Ming-flash-omni-2.0
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探索 AI 模型 84999
Qwen3.8-27B-Uncensored
JonathanColettiThese are the bf16 safetensors. For local inference under llama.cpp, see JonathanColetti/Qwen3.8-27B-Uncensored-GGUF, which ships imatrix quantizations with the multi-token prediction head retained. Try it first in the demo Space. > **The MTP head is present and verified.** Abliteration drops the `mtp.*` tensors: the > model is re-saved through transformers, which does not carry the MTP module, while > `config.json` still advertises `mtp_num_hidden_layers`. All 15 `mtp.*` tensors were > grafted back from the base checkpoint and the inventory was asserted against the source > before upload. - Refusal directions removed with Heretic, which co-minimizes refusal count against KL divergence from the base model. No hand-written refusal-removal code, no fine-tuning, no additional training data. - Abliteration runs at bf16 (no 4-bit quantization); the resulting LoRA is merged into the bf16 base, so these weights are not a quantized round trip. - Only `attn.o_proj` and `mlp.down_proj` are modified, 64 modules each. The `mtp.*` tensors are copied verbatim from the base checkpoint after merging. - 200 optimization trials. The published checkpoint is one point on the resulting Pareto front, selected for the lowest refusal count; the full front is below.
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查看详情 ↗Qwen3.8-27B-Uncensored-GGUF
JonathanColettiUncensored Qwen3.8-27B, published as GGUF quantizations with the multi token prediction (MTP) head retained and verified. Refusal behaviour has been **substantially reduced**, not eliminated. See Measured behaviour for the numbers. Capabilities, training data, and architecture are otherwise unchanged. - Method - Whats here - Overview - Files - Perplexity - Baseline against the unmodified base model - How the baseline was measured - Importance matrix - Building other low bit quants yourself - Third party quants - Usage - ComfyUI - llama cpp - Verification - Measured behaviour - How to read these - Caveats that matter - Requirements - Limitations - License - Speculative decoding, measured on this model - IQ2_M
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查看详情 ↗face-parsing
jonathandinu> ONNX model for web inference contributed by Xenova. Exhaustive list of labels can be extracted from config.json. Since p5.js uses an animation loop abstraction, we need to take care loading the model and making predictions.
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查看详情 ↗mos-mamba-18x130m-trainer-dgx-pile
jonathanjordan21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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查看详情 ↗jonawhisper-gec-t5-small-onnx
JonaWhisper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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查看详情 ↗airoboros-70b-3.3
jondurbinThis model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the `apply_chat_template` method to accurate format prompts, e.g.: BEGININPUT BEGINCONTEXT [key0: value0] [key1: value1] ... other metdata ... ENDCONTEXT [insert your text blocks here] ENDINPUT [add as many other blocks, in the exact same format] BEGININSTRUCTION [insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.] ENDINSTRUCTION BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT In a shocking turn of events, blueberries are now green, but will be sticking with the same name. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION
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查看详情 ↗airoboros-dpo-70b-3.3
jondurbinAnother experimental model, tuned primarily from synthetic data generated by airoboros, plus an additional tuning phase with various DPO datasets. This model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the `apply_chat_template` method to accurate format prompts, e.g.: BEGININPUT BEGINCONTEXT [key0: value0] [key1: value1] ... other metdata ... ENDCONTEXT [insert your text blocks here] ENDINPUT [add as many other blocks, in the exact same format] BEGININSTRUCTION [insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.] ENDINSTRUCTION
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查看详情 ↗bagel-8b-v1.0
jondurbinThis is a fine-tune of llama-3-8b using the bagel dataset, but instead of 4 prompt formats it's standardized on a single format - llama-3 instruct. There are many data sources used in the bagel models. See https://github.com/jondurbin/bagel for more information. __*Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.*__
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查看详情 ↗bagel-dpo-34b-v0.2
jondurbinThis version also includes the toxic DPO dataset, and should have less censorship than it's counterparts. You may want to use a system prompt like: 1) For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental. 2) After you created your account update your billing and navigate to the deploy page. 3) Select the following - GPU Type: A6000 - GPU Quantity: 2 - Category: Creator - Image: Jon Durbin - Coupon Code: JonDurbin 4) Deploy the VM! 5) Navigate to 'Running Instances' to retrieve instructions to login to the VM 6) Once inside the VM, open the terminal and run `volume=$PWD/data` 7) Run `model=jondurbin/bagel-dpo-34b-v0.2` 8) `sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.3 --model-id $model` 9) The model will take some time to load... 10) Once loaded the model will be available on port 8080 *Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check*
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查看详情 ↗bagel-dpo-34b-v0.5
jondurbinThis is a fine-tune of the updated yi-34b-200k with better long-context support, which underwent additional tuning via direct preference optimization (DPO). There are many data sources used in the bagel models. See https://github.com/jondurbin/bagel for more information. __*Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.*__
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查看详情 ↗electra-small-nordic
jonfdThis model was pretrained on the following corpora: * The Icelandic Gigaword Corpus (IGC) * The Icelandic Common Crawl Corpus (IC3) * The Icelandic Crawled Corpus (ICC) * The Multilingual Colossal Clean Crawled Corpus (mC4) - Icelandic, Norwegian, Swedish and Danish text obtained from .is, .no, .se and .dk domains, respectively The total size of the corpus after document-level deduplication and filtering was 14.82B tokens, split equally between the four languages. The model was trained using a WordPiece tokenizer with a vocabulary size of 96,105 for one million steps with a batch size of 256, and otherwise with default settings. This project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.
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查看详情 ↗MolGPT_pretrained-by-ZINC15
jonghyunlee1993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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查看详情 ↗Qwen3.5-35B-A3B-heretic-GGUF
Jongsim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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查看详情 ↗NSFWMaster
Jonjew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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查看详情 ↗PussyDiffusion
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。