bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-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_Nemotron-H-8B-Reasoning-128K-GGUF
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-07-31 11:02:13 +08:00
commit 0fe33c60f0
28 changed files with 287 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

164
README.md Normal file
View File

@@ -0,0 +1,164 @@
---
quantized_by: bartowski
pipeline_tag: text-generation
base_model_relation: quantized
base_model: nvidia/Nemotron-H-8B-Reasoning-128K
---
## Llamacpp imatrix Quantizations of Nemotron-H-8B-Reasoning-128K by nvidia
Using <a href="https://github.com/ggml-org/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggml-org/llama.cpp/releases/tag/b6317">b6317</a> for quantization.
Original model: https://huggingface.co/nvidia/Nemotron-H-8B-Reasoning-128K
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) combined with a subset of combined_all_small.parquet from Ed Addario [here](https://huggingface.co/datasets/eaddario/imatrix-calibration/blob/main/combined_all_small.parquet)
Run them in [LM Studio](https://lmstudio.ai/)
Run them directly with [llama.cpp](https://github.com/ggml-org/llama.cpp), or any other llama.cpp based project
## Prompt format
No prompt format found, check original model page
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Nemotron-H-8B-Reasoning-128K-bf16.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-bf16.gguf) | bf16 | 16.21GB | false | Full BF16 weights. |
| [Nemotron-H-8B-Reasoning-128K-Q8_0.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q8_0.gguf) | Q8_0 | 8.62GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Nemotron-H-8B-Reasoning-128K-Q6_K_L.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q6_K_L.gguf) | Q6_K_L | 6.92GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Nemotron-H-8B-Reasoning-128K-Q6_K.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q6_K.gguf) | Q6_K | 6.66GB | false | Very high quality, near perfect, *recommended*. |
| [Nemotron-H-8B-Reasoning-128K-Q5_K_L.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q5_K_L.gguf) | Q5_K_L | 6.13GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Nemotron-H-8B-Reasoning-128K-Q5_K_M.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q5_K_M.gguf) | Q5_K_M | 5.80GB | false | High quality, *recommended*. |
| [Nemotron-H-8B-Reasoning-128K-Q5_K_S.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q5_K_S.gguf) | Q5_K_S | 5.65GB | false | High quality, *recommended*. |
| [Nemotron-H-8B-Reasoning-128K-Q4_K_L.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q4_K_L.gguf) | Q4_K_L | 5.38GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Nemotron-H-8B-Reasoning-128K-Q4_1.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q4_1.gguf) | Q4_1 | 5.18GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [Nemotron-H-8B-Reasoning-128K-Q4_K_M.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q4_K_M.gguf) | Q4_K_M | 4.98GB | false | Good quality, default size for most use cases, *recommended*. |
| [Nemotron-H-8B-Reasoning-128K-Q4_K_S.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q4_K_S.gguf) | Q4_K_S | 4.78GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Nemotron-H-8B-Reasoning-128K-Q4_0.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q4_0.gguf) | Q4_0 | 4.74GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [Nemotron-H-8B-Reasoning-128K-Q3_K_XL.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q3_K_XL.gguf) | Q3_K_XL | 4.74GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Nemotron-H-8B-Reasoning-128K-IQ4_NL.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-IQ4_NL.gguf) | IQ4_NL | 4.71GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [Nemotron-H-8B-Reasoning-128K-IQ4_XS.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-IQ4_XS.gguf) | IQ4_XS | 4.47GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Nemotron-H-8B-Reasoning-128K-Q3_K_L.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q3_K_L.gguf) | Q3_K_L | 4.27GB | false | Lower quality but usable, good for low RAM availability. |
| [Nemotron-H-8B-Reasoning-128K-Q3_K_M.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q3_K_M.gguf) | Q3_K_M | 4.03GB | false | Low quality. |
| [Nemotron-H-8B-Reasoning-128K-IQ3_M.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-IQ3_M.gguf) | IQ3_M | 3.79GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Nemotron-H-8B-Reasoning-128K-Q3_K_S.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q3_K_S.gguf) | Q3_K_S | 3.70GB | false | Low quality, not recommended. |
| [Nemotron-H-8B-Reasoning-128K-Q2_K_L.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q2_K_L.gguf) | Q2_K_L | 3.68GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Nemotron-H-8B-Reasoning-128K-IQ3_XS.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-IQ3_XS.gguf) | IQ3_XS | 3.63GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Nemotron-H-8B-Reasoning-128K-IQ3_XXS.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-IQ3_XXS.gguf) | IQ3_XXS | 3.30GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Nemotron-H-8B-Reasoning-128K-Q2_K.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-Q2_K.gguf) | Q2_K | 3.16GB | false | Very low quality but surprisingly usable. |
| [Nemotron-H-8B-Reasoning-128K-IQ2_M.gguf](https://huggingface.co/bartowski/nvidia_Nemotron-H-8B-Reasoning-128K-GGUF/blob/main/nvidia_Nemotron-H-8B-Reasoning-128K-IQ2_M.gguf) | IQ2_M | 2.93GB | false | Relatively low quality, uses SOTA techniques to be surprisingly 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_Nemotron-H-8B-Reasoning-128K-GGUF --include "nvidia_Nemotron-H-8B-Reasoning-128K-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_Nemotron-H-8B-Reasoning-128K-GGUF --include "nvidia_Nemotron-H-8B-Reasoning-128K-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (nvidia_Nemotron-H-8B-Reasoning-128K-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/ggml-org/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/ggml-org/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/ggml-org/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/ggml-org/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_Nemotron-H-8B-Reasoning-128K-IQ2_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:8e4d891f321ea4847417f6e86c46d68bc4dd169353e3190a46c196d0edb279dc
size 2931592160

3
nvidia_Nemotron-H-8B-Reasoning-128K-IQ3_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:3948900993e7c1a0f46c85fe2fb0a000a37a35a634de065c5be0d4cfb533751d
size 3785657312

3
nvidia_Nemotron-H-8B-Reasoning-128K-IQ3_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ca0beba7afc34067b37972a2b685988cfa0dfeb399b3b44c1f510a419973c19d
size 3626077152

3
nvidia_Nemotron-H-8B-Reasoning-128K-IQ3_XXS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:ec77bab87061b7812ecf2d46562ac16ba645f92524bec7d790da6e22acb78a5a
size 3301608416

3
nvidia_Nemotron-H-8B-Reasoning-128K-IQ4_NL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:a83df883ad44b5b747f856cdb381479ecc69c59fc94eb25fda240880fa77382e
size 4708699104

3
nvidia_Nemotron-H-8B-Reasoning-128K-IQ4_XS.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:7dc3d83c2b3ba991b474af1fdc588e0d19109da249baacfb6d1a0814221e2e36
size 4472376288

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q2_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:dcd51eba859f78de0544405cdf8d2ce67aa8fb106effbfdb829d7888065c6edd
size 3159493600

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q2_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:bfdfc45a684b97edb88b8bac7f95d0aff1b54f8937ba4e08b9154c0d48027a6a
size 3683781600

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q3_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:624cd0b23892be47d5d22a5aaee5640d2ac361dd3c06be151d6aea34bee89390
size 4270951392

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q3_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f32a39e8e797fde71f1bf394516a8765935cb7ca3d0f89358c4b92e8ed700b72
size 4030303200

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q3_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:a4e3b9fd85b5f542c4f675b450da54ce324a3c35b0c0adb27dd61286392740fb
size 3704327136

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q3_K_XL.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:c316a334c946e575f8b081436ccec5258ca84d0c5e9971250f7afe8ad98b2fd9
size 4740713440

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q4_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:cd1c37f82fe3ab8c753585742b960ad8c57c50b026c2e0604a9a4e1bc3ff71be
size 4741729248

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q4_1.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:dfdbd5306f34f8025b50985bc72c851099568a65d6b30b2ca7f3688da48c1978
size 5181344736

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q4_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:37a5a0573eef45fa43f91cf8632d479c25cacb800d9e1c356fc72e52085efd66
size 5381819360

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q4_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:312ebd50999058707868f49011d87225fcc92af0f5a526cd3553f821dc161956
size 4983360480

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q4_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:0968c2b1a90e4ad35c0b68ad3991f634c807eb10a3da5d15942516e2877eedd3
size 4776856544

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q5_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:976b10ec96e6bccf40320731adbebc7e472293cfdb6bdf9d1f4efc3a3009a749
size 6126832608

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q5_K_M.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:2624f499b97e138cb272823212183c2219a8537a295ba737d2d0cc09142f7453
size 5795482592

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q5_K_S.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:0a6f16d1adbf3a0b73d95e2be14dd0899a54a64b28815bf61b02ba6c78e6a096
size 5653990368

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q6_K.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:95c71afeac191b8cda9d84954578fd6d892fa42cf5b2bc3ea07caf4b3698d506
size 6658362336

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q6_K_L.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:4f0996edc6ed1b5f43a8524ad1d8cfaaf31f05c566dd3fb04b50475e7420b277
size 6918409184

3
nvidia_Nemotron-H-8B-Reasoning-128K-Q8_0.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f9b1a4593565561275d6438ce4a3a0c455369aa3d7abecffcc8e3d0b7ecd0fe6
size 8619887584

3
nvidia_Nemotron-H-8B-Reasoning-128K-bf16.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:73a43da226bf9502584260661ed466ec8c3b97f712db4d70a968e7b35279be77
size 16212888224

3
nvidia_Nemotron-H-8B-Reasoning-128K-imatrix.gguf Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:9b349909b4a869195f75fea420a3a3344a5efde64407ffa410245c2d535b09a3
size 3916672