base_model: PJMixers-Dev/L3.2-Instruct-Thinking-v0.1-1B
datasets:
- >-
PJMixers-Dev/Weyaxi_HelpSteer-filtered-gemini-2.0-flash-thinking-exp-1219-CustomShareGPT
- PJMixers-Dev/lmsys-chat-1m-gemini-2.0-flash-thinking-exp-1219-CustomShareGPT
- >-
PJMixers-Dev/allenai_WildChat-1M-gemini-2.0-flash-thinking-exp-1219-CustomShareGPT
- PJMixers-Dev/allenai_WildChat-1M-gemini-2.0-flash-exp-ShareGPT
- PJMixers-Dev/grimulkan_theory-of-mind-gemini-2.0-flash-exp-ShareGPT
- PJMixers-Dev/grimulkan_physical-reasoning-gemini-2.0-flash-exp-ShareGPT
- >-
PJMixers-Dev/WizardLMTeam_WizardLM_evol_instruct_70k-gemini-2.0-flash-thinking-exp-1219-CustomShareGPT
- >-
PJMixers-Dev/WizardLMTeam_WizardLM_evol_instruct_70k-gemini-2.0-flash-exp-ShareGPT
- PJMixers-Dev/allenai_WildChat-1M-gemini-exp-1206-ShareGPT
- PJMixers-Dev/grimulkan_theory-of-mind-gemini-exp-1206-ShareGPT
- PJMixers-Dev/FreedomIntelligence_medical-o1-reasoning-SFT-CustomShareGPT
language:
- en
library_name: transformers
license: llama3.2
quantized_by: mradermacher
About
weighted/imatrix quants of https://huggingface.co/PJMixers-Dev/L3.2-Instruct-Thinking-v0.1-1B
static quants are available at https://huggingface.co/mradermacher/L3.2-Instruct-Thinking-v0.1-1B-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 | i1-IQ1_S | 0.5 | for the desperate |
| GGUF | i1-IQ1_M | 0.5 | mostly desperate |
| GGUF | i1-IQ2_XXS | 0.5 | |
| GGUF | i1-IQ2_XS | 0.6 | |
| GGUF | i1-IQ2_S | 0.6 | |
| GGUF | i1-IQ2_M | 0.6 | |
| GGUF | i1-Q2_K_S | 0.7 | very low quality |
| GGUF | i1-IQ3_XXS | 0.7 | lower quality |
| GGUF | i1-Q2_K | 0.7 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XS | 0.7 | |
| GGUF | i1-Q3_K_S | 0.7 | IQ3_XS probably better |
| GGUF | i1-IQ3_S | 0.7 | beats Q3_K* |
| GGUF | i1-IQ3_M | 0.8 | |
| GGUF | i1-Q3_K_M | 0.8 | IQ3_S probably better |
| GGUF | i1-Q3_K_L | 0.8 | IQ3_M probably better |
| GGUF | i1-IQ4_XS | 0.8 | |
| GGUF | i1-IQ4_NL | 0.9 | prefer IQ4_XS |
| GGUF | i1-Q4_0 | 0.9 | fast, low quality |
| GGUF | i1-Q4_K_S | 0.9 | optimal size/speed/quality |
| GGUF | i1-Q4_K_M | 0.9 | fast, recommended |
| GGUF | i1-Q4_1 | 0.9 | |
| GGUF | i1-Q5_K_S | 1.0 | |
| GGUF | i1-Q5_K_M | 1.0 | |
| GGUF | i1-Q6_K | 1.1 | 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.