 P3 P4 P5 P6 P7 P8 P9 | Q8_0 | 423.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
Thanks
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.
