exp4_averaged
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从语言到影像,从开源到 API。读懂模型能力、比较真实成本,
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This model is a text classification model based on Deberta-v3 that predicts whether a text contains text that could be considered offensive. It is split up in the following labels: It's important to remember that this model was only trained on English texts, and may not perform well on non-English inputs. This is a model that deals with sensitive and potentially harmful language. Users should consider the ethical implications and potential risks of using or deploying this model in their applications or contexts. Some of the ethical issues that may arise are:
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> **Ungated mirror of black-forest-labs/flux-3-image-klein-comfy.** > All credit to Black Forest Labs — this is not an official Black Forest Labs > release and is not endorsed by them. The weights are redistributed under the > FLUX Kommunity License, included here as `LICENSE.md` together > with `NOTICE.md`; any rights to use them are granted directly by > Black Forest Labs under that licence. > > **What this mirror carries** — the int8 / w4a8 builds the ComfyUI templates and > the SAUS apps use: > > | path | size | what it is | > | --- | --- | --- | > | `diffusion_models/flux3_image_klein_base_int8_convrot.safetensors` | 5.81 GB | Klein Base, undistilled — 50 steps, guidance 3.5 | > | `diffusion_models/flux3_image_klein_sd_int8_convrot.safetensors` | 5.83 GB | Klein distilled — 16 steps, no guidance | > | `text_encoders/qwen3vl_4b_int8_convrot.safetensors` | 4.86 GB | Qwen3-VL-4B-Instruct text encoder | > | `text_encoders/qwen3.5_9b_flux3_prompt_upsampler_w4a8.safetensors` | 6.72 GB | prompt upsampler | > | `vae/flux2-vae.safetensors` | 336 MB | image autoencoder | > > **Installing into ComfyUI:** copy (or symlink) `diffusion_models/` and > `text_encoders/` into `ComfyUI/models/`, and `vae/flux2-vae.safetensors` into > `ComfyUI/models/vae/`. The sub-folder layout above deliberately matches the > upstream repository, so a plain directory copy is enough. FLUX 3 Image Klein is a 5.6B open weights model for text-to-image generation and image editing. It takes a prompt and up to 10 reference images and returns one image. Text-to-image, single-reference edits and multi-reference compositions run through the same model, with Qwen3-VL-4B-Instruct as the text encoder. The 5.6B parameter label refers to the image transformer; the text encoder is separate.
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This is the GGUF version of the model meant for use in KoboldCpp, check the Float16 version for the original. Tiefighter is a merged model achieved trough merging two different lora's on top of a well established existing merge. To achieve this the following recipe was used: This means this model contains the following ingredients from their upstream models for as far as we can track them: - Undi95/Xwin-MLewd-13B-V0.2 - - Undi95/ReMM-S-Light - Undi95/CreativeEngine - Brouz/Slerpeno - - elinas/chronos-13b-v2 - jondurbin/airoboros-l2-13b-2.1 - NousResearch/Nous-Hermes-Llama2-13b+nRuaif/Kimiko-v2 - CalderaAI/13B-Legerdemain-L2+lemonilia/limarp-llama2-v2 - - KoboldAI/LLAMA2-13B-Holodeck-1 - NousResearch/Nous-Hermes-13b - OpenAssistant/llama2-13b-orca-8k-3319 - ehartford/WizardLM-1.0-Uncensored-Llama2-13b - Henk717/spring-dragon - The-Face-Of-Goonery/Huginn-v3-13b (Contains undisclosed model versions, those we assumed where possible) - - SuperCOT (Undisclosed version) - elinas/chronos-13b-v2 (Version assumed) - NousResearch/Nous-Hermes-Llama2-13b - stabilityai/StableBeluga-13B (Version assumed) - zattio770/120-Days-of-LORA-v2-13B - PygmalionAI/pygmalion-2-13b - Undi95/Storytelling-v1-13B-lora - TokenBender/sakhi_13B_roleplayer_NSFW_chat_adapter - nRuaif/Kimiko-v2-13B - The-Face-Of-Goonery/Huginn-13b-FP16 - - "a lot of different models, like hermes, beluga, airoboros, chronos.. limarp" - lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT - Xwin-LM/Xwin-LM-13B-V0.2 - PocketDoc/Dans-RetroRodeo-13b - Blackroot/Llama-2-13B-Storywriter-LORA While we could possibly not credit every single lora or model involved in this merged model, we'd like to thank all involved creators upstream for making this awesome model possible! Thanks to you the AI ecosystem is thriving, and without your dedicated tuning efforts models such as this one would not be possible.
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可以调整厂商、分类或输入输出条件;新模型正持续收录。
调整月度 Token 用量,直观比较不同模型的预估 API 开支。
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。
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。
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。
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。