GraySoft
Projects Models About FAQ Contact Download guIDE →
Model Intelligence Sheet

tatsuyaaaaaaa/nvidia-nemotron-nano-9b-v2-gguf overview

NVIDIAのNVIDIA-Nemotron-Nano-9B-v2をgguf変換したものです。 imatrix量子化時にはTFMC/imatrix-dataset-for-japanese-llmのデータセットを用いています。

ggufenjadataset:TFMC/imatrix-dataset-for-japanese-llmbase_model:nvidia/NVIDIA-Nemotron-Nano-9B-v2base_model:quantized:nvidia/NVIDIA-Nemotron-Nano-9B-v2endpoints_compatibleregion:usconversational
tatsuyaaaaaaa/nvidia-nemotron-nano-9b-v2-gguf visual
Downloads
3,874
Likes
0
Pipeline
Library
Visibility
Public
Access
Open

Repository Files & Downloads

15 files detected
Direct downloads for all repository files
FileTypeQuantizationSizeLink
NVIDIA-Nemotron-Nano-9B-v2_IQ3_M.gguf GGUF IQ3_M 4.85 GB Download
NVIDIA-Nemotron-Nano-9B-v2_IQ3_S.gguf GGUF IQ3_S 4.78 GB Download
NVIDIA-Nemotron-Nano-9B-v2_IQ3_XS.gguf GGUF IQ3_XS 4.78 GB Download
NVIDIA-Nemotron-Nano-9B-v2_IQ4_NL.gguf GGUF IQ4_NL 4.94 GB Download
NVIDIA-Nemotron-Nano-9B-v2_IQ4_XS.gguf GGUF IQ4_XS 4.91 GB Download
NVIDIA-Nemotron-Nano-9B-v2_Q4_0.gguf GGUF 4.94 GB Download
NVIDIA-Nemotron-Nano-9B-v2_Q4_K_M.gguf GGUF Q4_K_M 6.08 GB Download
NVIDIA-Nemotron-Nano-9B-v2_Q4_K_S.gguf GGUF Q4_K_S 5.79 GB Download
NVIDIA-Nemotron-Nano-9B-v2_Q5_0.gguf GGUF 5.91 GB Download
NVIDIA-Nemotron-Nano-9B-v2_Q5_1.gguf GGUF 6.39 GB Download
NVIDIA-Nemotron-Nano-9B-v2_Q5_K_M.gguf GGUF Q5_K_M 6.58 GB Download
NVIDIA-Nemotron-Nano-9B-v2_Q5_K_S.gguf GGUF Q5_K_S 6.32 GB Download
NVIDIA-Nemotron-Nano-9B-v2_Q6_K.gguf GGUF Q6_K 8.51 GB Download
NVIDIA-Nemotron-Nano-9B-v2_Q8_0.gguf GGUF 8.81 GB Download
NVIDIA-Nemotron-Nano-9B-v2_bf16.gguf GGUF BF16 16.57 GB Download

Model Details Live

Model Slug
tatsuyaaaaaaa/nvidia-nemotron-nano-9b-v2-gguf
Author
tatsuyaaaaaaa
Pipeline Task
Library
Created
2025-08-30
Last Modified
2026-04-01
Gated
No
Private
No
HF SHA
65de8378b7edcb610644f44c3a406b3845426260
License
Unknown
Language
en, ja
Base Model
nvidia/NVIDIA-Nemotron-Nano-9B-v2

Metadata Inspector

Normalized metadata (stored in metadata_json)
{
  "metadata": {},
  "card_data": {
    "datasets": [
      "TFMC/imatrix-dataset-for-japanese-llm"
    ],
    "language": [
      "en",
      "ja"
    ],
    "base_model": [
      "nvidia/NVIDIA-Nemotron-Nano-9B-v2"
    ],
    "frontmatter": {
      "datasets": [
        "TFMC/imatrix-dataset-for-japanese-llm"
      ],
      "language": [
        "en",
        "ja"
      ],
      "base_model": [
        "nvidia/NVIDIA-Nemotron-Nano-9B-v2"
      ]
    },
    "hero_image_url": "",
    "summary": "NVIDIAのNVIDIA-Nemotron-Nano-9B-v2をgguf変換したものです。 imatrix量子化時にはTFMC/imatrix-dataset-for-japanese-llmのデータセットを用いています。",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\ndatasets:\n- TFMC/imatrix-dataset-for-japanese-llm\nlanguage:\n- en\n- ja\nbase_model:\n- nvidia/NVIDIA-Nemotron-Nano-9B-v2\n---\n\nNVIDIAの[NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2)をgguf変換したものです。\n\nimatrix量子化時には[TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)のデータセットを用いています。",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "en",
    "ja",
    "dataset:TFMC/imatrix-dataset-for-japanese-llm",
    "base_model:nvidia/NVIDIA-Nemotron-Nano-9B-v2",
    "base_model:quantized:nvidia/NVIDIA-Nemotron-Nano-9B-v2",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 0,
  "downloads": 3874,
  "gated": false,
  "private": false,
  "last_modified": "2026-04-01T05:47:05.000Z",
  "created_at": "2025-08-30T01:00:40.000Z",
  "pipeline_tag": "",
  "library_name": ""
}
Source payload excerpt (from Hugging Face API)
{
  "_id": "68b24d387d1f62b0b7b1bf6e",
  "id": "tatsuyaaaaaaa/NVIDIA-Nemotron-Nano-9B-v2-gguf",
  "modelId": "tatsuyaaaaaaa/NVIDIA-Nemotron-Nano-9B-v2-gguf",
  "sha": "65de8378b7edcb610644f44c3a406b3845426260",
  "createdAt": "2025-08-30T01:00:40.000Z",
  "lastModified": "2026-04-01T05:47:05.000Z",
  "author": "tatsuyaaaaaaa",
  "downloads": 3874,
  "likes": 0,
  "gated": false,
  "private": false,
  "pipeline_tag": "",
  "library_name": "",
  "siblings_count": 17
}