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AI Models 85003

• mms-tts-jmd↗

  facebook

• xglm-564M↗

  facebook

  XGLM-564M is a multilingual autoregressive language model (with 564 million parameters) trained on a balanced corpus of a diverse set of 30 languages totaling 500 billion sub-tokens. It was introduced in the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin\*, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li\* (\*Equal Contribution). The original implementation was released in this repository. For intended usage of the model, please refer to the model card released by the XGLM-564M development team. The following snippet shows how to evaluate our models (GPT-3 style, zero-shot) on the Choice of Plausible Alternatives (COPA) task, using examples in English, Chinese and Hindi.

• cognitivecomputations-Llama-3-8B-Instruct-abliterated-v2-GGUF↗

  featherless-ai-quants

• gemma-7b-it-sfp-cpp↗

  Google

• LLaDA-1.5↗

  GSAI-ML

  We introduce LLaDA 1.5, a competitive large diffusion language model, trained by variance-reduced preference optimization (VRPO), as presented in the paper LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models. Compared with LLaDA-8B-Instruct, LLaDA 1.5 achieves better performance on a wide range of tasks, including Math, Code, and Alignment tasks.

• mamba2-mRNA↗

  helical-ai

• wam_ctxpool_avg↗

  hmkang

• CantoneseLLM-v2.0-8B-Thinking↗

  hon9kon9ize

• Ternary-Bonsai-2-27B-Uncensored-Heretic-swift_unc↗

  hotdogs

• granite-4.0-h-350m↗

  ibm-granite

  **Model Summary:** Granite-4.0-H-350M is a lightweight instruct model finetuned from *Granite-4.0-H-350M-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques including supervised finetuning, reinforcement learning, and model merging. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may fine-tune Granite 4.0 Nano models to support languages beyond those included in this list. **Intended use:** Granite 4.0 Nano instruct models feature strong instruction following capabilities bringing advanced AI capabilities within reach for on-device deployments and research use cases. Additionally, their compact size makes them well-suited for fine-tuning on specialized domains without requiring massive compute resources.