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AI Models 84759

• Z-Image-Turbo_GGUF↗

  tsqn

• DeepSeek-R1-0528-GGUF↗

  ubergarm

• csm-1b↗

  unsloth

  **2025/03/13** - We are releasing the 1B CSM variant. Code is available on GitHub: SesameAILabs/csm. CSM (Conversational Speech Model) is a speech generation model from Sesame that generates RVQ audio codes from text and audio inputs. The model architecture employs a Llama backbone and a smaller audio decoder that produces Mimi audio codes. CSM sounds best when provided with context. You can prompt or provide context to the model using a `Segment` for each speaker utterance.

• LTX-2.3-DEV-GGUF↗

  Viper-AI-Vaunt

• AIGC_detector_zhv2↗

  yuchuantian

• EAGLE-Vicuna-7B-v1.3↗

  yuhuili

• GLM-5 (Non-reasoning)↗

  Z AI

• chatglm-6b-int4-qe↗

  zai-org

• FinSense-ModernBERT-Financial-News-Sentiment-Analysis↗

  AnkitAI

• DiffuRefill-1B↗

  Asilarkness

  **Status: pretraining, about half way.** Step ~100k of 200k, ~46B effective training tokens, merge round 290. The weights here are a live pretraining checkpoint: fluent English, facts still unreliable, not instruction-tuned. Use `global_ema.pt`; it is the best checkpoint at every round. A 1.08B masked-diffusion language model decoded with **confidence-targeted re-infilling**: draft the whole sequence in a handful of parallel denoising passes, then spend a few more passes rewriting only the tokens the model is least sure about. An autoregressive model needs one pass per token; this one needs ~20 passes for a whole sequence, whatever its length. Tracked every round on fixed, deterministic batches, so the change between two rounds carries no sampling noise: