mradermacher/f2llm-v2-14b-gguf Q3_K_L 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/f2llm-v2-14b-gguf overview
About static quants of https://huggingface.co/codefuse-ai/F2LLM-v2-14B For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
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0
Pipeline
—
Library
transformers
Visibility
Public
Access
Open
Repository Files & Downloads
11 files detected
Direct downloads for all repository files
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| F2LLM-v2-14B.IQ4_XS.gguf | GGUF | IQ4_XS | 7.23 GB | Download |
| F2LLM-v2-14B.Q2_K.gguf | GGUF | Q2_K | 5.12 GB | Download |
| F2LLM-v2-14B.Q3_K_L.gguf | GGUF | Q3_K_L | 7.05 GB | Download |
| F2LLM-v2-14B.Q3_K_M.gguf | GGUF | Q3_K_M | 6.51 GB | Download |
| F2LLM-v2-14B.Q3_K_S.gguf | GGUF | Q3_K_S | 5.89 GB | Download |
| F2LLM-v2-14B.Q4_K_M.gguf | GGUF | Q4_K_M | 7.98 GB | Download |
| F2LLM-v2-14B.Q4_K_S.gguf | GGUF | Q4_K_S | 7.58 GB | Download |
| F2LLM-v2-14B.Q5_K_M.gguf | GGUF | Q5_K_M | 9.29 GB | Download |
| F2LLM-v2-14B.Q5_K_S.gguf | GGUF | Q5_K_S | 9.06 GB | Download |
| F2LLM-v2-14B.Q6_K.gguf | GGUF | Q6_K | 10.70 GB | Download |
| F2LLM-v2-14B.Q8_0.gguf | GGUF | — | 13.85 GB | Download |
Model Details Live
Metadata Inspector
Normalized metadata (stored in metadata_json)
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"summary": "## About static quants of https://huggingface.co/codefuse-ai/F2LLM-v2-14B ***For a convenient overview and download list, visit our model page for this model.*** weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.",
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"readme_markdown": "---\nbase_model: codefuse-ai/F2LLM-v2-14B\ndatasets:\n- codefuse-ai/F2LLM-v2\nlanguage:\n- en\n- zh\n- ru\n- es\n- fr\n- de\n- ar\n- nl\n- vi\n- hi\n- ko\n- ja\n- it\n- id\n- pt\n- pl\n- tr\n- da\n- th\n- sv\n- fa\n- uk\n- cs\n- no\n- el\n- ca\n- ro\n- fi\n- bg\n- tl\n- gl\n- my\n- hy\n- km\n- ne\n- hu\n- eu\n- he\n- lo\n- sw\n- az\n- lv\n- si\n- sk\n- tg\n- et\n- lt\n- ms\n- hr\n- is\n- sl\n- sr\n- ur\n- bn\n- af\n- ta\n- ka\n- te\n- ml\n- mn\n- nn\n- kk\n- cy\n- mr\n- sq\n- nb\n- mk\n- jv\n- kn\n- eo\n- la\n- gu\n- uz\n- am\n- oc\n- be\n- mg\n- vo\n- pa\n- lb\n- ht\n- br\n- ga\n- xh\n- tt\n- bs\n- yo\nlibrary_name: transformers\nlicense: apache-2.0\nmradermacher:\n readme_rev: 1\nquantized_by: mradermacher\ntags:\n- sentence-transformers\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/codefuse-ai/F2LLM-v2-14B\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#F2LLM-v2-14B-GGUF).***\n\nweighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.\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/F2LLM-v2-14B-GGUF/resolve/main/F2LLM-v2-14B.Q2_K.gguf) | Q2_K | 5.6 | |\n| [GGUF](https://huggingface.co/mradermacher/F2LLM-v2-14B-GGUF/resolve/main/F2LLM-v2-14B.Q3_K_S.gguf) | Q3_K_S | 6.4 | |\n| [GGUF](https://huggingface.co/mradermacher/F2LLM-v2-14B-GGUF/resolve/main/F2LLM-v2-14B.Q3_K_M.gguf) | Q3_K_M | 7.1 | lower quality |\n| [GGUF](https://huggingface.co/mradermacher/F2LLM-v2-14B-GGUF/resolve/main/F2LLM-v2-14B.Q3_K_L.gguf) | Q3_K_L | 7.7 | |\n| [GGUF](https://huggingface.co/mradermacher/F2LLM-v2-14B-GGUF/resolve/main/F2LLM-v2-14B.IQ4_XS.gguf) | IQ4_XS | 7.9 | |\n| [GGUF](https://huggingface.co/mradermacher/F2LLM-v2-14B-GGUF/resolve/main/F2LLM-v2-14B.Q4_K_S.gguf) | Q4_K_S | 8.2 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/F2LLM-v2-14B-GGUF/resolve/main/F2LLM-v2-14B.Q4_K_M.gguf) | Q4_K_M | 8.7 | fast, recommended |\n| [GGUF](https://huggingface.co/mradermacher/F2LLM-v2-14B-GGUF/resolve/main/F2LLM-v2-14B.Q5_K_S.gguf) | Q5_K_S | 9.8 | |\n| [GGUF](https://huggingface.co/mradermacher/F2LLM-v2-14B-GGUF/resolve/main/F2LLM-v2-14B.Q5_K_M.gguf) | Q5_K_M | 10.1 | |\n| [GGUF](https://huggingface.co/mradermacher/F2LLM-v2-14B-GGUF/resolve/main/F2LLM-v2-14B.Q6_K.gguf) | Q6_K | 11.6 | very good quality |\n| [GGUF](https://huggingface.co/mradermacher/F2LLM-v2-14B-GGUF/resolve/main/F2LLM-v2-14B.Q8_0.gguf) | Q8_0 | 15.0 | 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",
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Source payload excerpt (from Hugging Face API)
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