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).
AI Model2026
Fine-tuned Qwen3.6-35B-A3B MoE that reproduces Claude Opus 4.7-style chain-of-thought with explicit <think>…</think> blocks. Offers sparse activation (256 experts, ~3B active params), 64k context, and GGUF builds for local inference; best for long, multi-step reasoning but may emit very long reasoning traces.
AI Model2026
An ~18B frankenmerge text-generation model that stacks two 32-layer Qwen3.5-based finetunes and ships as a 9.2GB Q4_K_M GGUF for efficient local inference. A 1000-step QLoRA heal reduces layer-boundary code corruption and targets coding, reasoning, multilingual chat, and 12–16GB GPU compatibility.
AI Dataset2026
Contains 8,124 reasoning conversations (extended-thinking + final responses) generated by Anthropic Claude Opus 4.7 for distillation into open-source LLMs. Each row stores the prompt, thinking trace, final answer and usage metadata; packaged under Apache‑2.0.
AI Dataset2026
Synthetic JSON dataset of model-generated prompts and step-by-step reasoning traces (≈90k rows, ~75M tokens) created with Claude Sonnet 4.6 and cross-checked by Gemini 3.1 Pro — intended for training or fine-tuning LLMs on natural reasoning, multi-domain code/math, and instruction following. Hosted on Hugging Face, MIT license.
AI Model2026
Generates English text matching pre-1931 style — a 13B language model trained on ~260B tokens of pre-1931 English, useful for historical-language generation and stylistic research. An instruction-tuned variant exists for interactive tasks.
AI Dataset2026
Provides 2,405 chain-of-thought reasoning traces generated by Claude Opus 4.7 for hard math, science, and formal problems. Each record pairs a problem with the model's full <think> working and a polished answer; available as parquet splits for non-commercial research under Anthropic's usage policy.