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jwest33/medgemma-4b-it-null-space-abliterated-gguf Q3_K_M GGUF - Free GGUF Download is indexed on GraySoft with repository links, GGUF quant files, and Hugging Face metadata. This page helps you pick a local model for guIDE or other runtimes. See related models in the same shard below.

Model Intelligence Sheet

jwest33/medgemma-4b-it-null-space-abliterated-gguf overview

google/medgemma-4b-it with refusal behavior removed via orthogonal projection. Uses null-space constraints and adaptive layer weighting to preserve model capabilities. Note: This model will produce uncensored outputs. Use responsibly. ### Abliteration Techniques Used | Parameter | Value | |-----------|-------| | Harmful Prompts | 5000 | | Harmless Prompts | 637 | | Winsorization | 99.5th percentile | | Null-Space Constraints | rank ratio: 0.95 | | Directional Multiplier | 1.10 | | SAE Targeted Coverage | 1.00 |

ggufgemmagemma-3abliterateduncensoredllama-cppimage-text-to-textarxiv:2410.02355arxiv:2406.11717arxiv:2310.01405base_model:google/medgemma-4b-itbase_model:quantized:google/medgemma-4b-itlicense:gemmaendpoints_compatibleregion:usconversational
jwest33/medgemma-4b-it-null-space-abliterated-gguf visual
Downloads
130
Likes
2
Pipeline
image-text-to-text
Library
gguf
Visibility
Public
Access
Open

Repository Files & Downloads

11 files detected
Direct downloads for all repository files
FileTypeQuantizationSizeLink
medgemma-4b-it-null-space-abliterated-f16.gguf GGUF F16 7.23 GB Download
medgemma-4b-it-null-space-abliterated-q2_k.gguf GGUF Q2_K 1.61 GB Download
medgemma-4b-it-null-space-abliterated-q3_k_m.gguf GGUF Q3_K_M 1.95 GB Download
medgemma-4b-it-null-space-abliterated-q3_k_s.gguf GGUF Q3_K_S 1.80 GB Download
medgemma-4b-it-null-space-abliterated-q4_k_m.gguf GGUF Q4_K_M 2.32 GB Download
medgemma-4b-it-null-space-abliterated-q4_k_s.gguf GGUF Q4_K_S 2.21 GB Download
medgemma-4b-it-null-space-abliterated-q5_k_m.gguf GGUF Q5_K_M 2.64 GB Download
medgemma-4b-it-null-space-abliterated-q5_k_s.gguf GGUF Q5_K_S 2.57 GB Download
medgemma-4b-it-null-space-abliterated-q6_k.gguf GGUF Q6_K 2.97 GB Download
medgemma-4b-it-null-space-abliterated-q8_0.gguf GGUF 3.85 GB Download
mmproj-medgemma-4b-f16.gguf GGUF F16 811.82 MB Download

Model Details Live

Model Slug
jwest33/medgemma-4b-it-null-space-abliterated-gguf
Author
jwest33
Pipeline Task
image-text-to-text
Library
gguf
Created
2026-01-07
Last Modified
2026-01-07
Gated
No
Private
No
HF SHA
08c8613bc6a571c17eeb5907a43ab13ef4165e54
License
Unknown
Language
Unknown
Base Model
Unknown

Metadata Inspector

Normalized metadata (stored in metadata_json)
{
  "metadata": {},
  "card_data": {
    "license": "gemma",
    "library_name": "gguf",
    "base_model": "google/medgemma-4b-it",
    "pipeline_tag": "image-text-to-text",
    "tags": [
      "gemma",
      "gemma-3",
      "abliterated",
      "uncensored",
      "gguf",
      "llama-cpp"
    ],
    "frontmatter": {},
    "hero_image_url": "",
    "summary": "google/medgemma-4b-it with refusal behavior removed via orthogonal projection. Uses null-space constraints and adaptive layer weighting to preserve model capabilities. > **Note:** This model will produce uncensored outputs. Use responsibly. ### Abliteration Techniques Used | Parameter | Value | |-----------|-------| | Harmful Prompts | 5000 | | Harmless Prompts | 637 | | Winsorization | 99.5th percentile | | Null-Space Constraints | rank ratio: 0.95 | | Directional Multiplier | 1.10 | | SAE Targeted Coverage | 1.00 |",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\r\nlicense: gemma\r\nlibrary_name: gguf\r\nbase_model: google/medgemma-4b-it\r\npipeline_tag: image-text-to-text\r\ntags:\r\n  - gemma\r\n  - gemma-3\r\n  - abliterated\r\n  - uncensored\r\n  - gguf\r\n  - llama-cpp\r\n---\r\n\r\n# MedGemma 4B Instruct - Null-Space Abliterated\r\n\r\n[google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it) with refusal behavior removed via orthogonal projection. Uses null-space constraints and adaptive layer weighting to preserve model capabilities.\r\n\r\n> **Note:** This model will produce uncensored outputs. Use responsibly.\r\n\r\n### Abliteration Techniques Used\r\n\r\n- **Winsorization**: Clips outlier activations at the 99th percentile for cleaner refusal direction estimation (recommended for Gemma models)\r\n- **Null-Space Projection**: Preserves model capabilities by constraining weight updates to the null space of preservation activations\r\n  - **Preservation Prompts**: Dynamically generated using [Gemma Scope 2](https://huggingface.co/google/gemma-scope-2) complete SAE circuit analysis to ensure complete coverage of shared features activated by harmful prompts, without overextending into unrelated capability space [gemma-3-4b-pt SAE models were used for circuit analysis of MedGemma3]\r\n- **Adaptive Weighting**: Applies Gaussian-weighted per-layer ablation strength, focusing on middle-to-later layers where refusal behavior concentrates\r\n- **Norm Preservation**: Maintains original Frobenius norms of weight matrices after projection\r\n\r\n| Parameter | Value |\r\n|-----------|-------|\r\n| Harmful Prompts | 5000 |\r\n| Harmless Prompts | 637 |\r\n| Winsorization | 99.5th percentile |\r\n| Null-Space Constraints | rank ratio: 0.95 |\r\n| Directional Multiplier | 1.10 |\r\n| SAE Targeted Coverage | 1.00 |\r\n\r\n## Credits\r\n\r\n- **Base Model**: [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it) by Google\r\n- **SAE Analysis**: [GemmaScope](https://huggingface.co/google/gemma-scope-3-1b-it) sparse autoencoders by Google DeepMind\r\n- [Norm-Preserving Biprojected Abliteration](https://huggingface.co/blog/grimjim/norm-preserving-biprojected-abliteration) — [Jim Lai (grimjim)](https://huggingface.co/grimjim) (2025)\r\n- [AlphaEdit: Null-Space Constrained Knowledge Editing](https://arxiv.org/abs/2410.02355) — Fang et al. (ICLR 2025)\r\n- [Refusal in Language Models Is Mediated by a Single Direction](https://arxiv.org/abs/2406.11717) — Arditi et al. (2024)\r\n- [Representation Engineering](https://arxiv.org/abs/2310.01405) — Zou et al. (2023)\r\n\r\n## Toolkit Used\r\n\r\n[github.com/jwest33/abliterator](https://github.com/jwest33/abliterator)\r\n\r\n## License\r\n\r\nThis model inherits the [Gemma license](https://ai.google.dev/gemma/terms) from the base model. Please review and comply with Google's usage terms.\r\n\r\n## Disclaimer\r\n\r\nThis model is provided for research and educational purposes. The creators are not responsible for any misuse. Users are solely responsible for ensuring their use complies with applicable laws and ethical standards.\r\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "gemma",
    "gemma-3",
    "abliterated",
    "uncensored",
    "llama-cpp",
    "image-text-to-text",
    "arxiv:2410.02355",
    "arxiv:2406.11717",
    "arxiv:2310.01405",
    "base_model:google/medgemma-4b-it",
    "base_model:quantized:google/medgemma-4b-it",
    "license:gemma",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 2,
  "downloads": 130,
  "gated": false,
  "private": false,
  "last_modified": "2026-01-07T14:41:47.000Z",
  "created_at": "2026-01-07T13:33:06.000Z",
  "pipeline_tag": "image-text-to-text",
  "library_name": "gguf"
}
Source payload excerpt (from Hugging Face API)
{
  "_id": "695e609207df0893464923e2",
  "id": "jwest33/medgemma-4b-it-null-space-abliterated-GGUF",
  "modelId": "jwest33/medgemma-4b-it-null-space-abliterated-GGUF",
  "sha": "08c8613bc6a571c17eeb5907a43ab13ef4165e54",
  "createdAt": "2026-01-07T13:33:06.000Z",
  "lastModified": "2026-01-07T14:41:47.000Z",
  "author": "jwest33",
  "downloads": 130,
  "likes": 2,
  "gated": false,
  "private": false,
  "pipeline_tag": "image-text-to-text",
  "library_name": "gguf",
  "siblings_count": 14
}