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AI 모델 84760
- Teuken-7B-instruct-research-v0.4↗
openGPT-X
- OpenMed-ZeroShot-NER-DNA-Base-220M↗
OpenMed
**Specialized model for Biomedical Entity Recognition - Proteins, DNA, RNA, cell lines, and cell types** Targets **molecular biology entities**: proteins, DNA/RNA, cell lines, and cell types in biomedical research content.Great for **pathway curation**, **molecular interaction mining**, and **omics-aware information extraction**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.
- tiny-random-granitemoehybrid↗
optimum-intel-internal-testing
- Hemmingway-1-Uncensored-Heretic-GGUF↗
OS-Software
- PP-LCNet_x1_0_textline_ori↗
PaddlePaddle
The text line orientation classification module primarily distinguishes the orientation of text lines and corrects them using post-processing. In processes such as document scanning and license/certificate photography, to capture clearer images, the capture device may be rotated, resulting in text lines in various orientations. Standard OCR pipelines cannot handle such data well. By utilizing image classification technology, the orientation of text lines can be predetermined and adjusted, thereby enhancing the accuracy of OCR processing. The key accuracy metrics are as follow: PP-LCNet_x1_0_textline_ori 98.85 0.96 Text line classification model based on PP-LCNet_x0_25, with two classes: 0 degrees and 180 degrees For details about PaddlePaddle installation, please refer to the PaddlePaddle official website.
- Dirk-Qwen3.8-27B-GGUF↗
peculiar-ragdoll
With our **Sharp chat template, MTP, and vision baked in**, the model answers lean and stays on-task out of the box. No template wrangling: download, point llama.cpp at it, go. If you want it to think deeper, set the effort level through `chat_template_kwargs`: Levels: `low`, `medium`, `xhigh` — `high` is accepted but is an alias for `xhigh`, not a step below it. Omit it for Dirk's lean default (medium). Turn thinking off entirely with `"enable_thinking": false`. - **Base:** `Qwen/Qwen3.8-27B`, a dense 27B **vision-language** model (vision preserved). - **Quant:** two quantizers, each where it is strongest. From 3 bpw up, Unsloth's **Dynamic 3.0** **UD** quants — their current generation, not an older ladder. Below that, **GSQ-RCO** quants from IST-DASLab, which hold up substantially better at 2–3 bpw (see the note under the file table). Every tier **keeps the model's MTP (`nextn`) head**: runtimes with multi-token-prediction speculative decoding can use it for faster generation. - **Template:** the Sharp chat template (Qwen 3.8-aware) — froggeric's fixed Qwen template plus a terseness system prompt, and turning off the xhigh thinking default. Every tier supports the terseness opt-out (`chat_template_kwargs: {"terse": false}`). The `GSQ-RCO-` tiers carry `v22.4.1`, which also stands down when the runtime injects its own tool protocol (an LM Studio fix); the `UD-` tiers carry `v22.4.0` and are otherwise identical — they pick up `v22.4.1` on the next pass. It is byte-swapped into the GGUF metadata; the weights and the MTP tensors are untouched.
- Qwen3.8-27B-Whitehat-FP8↗
QuaduxIT
- Gemma-2-Ataraxy-Doppel-9B-GGUF↗
QuantFactory
- Llama-3-Ko-Luxia-Instruct-GGUF↗
QuantFactory
- TouchNight-Ministral-8B-Instruct-2410-HF-GGUF↗
QuantFactory