Curated free roadmap and runnable Colab notebooks to learn Large Language Models, split into LLM Fundamentals, LLM Scientist, and LLM Engineer tracks. Includes hands-on labs for quantization, model merging, fine‑tuning, retrieval augmentation, and local/server/edge deployment workflows.
AI Model2026
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.
AI Model2026
Rewrites AI-generated English and Chinese drafts so they read like human writing while preserving every number, date, unit, name and quote. Runs locally with multiple GGUF quantized builds and a strict byte-for-byte prompt format for consistent rewrites.
AI Model2026
Provides compact mixed-precision GGUF quantizations of UkisAI's Swift 1.5 (derived from Qwen3.8-27B), using ISTA GSQ-RCO per-tensor allocations with Swift-specific refinement. Offers multiple 8–12 GB tiers, optional MTP heads, and KLD evaluation against the Swift BF16 baseline.
AI Model2026
Capability-targeted compression of Qwen3.8-Flash-Next: half the experts are removed and remaining weights quantized to 3.5 bpw, producing a 58.4 GB GGUF (29.6 GB resident) that preserves coding and multimodal ability while trading off other domains.
AI Model2026
Provides a high-fidelity mixed-precision (≈3-bit) GGUF quant of Qwen3.8-27B tailored for long-horizon agent workloads and cyber-focused red-teaming. Preserves reasoning, thinking mode, MTP speculative decoding and vision (via a separate mmproj); released as an uncensored/abliterated research build under Apache-2.0.
AI Audio2026
Transcribes English speech into punctuated, capitalized text — a 164 MB quantized ASR model that averages 5.21% WER across seven Open ASR Leaderboard sets. Optimized for on-device and CPU/GPU inference, with fast runtimes on Apple M5 and Docker/GPU support.
Compresses recurrent states in linear-attention LLMs via spatial–temporal post-training quantization, allocating bits by error lifetime and per-row impact to preserve accuracy (6-bit ≈ FP32) while cutting serving memory up to 68.7%.
AI Audio2026
On-device speech-to-text for short clips (up to 30s) in seven languages, yielding transcripts, word-level timestamps and per-frame speech embeddings in a single 16.9 MB model. Runs on the Needle CPU engine with 2–4 bit quantization, supports keyword biasing and returns empty transcripts for silence.
AI Model2026
Provides an EXL3 3.0 bits-per-weight quantization of a weight-edited GLM-5.3 UNCENSORED FP8 model for self-hosted text generation and agent workflows. Key characteristics: 753B MoE architecture, 273 GiB on disk, converted with ExLlamaV3; tool-call parsing requires preserving string arguments.
AI Model2026
Performs a byte-level transplant of 144 tensors in an already-quantized GSQ-RCO Qwen3.8-Flash-Next to ablate the model's refusal direction while preserving GSQ-learned scales and the upstream per-tensor type assignment; multimodal, 262K context. Intended for local inference, red-teaming and quantization research; no retraining or built-in safety.
AI Model2026
Post-trained multimodal Qwen3.8-27B variant that uses alternating SFT and RLOO to reduce pathological long reasoning tails; ships multiple quantization tiers (BF16, FP8, NVFP4, INT8, INT4, GGUF), supports MTP and DFlash2 speculative decoding, and includes detailed benchmark and runtime recommendations.