my-qwen-lora-cisco
banhcarrot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。
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View details ↗DISCOVER & COMPARE
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Ouija2-1.7B
BansheeTechnologies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。
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View details ↗evalexplorer-classify-gguf
baobabtech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。
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View details ↗kinshield-ai-team
baopqp-qualgo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。
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View details ↗This 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).
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View details ↗FinBERT_market_based
baptle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。
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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。
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View details ↗cohere-transcribe-03-2026-CoreML-6bit
BarathwajAnandanThis is a **CoreML** conversion of CohereLabs/cohere-transcribe-03-2026 with **6-bit palettization** for on-device speech recognition on Apple Silicon (macOS / iOS). 1. Add all `.mlpackage` files to your Xcode project 2. Load `coreml_manifest.json` at runtime for tokenizer IDs and pipeline config 3. Use `MLModel` with `.computeUnits = .cpuAndGPU` for best performance 4. Process audio in **30-second chunks** with **5-second overlap** (params in manifest) The decoder runs in two phases: 1. **Full-sequence masked** decoder generates the first token from encoder output 2. **KV-cached** decoder generates subsequent tokens autoregressively
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View details ↗nemotron-3.5-asr-offline-6bit-CoreML
BarathwajAnandan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。
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View details ↗nemotron-3.5-asr-streaming320-int8-CoreML
BarathwajAnandan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。
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View details ↗Qwen3.8-27B-huihui-abliterated-groupwise-int-NInfer
Barding-DefenseA **`groupwise-int`** artifact of huihui-ai/Huihui-Qwen3.8-27B-abliterated, packaged for the NInfer engine. This is the refusal-removed counterpart to the official neroued/Qwen3.8-27B-NInfer artifact, using the same weights profile. One file carries the complete container — Text, Vision, MTP, the optimized proposal head, tokenizer, chat template, generation config and media-processor resources. > **There is also an NVFP4 build > of this same checkpoint.** It is ~35 % faster on speculative decode and 1.6–2× on prefill, for 3.1 GiB > more VRAM. **Prefer NVFP4 unless VRAM is tight.** This profile's advantages are a smaller footprint > (15.9 GiB resident vs 19.0) and a dramatically simpler build — one command, no external quantizer, > no calibration data, and no patch of any kind.
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View details ↗Qwen3.8-27B-huihui-abliterated-NVFP4-NInfer
Barding-DefenseAn **NVFP4** artifact of huihui-ai/Huihui-Qwen3.8-27B-abliterated, packaged for the NInfer engine. This is the refusal-removed (abliterated) counterpart to the official neroued/Qwen3.8-27B-NInfer artifact. It carries the complete container — Text, Vision, MTP, the optimized proposal head, tokenizer, chat template, generation config and media-processor resources — as one file. Qwen3.8-27B is the king of the local models right now, and the great work Neroued has done with the NInfer engine runs it extremely quickly on Blackwell. There has also been a lot of work on obliterating and otherwise liberating models, including Pliny's OBLITERATUS suite.
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View details ↗Qwen3.8-27B-OBLITERATED-NVFP4-NInfer
Barding-Defense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。
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View details ↗> **A newer, better version is available: bardsai/eu-pii-anonimization-multilang-v2.** > On real web pages, scrapes, PDFs and invoices its strict F1 is 0.973 vs 0.885 for this model, and it is better on every public benchmark we tested (Gretel 0.692 vs 0.615, Nemotron-PII 0.933 vs 0.882, OpenPII 0.997 vs 0.928). Same label set; ONNX and INT8 weights included. New projects should use v2. A multilingual PII detector built for EU compliance reality: 24 official languages, GDPR special categories, AI Act high-risk data - in one model. GDPR and the AI Act require you to detect and redact personal data across every language your users write in. Most open-source PII models were trained on English with a few translations bolted on, and they cover the basics — names, emails, phone numbers — while missing exactly the categories regulators care about: biometric data, genetic data, health information, political opinions, ethnic origin.
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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。
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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。
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View details ↗finance-sentiment-de-base
bardsai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。
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View details ↗finance-sentiment-fr-base
bardsai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。
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View details ↗finance-sentiment-ja-base
bardsai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。
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View details ↗finance-sentiment-zh-base
bardsai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。
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View details ↗ComfyUI-MiniMax-H3-FastVideo
barelyminingTwo files that let ComfyUI run **MiniMax H3 video generation in 4 steps** using FastVideo's Video Sparse Attention. On a 24 GB RTX 3090 Ti, this drops a 5-second, 0.8 MP clip from ~8 minutes (base, 20 steps) to under 1 minute (draft) or ~2.5 minutes (best quality). Requires the companion custom node — you can't load these with a normal LoRA loader alone. > **Most people should probably use one of the established H3 turbo LoRAs** (e.g. Comfy-Org's official 4-step turbo, lightx2v's 8-step turbo) — they're simpler to set up and don't need a custom node. This project exists for people who want to experiment with FastVideo's sparse-attention approach and see if the speed/quality tradeoff works better for their use case, or for people on older cards (RTX 3090 / 4090) who can't run FastVideo's latest official releases that target Hopper/Blackwell GPUs. Compare and pick what you like. > ⚠️ **Experimental.** Tested on one machine (RTX 3090 Ti). Requires the companion custom node — this is not a drop-in for standard LoRA loading. See the node repo before downloading.
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View details ↗Farsi-to-English
barghavani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。
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View details ↗whisper-turbo-quran
baristiran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。
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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。
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