bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

初始化项目，由ModelHub XC社区提供模型

Browse Source 
Model: bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-08-28 01:24:12 +08:00
commit 0fb26ede4b
29 changed files with 297 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

47
.gitattributes vendored Normal file
View File

@@ -0,0 +1,47 @@
*.7z filter=lfs diff=lfs merge=lfs -text
*.arrow filter=lfs diff=lfs merge=lfs -text
*.bin filter=lfs diff=lfs merge=lfs -text
*.bin.* filter=lfs diff=lfs merge=lfs -text
*.bz2 filter=lfs diff=lfs merge=lfs -text
*.ftz filter=lfs diff=lfs merge=lfs -text
*.gz filter=lfs diff=lfs merge=lfs -text
*.h5 filter=lfs diff=lfs merge=lfs -text
*.joblib filter=lfs diff=lfs merge=lfs -text
*.lfs.* filter=lfs diff=lfs merge=lfs -text
*.model filter=lfs diff=lfs merge=lfs -text
*.msgpack filter=lfs diff=lfs merge=lfs -text
*.onnx filter=lfs diff=lfs merge=lfs -text
*.ot filter=lfs diff=lfs merge=lfs -text
*.parquet filter=lfs diff=lfs merge=lfs -text
*.pb filter=lfs diff=lfs merge=lfs -text
*.pt filter=lfs diff=lfs merge=lfs -text
*.pth filter=lfs diff=lfs merge=lfs -text
*.rar filter=lfs diff=lfs merge=lfs -text
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.tar.* filter=lfs diff=lfs merge=lfs -text
*.tflite filter=lfs diff=lfs merge=lfs -text
*.tgz filter=lfs diff=lfs merge=lfs -text
*.xz filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zstandard filter=lfs diff=lfs merge=lfs -text
*.tfevents* filter=lfs diff=lfs merge=lfs -text
*.db* filter=lfs diff=lfs merge=lfs -text
*.ark* filter=lfs diff=lfs merge=lfs -text
**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text
**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text
**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text
*.safetensors filter=lfs diff=lfs merge=lfs -text
*.ckpt filter=lfs diff=lfs merge=lfs -text
*.gguf* filter=lfs diff=lfs merge=lfs -text
*.ggml filter=lfs diff=lfs merge=lfs -text
*.llamafile* filter=lfs diff=lfs merge=lfs -text
*.pt2 filter=lfs diff=lfs merge=lfs -text
*.mlmodel filter=lfs diff=lfs merge=lfs -text
*.npy filter=lfs diff=lfs merge=lfs -text
*.npz filter=lfs diff=lfs merge=lfs -text
*.pickle filter=lfs diff=lfs merge=lfs -text
*.pkl filter=lfs diff=lfs merge=lfs -text
*.tar filter=lfs diff=lfs merge=lfs -text
*.wasm filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text

171
README.md Normal file
View File

@@ -0,0 +1,171 @@
---
quantized_by: bartowski
pipeline_tag: text-generation
base_model: nvidia/OpenReasoning-Nemotron-14B
base_model_relation: quantized
---
## Llamacpp imatrix Quantizations of OpenReasoning-Nemotron-14B by nvidia
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5934">b5934</a> for quantization.
Original model: https://huggingface.co/nvidia/OpenReasoning-Nemotron-14B
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [OpenReasoning-Nemotron-14B-bf16.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-bf16.gguf) | bf16 | 29.55GB | false | Full BF16 weights. |
| [OpenReasoning-Nemotron-14B-Q8_0.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q8_0.gguf) | Q8_0 | 15.70GB | false | Extremely high quality, generally unneeded but max available quant. |
| [OpenReasoning-Nemotron-14B-Q6_K_L.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q6_K_L.gguf) | Q6_K_L | 12.50GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [OpenReasoning-Nemotron-14B-Q6_K.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q6_K.gguf) | Q6_K | 12.12GB | false | Very high quality, near perfect, *recommended*. |
| [OpenReasoning-Nemotron-14B-Q5_K_L.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q5_K_L.gguf) | Q5_K_L | 10.99GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [OpenReasoning-Nemotron-14B-Q5_K_M.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q5_K_M.gguf) | Q5_K_M | 10.51GB | false | High quality, *recommended*. |
| [OpenReasoning-Nemotron-14B-Q5_K_S.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q5_K_S.gguf) | Q5_K_S | 10.27GB | false | High quality, *recommended*. |
| [OpenReasoning-Nemotron-14B-Q4_K_L.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q4_K_L.gguf) | Q4_K_L | 9.57GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [OpenReasoning-Nemotron-14B-Q4_1.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q4_1.gguf) | Q4_1 | 9.39GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [OpenReasoning-Nemotron-14B-Q4_K_M.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q4_K_M.gguf) | Q4_K_M | 8.99GB | false | Good quality, default size for most use cases, *recommended*. |
| [OpenReasoning-Nemotron-14B-Q3_K_XL.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q3_K_XL.gguf) | Q3_K_XL | 8.61GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [OpenReasoning-Nemotron-14B-Q4_K_S.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q4_K_S.gguf) | Q4_K_S | 8.57GB | false | Slightly lower quality with more space savings, *recommended*. |
| [OpenReasoning-Nemotron-14B-IQ4_NL.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-IQ4_NL.gguf) | IQ4_NL | 8.55GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [OpenReasoning-Nemotron-14B-Q4_0.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q4_0.gguf) | Q4_0 | 8.54GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [OpenReasoning-Nemotron-14B-IQ4_XS.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-IQ4_XS.gguf) | IQ4_XS | 8.12GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [OpenReasoning-Nemotron-14B-Q3_K_L.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q3_K_L.gguf) | Q3_K_L | 7.92GB | false | Lower quality but usable, good for low RAM availability. |
| [OpenReasoning-Nemotron-14B-Q3_K_M.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q3_K_M.gguf) | Q3_K_M | 7.34GB | false | Low quality. |
| [OpenReasoning-Nemotron-14B-IQ3_M.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-IQ3_M.gguf) | IQ3_M | 6.92GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [OpenReasoning-Nemotron-14B-Q3_K_S.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q3_K_S.gguf) | Q3_K_S | 6.66GB | false | Low quality, not recommended. |
| [OpenReasoning-Nemotron-14B-Q2_K_L.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q2_K_L.gguf) | Q2_K_L | 6.53GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [OpenReasoning-Nemotron-14B-IQ3_XS.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-IQ3_XS.gguf) | IQ3_XS | 6.38GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [OpenReasoning-Nemotron-14B-IQ3_XXS.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-IQ3_XXS.gguf) | IQ3_XXS | 5.95GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [OpenReasoning-Nemotron-14B-Q2_K.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-Q2_K.gguf) | Q2_K | 5.77GB | false | Very low quality but surprisingly usable. |
| [OpenReasoning-Nemotron-14B-IQ2_M.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-IQ2_M.gguf) | IQ2_M | 5.36GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| [OpenReasoning-Nemotron-14B-IQ2_S.gguf](https://huggingface.co/bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF/blob/main/nvidia_OpenReasoning-Nemotron-14B-IQ2_S.gguf) | IQ2_S | 5.00GB | false | Low quality, uses SOTA techniques to be usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
## Downloading using huggingface-cli
<details>
<summary>Click to view download instructions</summary>
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF --include "nvidia_OpenReasoning-Nemotron-14B-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/nvidia_OpenReasoning-Nemotron-14B-GGUF --include "nvidia_OpenReasoning-Nemotron-14B-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (nvidia_OpenReasoning-Nemotron-14B-Q8_0) or download them all in place (./)
</details>
## ARM/AVX information
Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.
Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.
As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.
Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.
<details>
<summary>Click to view Q4_0_X_X information (deprecated</summary>
I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
<details>
<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
</details>
</details>
## Which file should I choose?
<details>
<summary>Click here for details</summary>
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
</details>
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
Thank you ZeroWw for the inspiration to experiment with embed/output.
Thank you to LM Studio for sponsoring my work.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

1
configuration.json Normal file
View File

@@ -0,0 +1 @@
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

3
nvidia_OpenReasoning-Nemotron-14B-IQ2_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:cf3c44f5440d2dc8803f038a96b6562a9fe88addb55027d098215c06ec653f24
size 5356144800

3
nvidia_OpenReasoning-Nemotron-14B-IQ2_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f03f9959a3edbede7bd2359fa8798535e60e63f0ead9e0b551d6d95ec1102be8
size 5003724960

3
nvidia_OpenReasoning-Nemotron-14B-IQ3_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:29a57dc9b11cf7e2735819505f9101991f77d415189ac0efdef2a7f45fc11152
size 6916536480

3
nvidia_OpenReasoning-Nemotron-14B-IQ3_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:3694e2838a17fcfbf59e8dc86b178d5d6cee66f465e4d1322dc3e2c857105137
size 6383360160

3
nvidia_OpenReasoning-Nemotron-14B-IQ3_XXS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:4374f1b79517b1cdace5c5a1d8b89940d4860ed62d26e27bcf56d0b4fa428626
size 5946706080

3
nvidia_OpenReasoning-Nemotron-14B-IQ4_NL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:83c69447e019f3e21a192a98e8203440de7c01e3180f2fdc766acecdf7d6e21e
size 8549181600

3
nvidia_OpenReasoning-Nemotron-14B-IQ4_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:2db992557fecfa5ac22a72eaa90a0c1ea4d2b176e458d3ee91562a23b3a62676
size 8119838880

3
nvidia_OpenReasoning-Nemotron-14B-Q2_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:fbb3f2d648687524d93793ce494b6f8436badfa1232e179fb8501ad55b781d26
size 5770496160

3
nvidia_OpenReasoning-Nemotron-14B-Q2_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:863d1da8a8aad6be464fce88daf3241d13c40e7519ab4ba82f3d26352baa6ff4
size 6530816160

3
nvidia_OpenReasoning-Nemotron-14B-Q3_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ce56a73bb961d1bc95b85f6b7547b4b8dcd3918e83261c587d4f19cdfacf1f6e
size 7924766880

3
nvidia_OpenReasoning-Nemotron-14B-Q3_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:12cd5e5eca688d6a8700d1054a23a8155aaaa1e3b8198ceac54cdd0927cb2954
size 7339202720

3
nvidia_OpenReasoning-Nemotron-14B-Q3_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ae496cfb8b01328fd91634be5eb8ad41c6a47958fbddee8f4c273c1a46f574bf
size 6659594400

3
nvidia_OpenReasoning-Nemotron-14B-Q3_K_XL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:682730bc8db60255b0b9ba0fa498da3f7f1a938205fddbb5cbdd6a85c1a9526b
size 8606013600

3
nvidia_OpenReasoning-Nemotron-14B-Q4_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:dc1de9429acd33466139085faecfaae2b83fb8c9f2cef6031acbbb93f4ac4db9
size 8544266400

3
nvidia_OpenReasoning-Nemotron-14B-Q4_1.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:b8fe71a321784c5f00f7725a1164847b0e1219f78f009a5d220120bdbc8039b0
size 9392138400

3
nvidia_OpenReasoning-Nemotron-14B-Q4_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:9c4027e2301fc3ff9d8cb6ca2bc5eb9a3cf0a343aac3315dba6d72d48915f5ad
size 9565952160

3
nvidia_OpenReasoning-Nemotron-14B-Q4_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:e47e8a6b37cfcbe4f2f89179d2e628a29f0c05643a4d06dbca6588daf91e407c
size 8988108960

3
nvidia_OpenReasoning-Nemotron-14B-Q4_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:e9707e5ee3821f4e314715ed91c16fff2bf3f2077eb8fcd81a5f4ab7e688d2b8
size 8573429920

3
nvidia_OpenReasoning-Nemotron-14B-Q5_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ee3db81e2e2c8790197560625220aa8e3bd869f5184737196e58d1529aa1d2bd
size 10989394080

3
nvidia_OpenReasoning-Nemotron-14B-Q5_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:89ac094788e1e259d8a755ffedeff6e5f05b3b5778e33abd43150272a19a070a
size 10508871840

3
nvidia_OpenReasoning-Nemotron-14B-Q5_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:218c7651995acfd599cc3f4a6ec2f8fe6ec135a296b6fccf5ace8f4e3b4e533d
size 10266552480

3
nvidia_OpenReasoning-Nemotron-14B-Q6_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ea34fe861604c83126a77a3c24bbf585f15d0a720306cb694759c14cc323fc24
size 12124682400

3
nvidia_OpenReasoning-Nemotron-14B-Q6_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:5b092a5c867cbc28554163c32626053685200918c895fef5ca0a775404826ded
size 12501801120

3
nvidia_OpenReasoning-Nemotron-14B-Q8_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:d2d0a1dc1a5692a26a23711e23e48579158a9c473ab940af19e543e91f0d0cc3
size 15701596320

3
nvidia_OpenReasoning-Nemotron-14B-bf16.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:8128b6452b7e8da34fbf4c55c171b54be6515e90b339b01ae21f81050b761c67
size 29547714432

3
nvidia_OpenReasoning-Nemotron-14B.imatrix Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:299670ad0b4355f993bc9b2bbdd4ed004ab050334d5347b8f9b8d6fa945a9bba
size 8563610