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blt-1b-hf

itazap

**BLT (Byte Latent Transformer)** is a tokenizer-free transformer architecture that operates directly on raw byte sequences. Instead of processing text token by token, BLT dynamically groups bytes into **entropy-based patches**, enabling more efficient and scalable processing for byte-level tasks. Key components: - **Local Encoder → Latent Transformer → Local Decoder** architecture. - **Entropy-based patcher (BltPatcher)**: scans byte streams and creates patches when entropy thresholds are met. - **Hash n-gram embeddings**: maintain contextual information over neighboring bytes. BLT achieves competitive performance compared to traditional token-based transformers, supporting multilingual, noisy, or mixed-script input.

Category pending

Input Not verified→ Output Not verified

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MOSS-Transcribe-Diarize-HF

itazap

MOSS-Transcribe-Diarize 0.9B is an end-to-end audio understanding model for long-form multi-speaker transcription, diarization, timestamps, and acoustic event awareness. It supports transcription and diarization across **50+** languages, single-pass inference on audio recordings up to **90 minutes** long, and custom hotword prompting for domain-specific terms. Given an audio or video file, the model generates a compact speaker-aware transcript in one pass, including timestamps and anonymous speaker labels such as `[S01]`, `[S02]`, and beyond.

Speech recognition

Input Audio · Text→ Output Text

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

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Is the top-ranked model the best for me?+

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