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---
base_model: nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
datasets:
- nvidia/Nemotron-CC-v2
- nvidia/Nemotron-Post-Training-Dataset-v2
- nvidia/Nemotron-Science-v1
- nvidia/Nemotron-Instruction-Following-Chat-v1
- nvidia/Nemotron-Agentic-v1
- nvidia/Nemotron-Competitive-Programming-v1
- nvidia/Nemotron-Math-Proofs-v1
- nvidia/Nemotron-RL-Agentic-Conversational-Tool-Use-Pivot-v1
- nvidia/Nemotron-RL-instruction_following
- nvidia/Nemotron-RL-agent-calendar_scheduling
- nvidia/Nemotron-RL-instruction_following-structured_outputs
language:
- en
library_name: transformers
license: other
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/
license_name: nvidia-nemotron-open-model-license
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- nvidia
- pytorch
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#NVIDIA-Nemotron-3-Nano-4B-BF16-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Nano-4B-BF16-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) 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](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Nano-4B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Nano-4B-BF16.Q2_K.gguf) | Q2_K | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Nano-4B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Nano-4B-BF16.Q3_K_S.gguf) | Q3_K_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Nano-4B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Nano-4B-BF16.Q3_K_M.gguf) | Q3_K_M | 2.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Nano-4B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Nano-4B-BF16.IQ4_XS.gguf) | IQ4_XS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Nano-4B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Nano-4B-BF16.Q3_K_L.gguf) | Q3_K_L | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Nano-4B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Nano-4B-BF16.Q4_K_S.gguf) | Q4_K_S | 2.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Nano-4B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Nano-4B-BF16.Q4_K_M.gguf) | Q4_K_M | 2.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Nano-4B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Nano-4B-BF16.Q5_K_S.gguf) | Q5_K_S | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Nano-4B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Nano-4B-BF16.Q5_K_M.gguf) | Q5_K_M | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Nano-4B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Nano-4B-BF16.Q6_K.gguf) | Q6_K | 4.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Nano-4B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Nano-4B-BF16.Q8_0.gguf) | Q8_0 | 4.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/NVIDIA-Nemotron-3-Nano-4B-BF16-GGUF/resolve/main/NVIDIA-Nemotron-3-Nano-4B-BF16.f16.gguf) | f16 | 8.1 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->