mradermacher/huihui-glm-4.7-flash-abliterated-gguf Q5_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
mradermacher/huihui-glm-4.7-flash-abliterated-gguf overview
About static quants of https://huggingface.co/huihui-ai/Huihui-GLM-4.7-Flash-abliterated For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-i1-GGUF
Downloads
5,213
Likes
16
Pipeline
—
Library
transformers
Visibility
Public
Access
Open
Repository Files & Downloads
11 files detected
Direct downloads for all repository files
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| Huihui-GLM-4.7-Flash-abliterated.IQ4_XS.gguf | GGUF | IQ4_XS | 15.05 GB | Download |
| Huihui-GLM-4.7-Flash-abliterated.Q2_K.gguf | GGUF | Q2_K | 10.28 GB | Download |
| Huihui-GLM-4.7-Flash-abliterated.Q3_K_L.gguf | GGUF | Q3_K_L | 14.52 GB | Download |
| Huihui-GLM-4.7-Flash-abliterated.Q3_K_M.gguf | GGUF | Q3_K_M | 13.39 GB | Download |
| Huihui-GLM-4.7-Flash-abliterated.Q3_K_S.gguf | GGUF | Q3_K_S | 12.14 GB | Download |
| Huihui-GLM-4.7-Flash-abliterated.Q4_K_M.gguf | GGUF | Q4_K_M | 16.89 GB | Download |
| Huihui-GLM-4.7-Flash-abliterated.Q4_K_S.gguf | GGUF | Q4_K_S | 15.90 GB | Download |
| Huihui-GLM-4.7-Flash-abliterated.Q5_K_M.gguf | GGUF | Q5_K_M | 19.80 GB | Download |
| Huihui-GLM-4.7-Flash-abliterated.Q5_K_S.gguf | GGUF | Q5_K_S | 19.25 GB | Download |
| Huihui-GLM-4.7-Flash-abliterated.Q6_K.gguf | GGUF | Q6_K | 22.92 GB | Download |
| Huihui-GLM-4.7-Flash-abliterated.Q8_0.gguf | GGUF | — | 29.66 GB | Download |
Model Details Live
Metadata Inspector
Normalized metadata (stored in metadata_json)
{
"metadata": {},
"card_data": {
"base_model": "huihui-ai/Huihui-GLM-4.7-Flash-abliterated",
"language": [
"en",
"zh"
],
"library_name": "transformers",
"license": "mit",
"mradermacher": {
"readme_rev": 1
},
"quantized_by": "mradermacher",
"tags": [
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"uncensored"
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"frontmatter": {
"base_model": "huihui-ai/Huihui-GLM-4.7-Flash-abliterated",
"language": [
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"library_name": "transformers",
"license": "mit",
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"tags": [
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},
"hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
"summary": "## About static quants of https://huggingface.co/huihui-ai/Huihui-GLM-4.7-Flash-abliterated ***For a convenient overview and download list, visit our model page for this model.*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-i1-GGUF",
"quick_links": [],
"benchmark_table_html": "",
"readme_markdown": "---\nbase_model: huihui-ai/Huihui-GLM-4.7-Flash-abliterated\nlanguage:\n- en\n- zh\nlibrary_name: transformers\nlicense: mit\nmradermacher:\n readme_rev: 1\nquantized_by: mradermacher\ntags:\n- abliterated\n- uncensored\n---\n## About\n\n<!-- ### quantize_version: 2 -->\n<!-- ### output_tensor_quantised: 1 -->\n<!-- ### convert_type: hf -->\n<!-- ### vocab_type: -->\n<!-- ### tags: -->\n<!-- ### 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 -->\n<!-- ### quants_skip: -->\n<!-- ### skip_mmproj: -->\nstatic quants of https://huggingface.co/huihui-ai/Huihui-GLM-4.7-Flash-abliterated\n\n<!-- provided-files -->\n\n***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Huihui-GLM-4.7-Flash-abliterated-GGUF).***\n\nweighted/imatrix quants are available at https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-i1-GGUF\n## Usage\n\nIf you are unsure how to use GGUF files, refer to one of [TheBloke's\nREADMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for\nmore details, including on how to concatenate multi-part files.\n\n## Provided Quants\n\n(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)\n\n| Link | Type | Size/GB | Notes |\n|:-----|:-----|--------:|:------|\n| [GGUF](https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-GGUF/resolve/main/Huihui-GLM-4.7-Flash-abliterated.Q2_K.gguf) | Q2_K | 11.1 | |\n| [GGUF](https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-GGUF/resolve/main/Huihui-GLM-4.7-Flash-abliterated.Q3_K_S.gguf) | Q3_K_S | 13.1 | |\n| [GGUF](https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-GGUF/resolve/main/Huihui-GLM-4.7-Flash-abliterated.Q3_K_M.gguf) | Q3_K_M | 14.5 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-GGUF/resolve/main/Huihui-GLM-4.7-Flash-abliterated.Q3_K_L.gguf) | Q3_K_L | 15.7 | |\n| [GGUF](https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-GGUF/resolve/main/Huihui-GLM-4.7-Flash-abliterated.IQ4_XS.gguf) | IQ4_XS | 16.3 | |\n| [GGUF](https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-GGUF/resolve/main/Huihui-GLM-4.7-Flash-abliterated.Q4_K_S.gguf) | Q4_K_S | 17.2 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-GGUF/resolve/main/Huihui-GLM-4.7-Flash-abliterated.Q4_K_M.gguf) | Q4_K_M | 18.2 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-GGUF/resolve/main/Huihui-GLM-4.7-Flash-abliterated.Q5_K_S.gguf) | Q5_K_S | 20.8 | |\n| [GGUF](https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-GGUF/resolve/main/Huihui-GLM-4.7-Flash-abliterated.Q5_K_M.gguf) | Q5_K_M | 21.4 | |\n| [GGUF](https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-GGUF/resolve/main/Huihui-GLM-4.7-Flash-abliterated.Q6_K.gguf) | Q6_K | 24.7 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-GGUF/resolve/main/Huihui-GLM-4.7-Flash-abliterated.Q8_0.gguf) | Q8_0 | 31.9 | fast, best quality |\n\nHere is a handy graph by ikawrakow comparing some lower-quality quant\ntypes (lower is better):\n\n\n\nAnd here are Artefact2's thoughts on the matter:\nhttps://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9\n\n## FAQ / Model Request\n\nSee https://huggingface.co/mradermacher/model_requests for some answers to\nquestions you might have and/or if you want some other model quantized.\n\n## Thanks\n\nI thank my company, [nethype GmbH](https://www.nethype.de/), for letting\nme use its servers and providing upgrades to my workstation to enable\nthis work in my free time.\n\n<!-- end -->\n",
"related_quantizations": []
},
"tags": [
"transformers",
"gguf",
"abliterated",
"uncensored",
"en",
"zh",
"base_model:huihui-ai/Huihui-GLM-4.7-Flash-abliterated",
"base_model:quantized:huihui-ai/Huihui-GLM-4.7-Flash-abliterated",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
],
"likes": 16,
"downloads": 5213,
"gated": false,
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"last_modified": "2026-01-22T21:56:01.000Z",
"created_at": "2026-01-22T18:05:16.000Z",
"pipeline_tag": "",
"library_name": "transformers"
}
Source payload excerpt (from Hugging Face API)
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