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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 85001
This model is an experimental AI trained on three distinct datasets focusing on logical reasoning, mathematics, and programming. The training process involved fine-tuning from the last layer (31) backward with a gradually decreasing learning rate. The primary goal is to address and rectify the common 'INSTINST' bug observed in leaderboard models through targeted training on the latest layers. Each dataset contributed 20,000 data points to the training process, ensuring a balanced representation of logic, mathematics, and programming tasks.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗NeuralLLaMa-3-8b-DT-v0.1
KukedlcNeuralLLaMa-3-8b-DT-v0.1 is a merge of the following models using LazyMergekit: * mlabonne/ChimeraLlama-3-8B-v2 * nbeerbower/llama-3-stella-8B * uygarkurt/llama-3-merged-linear
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗NeuralLLaMa-3-8b-ORPO-v0.3
Kukedlc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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗NeuralLLaMa-3-8b-ORPO-v0.4
KukedlcThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗NeuralSynthesis-7B-v0.1
Kukedlc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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗NeuralSynthesis-7B-v0.3
Kukedlc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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗NeuralSynthesis-7b-v0.4-slerp is a merge of the following models using LazyMergekit: * allknowingroger/MultiverseEx26-7B-slerp * Kukedlc/NeuralSynthesis-7B-v0.1
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗LORAs
kukidevalml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。
Basic profile indexed · More details to follow
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Z-Image-Lora
kukidevalml+ Always use full LoRa name with "vrtlxxxx" trigger in prompt like "Alba Baptista (vrtlalbabaptista) in a swimming pool". "Woman" or "1girl" will NOT work due to my way i do captions. + Remove the name when internal model knowledge is bad or censored or is confusing to model like "Sandy Cheeks" or "Kate Middleton". + When using a Lora with multiple triggers (vrtlxx,vrtlyy) do not use the real character name but only trigger or a combination of it. "vrtlMain" always combines all trigger-words.
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗genpatsu-ai-8b-GGUF
kukutan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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗wav2vec2-xls-r-1b-lay41
kul-speech-lab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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗bd3lm-owt-block_size4
kuleshov-group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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗caduceus-ph_seqlen-131k_d_model-256_n_layer-16
kuleshov-groupThis is the Caduceus-Ph model with hidden dimension 256 and 16 MambaDNA layers. This model is not inherently reverse complement (RC) equivariant. Rather, it was pre-trained using RC data augmentation. Its intended usage is as follows: for downstream tasks, the model should be trained with RC data augmentation. At downstream task inference, the model should be run twice: once on a sequence and once on its RC. The output of these two applications should be combined (averaged) to form the downstream task prediction. This model was pre-trained on the human reference genome with sequence length 131,072 for 50k steps (each step contained ~1M base pairs / tokens). For more details, please see our paper: Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗caduceus-ps_seqlen-131k_d_model-256_n_layer-16
kuleshov-groupTo use the pre-trained model for masked language modeling, use the following snippet: Alternatively, you can instantiate a model from scratch to train on your own data as follows: This is the Caduceus-PS model with hidden dimension 256 and 16 MambaDNA layers. This model is reverse complement (RC) equivariant and thus no RC data augmentation is required when training this model, either during pre-training or for downstream fine-tuning. Note that the model hidden state will be **twice** that of a non-RC equivariant counterpart. For downstream task training and inference, and to ensure RC **invariant** outputs at downstream time, one can either run the downstream model on the hidden state and its RC or one can take the hidden state and its RC and average them before passing to the downstream model. To RC the hidden states, one can use: `hidden_states.flip(dim=(-2, -1))` which will flip along the sequence lenght and channel dimensions.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗mdlm-owt
kuleshov-group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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗PlantCAD2-Large-l48-d1536
kuleshov-group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。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗PlantCAD2-Medium-l48-d1024
kuleshov-group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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗PlantCAD2-Small-l24-d0768
kuleshov-group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。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗PlantCaduceus_l32
kuleshov-group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。
Basic profile indexed · More details to follow
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗BrickNet-0.6B-PT
kulits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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Illustrious-xl-early-release-v0
kulololo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。
Basic profile indexed · More details to follow
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗No matching models
Adjust provider, category or input/output filters. New models are added continuously.
COST EXPLORER
Great models. Clear costs.
Adjust monthly token usage to compare estimated API costs.
1 M = 1 million tokens · USD
Compares base text-token rates within the same channel
Compares base text-token rates within the same channel
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
MODEL HANDBOOK
Understand before you choose
How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Is the top-ranked model the best for me?+
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.