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AutoGEO_mini_Qwen1.7B-i1-GGUF
mradermacher/AutoGEO_mini_Qwen1.7B-i1-GGUF
AutoGEO_mini_Qwen1.7B-i1-GGUF is a reinforcement learning model from mradermacher. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
weighted/imatrix quants of https://huggingface.co/cx-cmu/AutoGEOminiQwen1.7BResearchyGEO
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At a glance
- Task
- Reinforcement Learning
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- Dec 13, 2025
- Updated
- Jan 24, 2026
- SHA
- 7b27defb
Base models
Datasets
Model card
From the Hugging Face model README
About
<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: -->weighted/imatrix quants of https://huggingface.co/cx-cmu/AutoGEO_mini_Qwen1.7B_ResearchyGEO
<!-- provided-files -->For a convenient overview and download list, visit our model page for this model.
static quants are available at https://huggingface.co/mradermacher/AutoGEO_mini_Qwen1.7B-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 |
|---|---|---|---|
| GGUF | imatrix | 0.1 | imatrix file (for creating your own quants) |
| GGUF | i1-IQ1_S | 0.6 | for the desperate |
| GGUF | i1-IQ1_M | 0.6 | mostly desperate |
| GGUF | i1-IQ2_XXS | 0.7 | |
| GGUF | i1-IQ2_XS | 0.7 | |
| GGUF | i1-IQ2_S | 0.8 | |
| GGUF | i1-IQ2_M | 0.8 | |
| GGUF | i1-Q2_K_S | 0.8 | very low quality |
| GGUF | i1-IQ3_XXS | 0.9 | lower quality |
| GGUF | i1-Q2_K | 0.9 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XS | 0.9 | |
| GGUF | i1-IQ3_S | 1.0 | beats Q3_K* |
| GGUF | i1-Q3_K_S | 1.0 | IQ3_XS probably better |
| GGUF | i1-IQ3_M | 1.0 | |
| GGUF | i1-Q3_K_M | 1.0 | IQ3_S probably better |
| GGUF | i1-Q3_K_L | 1.1 | IQ3_M probably better |
| GGUF | i1-IQ4_XS | 1.1 | |
| GGUF | i1-IQ4_NL | 1.2 | prefer IQ4_XS |
| GGUF | i1-Q4_0 | 1.2 | fast, low quality |
| GGUF | i1-Q4_K_S | 1.2 | optimal size/speed/quality |
| GGUF | i1-Q4_K_M | 1.2 | fast, recommended |
| GGUF | i1-Q4_1 | 1.2 | |
| GGUF | i1-Q5_K_S | 1.3 | |
| GGUF | i1-Q5_K_M | 1.4 | |
| GGUF | i1-Q6_K | 1.5 | practically like static Q6_K |
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. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->- Task
- Reinforcement Learning
- Library
- transformers
- License
- mit
- Languages
- en
- Created
- Dec 13, 2025
- Updated
- Jan 24, 2026