Discover the Best AI Resources

UltraData-Code

Provides layered code pretraining corpora (L2 ~400B tokens, L3 ~150B tokens) across 11 languages by filtering ~192M public GitHub repositories into standardized files, algorithmically relevant selections, and implementation-grounded programming exercises. Includes per-file metadata (role, algo relevance, quality) and serialized task records; released under Apache-2.0.

NeoHorse-1-4B

A 4B causal language model post‑trained from Qwen3.5‑4B for agentic workloads — tool use, coding and instruction following — using a routing‑harness feedback loop aimed at iterative capability improvement; distributed as text-only safetensors with native 262,144-token context.

ZGCM-1-Data

Provides a bilingual Chinese–English corpus for LLM training covering pretraining, capability-oriented midtraining (16K–256K long contexts), and supervised fine-tuning. Includes ~4.2T pretrain tokens, ~600B midtrain tokens, and ~4.57M SFT samples; sources span web, PDFs/OCR, code, math, QA, and agentic trajectories under mixed upstream licenses.

Minimax-h3_Singularity

Generates short multimodal videos from text, images, or reference clips using a fine-tuned MiniMax‑H3 fusion model; improves HDR clarity, motion fluidity, distant-face fidelity and VFX while preserving MiniMax‑H3’s prompt/style behavior. Best used via ComfyUI.

MiniCPM5-2B (GGUF)

A 2B-class causal LLM packaged as a GGUF for local inference; offers 131072-token native long context, XML-style tool-calling support, and is tuned with SFT + RL + OPD using the UltraData family for stronger code, math and agentic abilities.

EvoSafeHarness: Evolving Model- and Domain-Specific Harnesses for Securing Agents

Synthesizes a deployment-specific safety harness (natural-language policy plus executable logic) for a frozen LLM in a target domain, using model behavior, domain specs, and adversarial review to optimize safety–utility trade-offs and transfer across agent benchmarks.

DriveZero: End-to-End Driving Beyond Human Demonstrations

Trains end-to-end driving without human trajectory supervision by decoupling perception and action: DriveVFM distills multiple frozen vision foundation models into a single camera backbone, and DriveRL trains a privileged closed-loop RL teacher whose rollouts supervise a camera-only planner, yielding state-of-the-art closed-loop benchmark results.

MiniCPM5-2B

Compact causal LLM for on-device assistants, coding agents and long-context tool use — ~2.52B parameters with a 131,072-token context, trained with SFT + RL + OPD and released with its UltraData training corpora and multi-format deployment checkpoints.

Alissonerdx/Minimax-H3-ComfyUI

LoRA adapters for MiniMax H3 that sharpen and enhance videos in ComfyUI by conditioning on source clips via guide latents for pixel-level alignment. Designed mainly for ref2va as a second-pass sharpening tool, includes a ComfyUI workflow and example before/after clips; requires aligned guide clips at the target resolution and valid clip lengths.

Scores Alone Do Not Prove Discovery: The Discovery Certification Protocol for Auditing AI Research Agents

Defines the Discovery Certification Protocol (DCP) to audit claims of discovery by AI research agents, converting claims into executable recovery and feedback tests. Specifies multi-gate certification, Core control requirements, and a deterministic offline verifier; validated in two controlled audits.

UltraData-RL-2609

Provides ~86K verifiable-reward RL training samples across Math, Knowledge (STEM), Long-Context, and Code for post-training LLMs; each sample includes a ground-truth and verifier-friendly JSONL format for stable reward signals. Note: code tasks require an external sandbox to execute tests.

Qwen3.8-Flash-Next GSQ-RCO GGUF

Provides per-tensor mixed-precision GGUF quantizations of the Qwen3.8-Flash-Next MoE model using GSQ (Gumbel-Softmax Quantization) to produce tensor-level quantized variants and RCO (Riemannian Constrained Optimization) to allocate quant types under exact size budgets; includes a BF16 vision projector and three size/bit-width operating points.