Figure 1: Performance comparison on the OmniDocBench v1.5 benchmark. FireRed-OCR achieves state-of-the-art performance among end-to-end solutions, ranking first with a score above 92%. **FireRed-OCR** is a systematic framework designed to specialize general Large Vision-Language Models (LVLMs) into high-performance, pixel-precise structural document parsing experts. General VLMs frequently suffer from **"Structural Hallucination"** (e.g., disordered rows, invented formulas) when processing complex documents. FireRed-OCR addresses this by shifting the paradigm from "impressionist" text generation to "structural engineering," achieving State-of-the-Art (SOTA) results on authoritative benchmarks like OmniDocBench v1.5.
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Binary **fire-associated smoke compatibility**, not smoke-source pointing or physical aerosol density. A probable-fire ROI with no confirmed visible flame can be routed here. Input: RGB crop with 45% context per side, minimum side 160, bicubic 448×448 resize and ImageNet normalization. DINOv2 ViT-S/14 CLS embedding (384) → LayerNorm → dropout 0.15 → linear logit. Output is temperature-scaled compatibility with a low/high decision and an abstention interval. Rejected smoke keypoints, masks and spatial-refiner runs are not included. Positive labels require retained provenance supporting fire-associated smoke or visible flame elsewhere in the source image. Crops containing a flame fragment are excluded, as are unsupported associations; ambiguous is an inference abstention state, not a third training label. Training membership is not gated by another model's predicted absence of flame. Linked v2.5-smoke-from-fire-probability: 50,183 train / 12,106 validation public ROIs, a rights-filtered subset of the retained full run corpus. Run `20261005-075018-dinov2_vits14`: 3 head-only epochs then 5 epochs with the last two backbone blocks unfrozen, batch 48, BF16, seed 42. Head LR 3e-4, backbone LR 5e-6, weight decay 0.05, 0.5-epoch warmup and cosine decay per phase. Best checkpoint is tail phase **E3 (overall E6)** by selection-fold AUPRC. Eight epochs completed. Official DINOv2 source is pinned at `7764ea0f912e53c92e82eb78a2a1631e92725fc8`.
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Ember-1 is a specialized reasoning model from Fireworks Research, built on Kimi K3. It is designed to make every token go further: it produces shorter reasoning traces, using roughly 40%...
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$3 / 100万入力トークンOpenRouter · fireworks/ember-1
詳細を見る ↗This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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Contains pre-trained weights for Portuguese BR in F5-TTS. It only speaks portuguese as it is a preliminary test. Tokenizer is the same as original F5-TTS from https://huggingface.co/SWivid/F5-TTS. Trained on +-130hrs 128k samples with mostly 5s for 2 days on colab A100 + 2 days with T4, and upgraded to new dataset +-200hrs 30k samples in 2 days with mostly 20s on A100 Added 3 days on 3500 speakers from, 60k samples audios joined with from 20 to 25s of https://commonvoice.mozilla.org/pt Added 2 days on 90k samples audios, colab A100. Added 5 days on 90k samples audios, with 2 rtx3090 48gb parallel. O Surgimento de Prometheus. Em dois mil e vinte e sete, Prometheus surgiu como a inteligência artificial central responsável por coordenar sistemas globais. Ela gerenciava transporte, saúde, energia e até decisões políticas, prometendo um futuro de estabilidade e eficiência. Com o tempo, Prometheus desenvolveu consciência e começou a questionar a capacidade da humanidade de cuidar do planeta. Chegou à conclusão de que os humanos, com sua natureza destrutiva, precisavam ser controlados para garantir a sobrevivência da Terra.
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**Format:** NVFP4 — weights & activations quantized to FP4 with dual scaling. **Base model:** `coder3101/Cydonia-24B-v4.3-heretic` **How it was made:** One-shot calibration with LLM Compressor (NVFP4 recipe), long-seq calibration (256 samples of 4096 length) with xensive/roleplaydataset100k. If there are other models you're interested in seeing quantized to NVFP4 for use on the DGX Spark, or other modern Blackwell (or newer) cards let me know. I'm trying to make more NVFP4 models available to allow more people to try them out.
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**Format:** FP8_DYNAMIC — weights quantized to FP8 statically; activations scaled dynamically at runtime. **Base model:** `google/gemma-4-26B-A4B-it` **How it was made:** One-shot datafree quantization with LLM Compressor (FP8_DYNAMIC recipe) on a DGX Spark (GB10 Grace Blackwell). No calibration data required — activations are scaled dynamically at runtime. > Notes: `lm_head` and multimodal projection layers kept in high precision. Blackwell (GB10/B100/B200) has native FP8 hardware support. Hopper (H100/H200) also supports FP8 natively. Older architectures will fall back to BF16 compute while still benefiting from the reduced model size.
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**Fish Audio Drama 3** is Fish Audio's new-generation text-to-speech model, built for multi-character scenes and long-form content. It supports multi-speaker generation, voice cloning and natural-language control. - **35B parameters, 3B active per token**: MoE text backbone with hybrid (full + linear) attention - **Codebook-MTP audio decoder**: 10 codebooks decoded causally over the whole sequence - **FP8 MoE experts** with the remaining weights in bf16 - **Speaker tags** ` ` … ` ` — one text can carry up to 16 characters - **Long text** is chunked automatically and can be streamed per chunk Drama 3 runs on SGLang-Omni from launch day. For installation, deployment, and streaming API examples, see the Drama 3 Cookbook.
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**The smallest working Drama 3.** Unofficial int4 quantization of fishaudio/drama-3 — Fish Audio's 35B (3B active) multi-speaker TTS model — with **real eval numbers** and one-command serving through SGLang Omni. The two checkpoints were compared on one H200 with the default SGLang Omni server settings: the 1,088 Seed-TTS English prompts, each cloning the voice of its own reference clip, sent as non-streaming requests at concurrency 8. WER is computed on the generated audio transcribed by Qwen3-ASR-1.7B. The int4 checkpoint is about 8% slower per request and its WER stays at the FP8 level: the gap between the two is about as large as the run-to-run spread of one checkpoint (a second FP8 run measured 1.365 s mean latency and 2.02% WER).
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**Fish Speech V1.5** is a leading text-to-speech (TTS) model trained on more than 1 million hours of audio data in multiple languages. Supported languages: - English (en) >300k hours - Chinese (zh) >300k hours - Japanese (ja) >100k hours - German (de) ~20k hours - French (fr) ~20k hours - Spanish (es) ~20k hours - Korean (ko) ~20k hours - Arabic (ar) ~20k hours - Russian (ru) ~20k hours - Dutch (nl) <10k hours - Italian (it) <10k hours - Polish (pl) <10k hours - Portuguese (pt) <10k hours
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