AI Model2026
Provides a compact GGUF export of a tuned Gemma‑4 26B variant for local inference, optimized for llama.cpp and Apple Silicon to deliver faster, less‑censored chat and coding outputs. Includes Q4_K_M quantization and a neutral embedded template for more reliable local deployments.
AI Dataset2026
Provides a ~9.2M-instance Japanese multimodal post-training dataset for vision–language models, combining image–text pairs, PDF corpora and generated VQA to improve Japanese VLM performance; access is restricted by Japanese copyright (download via llm-jp GitLab).
AI Dataset2026
Provides 1,003,589 full chain-of-thought reasoning traces and final answers generated by GLM-5.1, split into main/Math/PHD-Science/Multilingual-STEM subsets. Useful for instruction-tuning, supervised fine-tuning, and reasoning experiments; released under Apache-2.0.
AI Dataset2026
Provides 336,146 Turkish instruction-following chat examples (system→user→assistant) for supervised fine-tuning; single train split (no validation/test), reported MIT license, diverse tasks (rewrites, summarization, QA) and a uniform system prompt that may bias model behavior.
AI Model2026
Removes safety refusals from a Gemma 4 E4B–based model and publishes uncensored, locally runnable GGUF/safetensors variants while preserving all tensors and fixing prior corruption. Intended for red‑teaming and offline research; not recommended for production.
AI Dataset2026
Provides deduplicated, sanitized Usenet posts (1980–2013) for language-model pretraining and linguistic research. Includes a ~103.1B-token full corpus (408M posts) with freely downloadable sample files; full corpus access requires a license and PII redaction was applied.
AI Model2026
Clinical question-answering model for psychological support in obesity weight-management. Integrates UK Biobank population evidence to produce clinically interpretable, stigma-aware responses that help clinicians identify distress, prompt screening, and suggest appropriate referrals.
AI Model2026
A healed 64-layer 'frankenmerge' that stacks two Qwen3.5-derived finetunes into an ~18B GGUF model for multilingual text generation, reasoning, and reliable code/frontend output. Healed with a 1000-step QLoRA to reduce layer-boundary artifacts and targeted to run on 12–16 GB GPUs.
AI Model2026
Detects and masks personally identifiable information (PII) in text using a bidirectional token-classification model for high-throughput, on‑premises sanitization. Key traits: 1.5B parameters, 128k-token context, Apache 2.0 license, and tunable precision/recall operating points.
AI Dataset2026
Provides a cleaned, SFT-ready collection of ~746k GLM-5.1 reasoning traces for instruction tuning and reasoning distillation. Normalizes varied chain-of-thought formats into a single conversations/input/output schema and preserves four focused subsets (main, PHD-Science, Multilingual‑STEM, Math).
AI Model2026
GGUF quantized files for a Qwen3.6-35B checkpoint fine-tuned with Claude Opus 4.6-style chain-of-thought distillation to improve reasoning. Offers multiple llama.cpp-compatible quant options (Q4/Q5/Q6/Q8) for local text-generation inference.
AI Dataset2026
Provides 7,823 single-turn reasoning conversations generated by Anthropic's Claude Opus 4.7 and reformatted into Qwen-style chat templates for supervised fine-tuning (SFT). Includes explicit <think> chain-of-thought blocks and many long reasoning chains (avg ~4k tokens).