llama1-3b
Elchikibaby基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DISCOVER & COMPARE
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Winnow-12B
EldanRingWinnow-12B is an EldanRing fine-tune of Gemma 4 12B IT for local typed decisions, chat, and image input. Give it a shared state and questions with known answer options; the Winnow server scores those options through `/v1/systemone` without generating an explanation. Ordinary chat uses `/v1/chat/completions` from the same loaded model. The GGUF downloads contain the merged fine-tune. No separate adapter or base-model download is needed. Winnow Q8 and Jev each answered **198 of 231** JevBench questions correctly on these frozen inputs. JevBench reports public-subset accuracy rather than the composite leaderboard score. These panels were used during development. The BF16 results use an earlier GGUF export; see export provenance and the evaluation report.
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View details ↗Winnow-E2B
EldanRing基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Winnow-E4B
EldanRingWinnow-E4B is a compact EldanRing fine-tune of Gemma 4 E4B IT for typed decisions, chat, and image input. Give it a shared state and questions with known answer options; it scores the candidates without generating an explanation. Typical uses include routing requests, choosing actions, checking conditions, and rating urgency. The GGUF downloads contain the merged fine-tune. No separate adapter or base-model download is needed. Image input uses the matching vision projector. These published Q8 results compare the models on the same questions. Jev and Kev report top-choice accuracy; Typed reports agreement with synthetic teacher labels.
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗alephn-1
elebush基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗deepseek
electricpipelines基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3-4B-GGUF
electricpipelinesQwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: **Qwen3-4B** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 4.0B - Number of Paramaters (Non-Embedding): 3.6B - Number of Layers: 36 - Number of Attention Heads (GQA): 32 for Q and 8 for KV - Context Length: 32,768 natively and 131,072 tokens with YaRN. For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.
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View details ↗berty-phaseB
electroglyph基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗berty-phaseC
electroglyph基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗flint-uncensored
electroglyph基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗LFM2.5-24B-A2B-uncensored-GGUF
electroglyph基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3-4B-Instruct-2507-uncensored-unslop-v2
electroglyph基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗graig-alpha
electron271基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗graig-experiment-3
electron271基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗llama-3.2-1b-uncensored
ElementMerc基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗llama-3.2-1b-uncensored-GGUF
ElementMerc基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗qwen2.5-1.5b-uncensored
ElementMerc基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗qwen2.5-1.5b-uncensored-GGUF
ElementMerc> ## Correction notice, 2026-10-01 > > **The evaluation numbers for this model are withdrawn.** They were never printed on this > card; they live on the parent repository's card, and every one of them is withdrawn > there, with the reasons. The GGUF files themselves are unchanged and are not withdrawn. > > Read the notice on qwen2.5-1.5b-uncensored before quoting > any figure for this model. The parent card carries what this model is, how it was made, what abliteration did to it, and the evaluation numbers. **Read it before using these weights**: this model does not refuse, which is the entire point of it and the thing to understand before downloading. Both were converted from the parent's safetensors with `convert_hf_to_gguf.py` and quantised with `llama-quantize`. Each file was loaded and asked to generate before publication, because a GGUF that converts but does not run is exactly the kind of thing that ships broken.
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View details ↗qwen3-0.6b-uncensored
ElementMerc基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗qwen3-1.7b-uncensored
ElementMerc基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗qwen3-1.7b-uncensored-GGUF
ElementMerc基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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