PIT-4B-FT-202012
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PIT-4B-FT-202112
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View details ↗PIT-4B-FT-202212
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View details ↗PIT-4B-FT-202312
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View details ↗PIT-4B-FT-202412
Diamegs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_OldModel
diamkan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 ↗qwen-image-edit-plus-nsfw-lora
Diamond1400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 ↗EagleVLA-GGUF
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View details ↗DiarizationLM-Gemma-4-E4B-v1
diarizers-communityDiarizationLM is a Large Language Model framework designed to post-process, correct, and optimize automatic speech recognition (ASR) and speaker diarization outputs. `diarizers-community/DiarizationLM-Gemma-4-E4B-v1` is built on Google's **Gemma 4 E4B** (4B dense parameters) foundation model and fine-tuned with **Locality-Preserving Oracle Supervision** across **all 4 canonical speaker diarization benchmark corpora**: 1. **Fisher English** (2-speaker conversational telephone speech) 2. **Callhome American English** (2–5 speaker informal telephone conversations) 3. **ICSI Meeting Corpus** (3–9 speaker academic research meetings) 4. **AMI Meeting Corpus** (4-speaker tabletop meetings) Unlike earlier models trained exclusively on 2-speaker telephone data (such as google/DiarizationLM-8b-Fisher-v2) or naive multi-domain SFT (which suffers from long-monologue speaker identity drift in 4+ speaker meetings), `diarizers-community/DiarizationLM-Gemma-4-E4B-v1` is trained on locality-preserving oracle targets that teach the model to correct lexical turn boundaries and backchannels (1 ≤ L ≤ 5 words) while preserving acoustic speaker anchors across long monologues (L ≥ 6 words). As a result, it achieves **statistically significant (p --> [eod]`) * **Training Data**: 51,063 Fisher + 20,762 Multi-Corpus (Callhome, ICSI, AMI) locality-preserving prompt-completion pairs * **Optimization**: 10,000 steps, global batch size 8, AdamW (beta1 = 0.9, beta2 = 0.99), peak learning rate 1.5e-4 with 500-step linear warmup and cosine decay on 8 Google Cloud TPU v5p chips * **Prompt Segmentation Length**: 4,000 characters (maximal sequence length 2,560 tokens)
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View details ↗speaker-segmentation-fine-tuned-callhome-eng
diarizers-community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 ↗speaker-segmentation-fine-tuned-callhome-jpn
diarizers-community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 ↗gemma-3-1b-it-GGUF
Diasol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 ↗Diba-Base
Dibachain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 ↗amharic-property-retriever-afro-xlmr-base
dice-research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 ↗gpt-oss-20b-oQ5e-bf16-MLX
dicksondickson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 ↗gpt-oss-20b-oQ6e-bf16-MLX
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View details ↗gpt-oss-20b-oQ8e-bf16-MLX
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View details ↗Qwen3.8-27B-Uncensored-MLX-oQ5e-fp16-mtp
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View details ↗Qwen3.8-27B-Uncensored-oQ5e-bf16-mtp-MLX
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View details ↗Qwen3.8-27B-Uncensored-oQ6e-bf16-mtp-MLX
dicksondickson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-Uncensored-oQ8e-bf16-mtp-MLX
dicksondickson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-Flash-Next-NVFP4-reshard-mtp-fix
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View details ↗Swift-1.5-Qwen3.8-27b-oQ5e-bf16-mtp-MLX
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