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AI モデル 85003

• Llama-2-PeanutButter_v19_R8-7B-GGUF↗

  TheBloke

• DeepSeek-OCR-2↗

  TitanML

• Qwen3.8-27B-NVFP4↗

  unsloth

  > [!Note] > This model works with vLLM. It also works with SGLang, but only with the v0.5.19, because the `lm_head` is FP8 rather than 4-bit. This NVFP4 quant uses Unsloth Dynamic V3.0 (preview) for SOTA quantization performance. Developer Role Support so Qwen3.8 can work in agentic tools like Codex and more! MTP for fast inference is available. Qwen3.8 can now be run and fine-tuned in Unsloth Desktop . Read our guide . Tool calling improvements: Makes parsing nested objects to make tool calling succeed more. See below for 4-bit Qwen3.8-27B run inside of Unsloth Desktop:

• bio-clinical-bert-onnx↗

  valiancehealth

• Kolibri-1-MLX-3bit↗

  velaia

• QVikhr-2.5-1.5B-Instruct-SMPO_GGUF↗

  Vikhrmodels

• Qwen3-4B-toolcall-GGUF↗

  wesjos

• whisper-medium.en↗

  Xenova

• RadBERT-RoBERTa-4m↗

  zzxslp

  This is one variant of our RadBERT models trained with 4 million deidentified medical reports from US VA hospital, which achieves stronger medical language understanding performance than previous medical domain models such as BioBERT, Clinical-BERT, BLUE-BERT and BioMed-RoBERTa. Performances are evaluated on three tasks: (a) abnormal sentence classification: sentence classification in radiology reports as reporting abnormal or normal findings; (b) report coding: Assign a diagnostic code to a given radiology report for five different coding systems; (c) report summarization: given the findings section of a radiology report, extractively select key sentences that summarized the findings. For details, check out the paper here: RadBERT: Adapting transformer-based language models to radiology

• Qwen-Image-2.1-viggle-turbo-5x↗

  0xSojalSec