opus-mt-uk-ru
Helsinki-NLP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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opus-mt-ur-en
Helsinki-NLP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 ↗opus-mt-vi-en
Helsinki-NLP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 ↗opus-mt-zh-en
Helsinki-NLP- Model Details - Uses - Risks, Limitations and Biases - Training - Evaluation - Citation Information - How to Get Started With the Model - **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation - **Language(s):** - Source Language: Chinese - Target Language: English - **License:** CC-BY-4.0 - **Resources for more information:** - GitHub Repo **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
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View details ↗opus-mt-zh-vi
Helsinki-NLP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 ↗opus-tatoeba-en-ja
Helsinki-NLP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 ↗rlcd-modernbert-151m
heman10x**OpenJev (Verdict)** is an open-source, post-trained foundational decision model built for structured software workflows, inspired by **TypeSafe AI's Jev** and **Reinforcement Learning for Calibrated Decisions (RLCD)**. It provides calibrated semantic judgments (discrete choices, ordinal scores, and binary probabilities) in a single forward pass without conversational text generation. - **GitHub Repository**: https://github.com/Heman10x-NGU/Verdict-open-jev - **Base Architecture**: ModernBERT-base (`knowledgator/gliclass-modern-base-v2.0`, 151,378,177 parameters) - **Logit Capacity**: 25 candidate slots (24 substantive options + 1 explicit abstention slot) 1. **Non-Autoregressive Single Pass**: Evaluates all candidate options simultaneously in a single forward pass (< 35ms latency) without token generation loops. 2. **Proper Scoring Calibration**: Trained with composite Cross-Entropy + Brier Score loss: $$\mathcal{L}_{\text{total}} = \mathcal{L}_{\text{CE}} + 1.0 \times \mathcal{L}_{\text{Brier}}$$ followed by post-hoc L-BFGS temperature scaling ($T = 1.0716$). 3. **Explicit Abstention Route**: Dedicated `__insufficient_evidence__` candidate slot ensures calibrated rejection on out-of-distribution or insufficient context queries. 4. **Edge and In-Browser WebGPU**: Runs locally in browsers via WebGPU/WASM and on servers via PyTorch/ONNX Runtime.
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View details ↗Tamil-Mistral-7B-Instruct-v0.1
Hemanth-thunder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 model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2288 - Accuracy: 0.9545
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View details ↗GingerMix
Hemlok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 ↗Astrea-R8-Chat-9B-GGUF
HemmingwayAI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 ↗Hemmingway-1
HemmingwayAIAsk most models for a text to your landlord and you get three options, a preamble, and a paragraph explaining the options. Hemmingway-1 just gives you the text. We built it for the writing people actually do every day: messages, emails, the awkward note to a colleague, the thing you have been putting off. Then we tested it against the biggest models in the world at exactly that, and it came first. We tested on eighty real requests. Every answer went head to head against another model's answer to the same request, shuffled so the judge never knew which was which.
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View details ↗Hemmingway-1.5
HemmingwayAI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 ↗Ternary-Bonsai-2-24B-PTQ1_0-GGUF
henceiusegentoo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 ↗fine-tuned-model-indonesian
hendrimardaniThis model is an instruction-tuned language model optimized for **Indonesian natural language understanding, conversational tasks, and general text generation**, quantized using **bitsandbytes 4-bit (NF4)** precision for memory-efficient deployment.
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View details ↗fine-tuned-model-indonesian-2
hendrimardaniThis model is an instruction-tuned language model optimized for **Indonesian natural language understanding, conversational tasks, and general text generation**, quantized using **bitsandbytes 4-bit (NF4)** precision for memory-efficient deployment.
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View details ↗fine-tuned-model-indonesian-GRPO
hendrimardaniThis model is fine-tuned using the **GRPO (Group Relative Policy Optimization)** method with the **Unsloth** and **TRL** frameworks. It was developed to enhance step-by-step automatic reasoning (*Chain-of-Thought*) capabilities in Indonesian.
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View details ↗madlad400-3b-mt-ct2-int8
Heng666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 ↗music-flamingo-gguf
henry1477Download one of the quantizations of the text-text part, then download the mmproj audio processing model (in full BF16 precision). Ensure you are using the latest version of llama.cpp (or at least a release after the PR tag: b7593). 🚨 **Note:** This is a *preview* model. We will release a better base and a thinking version of the model in the next few weeks.
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View details ↗translategemma-27b-it-NVFP4A16
henry1477This is a public, text-only `NVFP4A16` derivative produced with a GPTQ recipe from `google/translategemma-27b-it` at immutable revision `7d10f0b72f89a2d0f268cea30727d8b77c0d25c2`. The vision tower and multimodal projector were removed; the published architecture is `Gemma3ForCausalLM` and `lm_head` was excluded from quantization because Gemma 3 ties it to the embedding table. The upstream multimodal checkpoint has 27.43B parameters; this text-only derivative has 27.01B. This checkpoint is intended for vLLM subtitle translation on NVIDIA Blackwell. Use constrained JSON decoding and independently validate cue IDs. The release evidence below is the measured 12B-versus-27B real-corpus comparison for this exact base revision; it is not copied from the earlier 12B checkpoint. This derivative is governed by the Gemma Terms of Use. You must review and accept the gated upstream terms on `google/translategemma-27b-it`; downloading this public quantization does not waive or replace those terms. Provenance is pinned to `google/translategemma-27b-it@7d10f0b72f89a2d0f268cea30727d8b77c0d25c2`.
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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 ↗Moody-Krea-2-Mix-V4-GGUF
Henrysk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 ↗wai-anima-GGUF
Henrysk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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