bern2-ner
dmis-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 ↗DISCOVER & COMPARE
Explore AI models 11
biobert-base-cased-v1.1
dmis-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。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗biobert-base-cased-v1.2
dmis-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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗biobert-large-cased-v1.1
dmis-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。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗biobert-v1.1
dmis-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。
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗KAZU-NER-module-distil-v1.0
dmis-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 ↗llama-3.1-medprm-reward-v1.0
dmis-lab🚀 Med-PRM-Reward is among the first Process Reward Models (PRMs) specifically designed for the medical domain. Unlike conventional PRMs, it enhances its verification capabilities by integrating clinical knowledge through retrieval-augmented generation (RAG). Med-PRM-Reward demonstrates exceptional performance in scaling-test-time computation, particularly outperforming majority‐voting ensembles on complex medical reasoning tasks. Moreover, its scalability is not limited to Llama-3.1-8B-Instruct: it delivers similarly outstanding results in scaling-test-time computation across multiple other medical‐specialized models. Notably, when combined with llama-3-meerkat-8b-v1.0, it became the first 8B model framework to surpass a score of 80 on the MedQA (4-option) benchmark. 📄 Paper: Med-PRM-Reward: Medical Reasoning Models with Stepwise, Guideline‑verified Process Rewards Across seven medical benchmarks, we conducted scaling‐test‐time computation using solutions generated by the Med-PRM-policy model, evaluating 64 solutions per question.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗llama-3-meerkat-8b-v1.0
dmis-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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗UltraBERT-150M
dmis-labUltraBERT is a family of English bidirectional encoders with a native 32K context that reach state of the art on both classification and retrieval, two task families on which previous encoders had to choose a side. UltraBERT is supported natively in `transformers >= 5.20`. On earlier 5.x versions (tested with 5.16.1), pass `trust_remote_code=True`. FlashAttention 2 is recommended for long inputs; SDPA and eager attention are also supported. We recommend starting from the settings used in the paper. For classification: AdamW (β1 0.9, β2 0.98, ε 1e-6), weight decay 0.1, a linear schedule with 5% warm-up, and a learning rate swept over {5e-6,8e-6,1e-5,3e-5,5e-5,8e-5}. For dense retrieval: mean pooling with a contrastive loss and a wider learning-rate sweep of {1e-5,3e-5,5e-5,8e-5,1e-4,2e-4,3e-4,5e-4,8e-4,1e-3}.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗UltraBERT-380M
dmis-labUltraBERT is a family of English bidirectional encoders with a native 32K context that reach state of the art on both classification and retrieval, two task families on which previous encoders had to choose a side. UltraBERT is supported natively in `transformers >= 5.20`. On earlier 5.x versions (tested with 5.16.1), pass `trust_remote_code=True`. FlashAttention 2 is recommended for long inputs; SDPA and eager attention are also supported. We recommend starting from the settings used in the paper. For classification: AdamW (β1 0.9, β2 0.98, ε 1e-6), weight decay 0.1, a linear schedule with 5% warm-up, and a learning rate swept over {5e-6,8e-6,1e-5,3e-5,5e-5,8e-5}. For dense retrieval: mean pooling with a contrastive loss and a wider learning-rate sweep of {1e-5,3e-5,5e-5,8e-5,1e-4,2e-4,3e-4,5e-4,8e-4,1e-3}.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗UltraBERT-Embed-150M
dmis-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。
Input Text→ Output Embedding
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.