Tag

Simulates agentic environments and predicts next environment states from actions and interaction history using a language-based world model across seven domains. Trained via a CPT→SFT→RL pipeline with an MoE architecture and very long context; intended for environment simulation and agent research.

Provides 2,170 reference-grounded evaluation samples across seven agent domains (MCP, Search, Terminal, SWE, Android, Web, OS) to score language world models on Format, Factuality, Consistency, Realism and Quality. Includes per-domain JSONL files, judge prompts and an evaluation script for reproducible scoring.

Provides a pre-quantized NVFP4 checkpoint of GLM-5.2 for long-context reasoning and coding; reduces model footprint so GLM-5.2 can run on multi‑GPU Blackwell nodes and is ready for inference with SGLang and vLLM.

NVFP4-quantized variant of Qwen3.6-27B that reduces parameter bits from 16 to 4, cutting disk and GPU memory requirements by ~2.5× while keeping comparable benchmark accuracy; ready for vLLM-based inference on NVIDIA hardware and supports long, multimodal contexts.

Compares 30 frontier LLMs generating static SVG markup from 500 prompts using 1,355,161 human votes across three leaderboards (Preference, Coherence, Alignment); provides raw SVGs, 768×768 rasterized PNGs, and per-comparison human vote records under a CC-BY-4.0 prompt license.

Provides an open-source Mixture-of-Experts coding LLM (397B) optimized for agentic, tool-enabled coding workflows with a 262,144-token context window, OpenAI-compatible API, serving recipes (vLLM/SGLang), and published coding-benchmark results.

230M-parameter multilingual instruction-tuned text-only LLM for on-device agentic pipelines and data extraction; 32K context, 19T-token pretraining, optimized for fast CPU/edge inference (e.g., 213 tok/s on Galaxy S25 Ultra, 42 tok/s on Raspberry Pi 5); not for heavy reasoning or complex code generation.

Measures whether models produce valid JSON/YAML that strictly follow a requested schema across diverse, naturally phrased prompts. Contains 2,000 frozen test prompts with binary structural validation (no constrained decoding), focusing on schema compliance and edge cases like escaping, wrapper keys, and fenced code blocks.

A self-improving, agentic coding LLM tailored for terminal-style coding agents and tool-calling, provided as 35B MoE GGUF weights with very large context support. Trained with reinforcement learning to jointly generate task scaffolds and solutions; designed for local inference and OpenAI-compatible tool endpoints.

Converts low‑poly 3D viewport or game/CG renders into photorealistic cinematic video while preserving the input's composition, camera motion and layout; offers Light and Strong LoRA variants to trade fidelity for aggressive photorealism.

Provides a rubric-based benchmark that converts dense image captions into instance-specific atomic checks (Must-Right and Easy-Wrong) and a gated scoring rule, aiming to expose perceptual brittleness and better align multimodal model evaluation with human judgment.

Provides 1,503 Krea 2 style LoRAs (original safetensors + ComfyUI builds) trained on fal.ai, each with a short trigger phrase and downloadable weights for quick style transfer or further retraining.