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Hugging Face
AI Model·2026
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CohereLabs/command-a-plus-05-2026-w4a4

CohereLabs

W4A4-quantized build of a 25B-parameter multimodal LLM that produces text from image+text inputs and supports conversational tool use. Trades very small quality differences for much lower GPU memory and latency so inference can run on smaller accelerators (vLLM support).

#transformers#llm#multimodal#vision#vllm+3
GitHub
AI Model·2026
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Hy-MT2

Mao Zheng, Zheng Li +11·Tencent Hunyuan (Tencent)

Provides a family of multilingual translation models (1.8B, 7B, 30B-A3B) supporting 33 languages and instruction-following; offers low-bit quantized formats (1.25-bit, 2-bit, FP8, GGUF) and deployment guides for on-device and server inference.

#multilingual#translation#quantization#moe#gguf+8
Hugging Face
AI Model·2026
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Qwen3.6 27B - OBLITERATED

OBLITERATUS

Provides a locally runnable 26.9B Qwen3.6 checkpoint that surgically reduces refusal behavior in weight space while preserving capability; ships bfloat16 safetensors and a GGUF quant ladder for local runtimes and red-team evaluation.

#huggingface#transformers#llm#vllm#ai-deploy+5
GitHub
AI Infra·2026
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Switchyard

NVIDIA Corporation, NVIDIA-NeMo

Routes LLM API traffic across providers by translating OpenAI, Anthropic, and OpenAI Responses formats, and orchestrates multi-backend routing with typed algorithms and Prometheus metrics. A Rust proxy/library offering launcher, standalone server, and embeddable routing components; experimental (pre-alpha).

#rust#llm#ai-serving#ai-api#ai-api-management+9
Hugging Face
AI Audio·2026
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MOSS-Transcribe-Diarize

OpenMOSS-Team, MOSI.AI +1

Converts long-form multi-speaker audio/video into a compact, speaker-aware transcript with timestamps and anonymous speaker labels in one pass. Combines ASR and diarization in a single model, supports custom prompts/hotwords, and targets meetings, podcasts, interviews and long recordings.

#ASR#audio#speech#stt#transformers+5
GitHub
AI Train·2026
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Cosmos-Framework

NVIDIA

End-to-end Python framework for training and serving NVIDIA's Cosmos world models (Cosmos3), integrating distributed training (FSDP/TP/CP/PP), DCP/safetensors checkpoints, dataset adapters, multiple inference backends, online serving, and agent skills.

#nvidia#ai-train#ai-serving#pytorch#cuda+8
Hugging Face
AI Model·2026
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Fara1.5-27B

Microsoft Research AI Frontiers, Microsoft

Automates end-to-end web workflows from browser screenshots by emitting pixel-grounded actions (click, type, scroll, visit, search). Vision-first multimodal agent fine-tuned from Qwen3.5-27B with critical-point safety checks; intended for sandboxed, human-supervised deployments.

#qwen#multimodal#vision#agent-skills#microsoft+6
Hugging Face
AI Model·2026
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NVIDIA Cosmos3-Super-Image2Video

NVIDIA

Generates temporally coherent MP4 videos from a single input image plus text instructions, with configurable resolution, frame count, and optional AAC audio. Optimized for NVIDIA GPU stacks and integrates with vLLM‑Omni and Hugging Face Diffusers for production inference and research workflows.

#nvidia#huggingface#diffusers#ai-video#video+5
Hugging Face
AI Model·2026
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MiniCPM5-1B

openbmb

A 1.08B-parameter causal LLM engineered for on-device text generation with native long-context (131k tokens) and built-in Think/No-Think modes. It emphasizes tool-calling support, lightweight deployment formats (BF16, GGUF, MLX), and RL+OPD post-training for stronger reasoning and code generation.

#llm#transformers#huggingface#vllm#ollama+3
Hugging Face
AI Model·2026
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Step 3.7 Flash

stepfun-ai

Processes images and text to produce structured, reasoning-rich text outputs for high-throughput agentic workflows. Sparse MoE design (198B total, ~11B active per token), 256k context window and selectable reasoning levels—optimized for single-pass parsing, verification, and multi-step automation.

#multimodal#llm#transformers#vllm#ai-inference+4
Hugging Face
AI Model·2026
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Mellum2 Thinking

JetBrains

Generates text with explicit chain-of-thought traces for multi-step reasoning and math-heavy tasks, emitting reasoning inside <think>...</think> blocks. Uses a Mixture-of-Experts design and 131k token context for long, verifiable workflows—best when you need inspectable reasoning.

#huggingface#transformers#llm#vllm#foundation-model+1
Hugging Face
AI Model·2026
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PaddleOCR-VL-1.6

PaddlePaddle

Performs image-to-text document parsing and OCR for complex elements (tables, formulas, charts, seals), with multilingual support (en/zh). It uses region-aware data optimization and progressive post-training to improve weak-region supervision and is plug-and-play compatible with PaddleOCR-VL-1.5.

#ocr#multimodal#vision#image#multilingual+5
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