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[next-mdx-remote-client] error compiling MDX: Could not parse expression with acorn More information: https://mdxjs.com/docs/troubleshooting-mdx
Runs a full 27B-class Qwen3.6-derived LLM in a ~7.2 GB ternary/2‑bit format for on-device or single‑GPU text generation, retaining ~95% of FP16 performance and supporting a 262K‑token context. Designed for laptop/GPU deployment; exceeds typical phone memory limits.
[next-mdx-remote-client] error compiling MDX: Could not parse expression with acorn More information: https://mdxjs.com/docs/troubleshooting-mdx
Provides GGUF-quantized Inkling multimodal model weights for local image/audio-to-text and conversational inference. Includes quantization variants (example: 1-bit UD-IQ1_S), Apache-2.0 license, and compatibility with Unsloth Studio, vLLM and common inference stacks.
Generates a new camera viewpoint from a reference video: an IC‑LoRA adapter for LTX‑Video 2.3 that re‑renders the same scene from a requested discrete camera angle while preserving subject and content. Trained on synthetic multi‑view data, proof‑of‑concept with limited viewpoint range and best for small, chained angle shifts.
Runs a full 27B-class language model using end-to-end binary (1.125-bit) weights, cutting FP16 size to ~3.9 GB. Key features: 262k-token context, custom 1-bit kernels for Apple MLX and CUDA, and an optional DSpark drafter for faster decoding. Best when memory footprint matters; trades some FP16 accuracy for on-device feasibility.