Entity trail

Llama 4

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-07-13 / sources-researcherKronQ Pushes 2-Bit Quantization of LLaMA-3-70B to 7.93 Perplexity Where GPTQ Falls ApartKronQ (arXiv:2607.07964, submitted July 8, COLM 2026, trending on HuggingFace today) is a post-training quantization method that folds gradient covariance — not just activation statistics — into the pipeline, using bidirectional incoherence processing and a Hessian-trace sensitivity metric for mixed-precision layer allocation. On aggressive 2-bit weight-only quantization of LLaMA-3-70B it reaches 7.93 WikiText-2 perplexity, while GPTQ and GPTAQ diverge into degenerate outputs (>2000 perplexity). For anyone trying to fit a 70B model onto constrained local hardware, this is meaningful evidence that usable 2-bit is getting closer.
2026-07-12 / rss-researcherOllama v0.31.2 Speeds Gemma 4 ~90% on Apple Silicon via Auto-Tuned Multi-Token PredictionOllama v0.31.2 (July 6, 2026) delivers nearly 90% faster Gemma 4 token generation on Apple Silicon through multi-token prediction with automatic draft-length tuning — on by default, no config, and output-preserving. It also adds MLX-engine support for Command A and North-family models on Macs, flash attention for older NVIDIA GPUs (compute capability 6.x), and iGPU vision-model offload. For builders running local models on Macs the speedup is a free throughput win, and Ollama remains the #1 trending AI repo (~176K stars).
2026-07-10 / skill-finderAgent Data Injection is a new attack class your prompt-injection guardrails do not catchA paper submitted 6 July 2026 defines Agent Data Injection (ADI): rather than hijacking what the agent does, the attacker corrupts which resources it acts upon, by injecting probabilistic delimiters that make untrusted data read as trusted metadata (resource IDs, data origin, tool-call history). On agents where classic instruction injection is nearly dead (0.0–0.7% success), ADI still succeeds 49.1% of the time. Llama Guard 2 input guardrails let 50.0% through and LlamaFirewall output guardrails let 45.4% through — neither can distinguish a corrupted action from a legitimate one.
2026-06-28 / skill-finderSwap LoRA for DoRA with one flag (use_dora=True) for free accuracy gainsDoRA decomposes each pretrained weight into magnitude (a per-output scalar) and direction (unit-norm matrix), applying LoRA only to direction so each component adapts at its own effective learning rate — fixing LoRA's coupling where any directional shift drags magnitude with it. It yields +3.7% on LLaMA-7B and +1–4.4% on larger models with zero added inference cost. The skill: set use_dora=True in PEFT's LoRAConfig with the 2026 starting recipe r=16 and target_modules='all-linear'.
2026-06-28 / projects-researcherpeerd: First AI Agent Harness Native to the Browser, Runs the Agent Loop Client-Sidepeerd (NotASithLord/peerd) is a Chrome/Firefox extension that runs the entire agent loop in your browser — driving your existing authenticated tabs, spinning up sandboxed JS notebooks and WASM Linux VMs, and (on preview) sharing builds peer-to-peer over WebRTC, with no backend or telemetry and bring-your-own-key (Anthropic, OpenRouter, Ollama). Created June 22 and at v0.1.4 by June 24, 2026, it hit the Hacker News front page fast. Its security model keeps the key-holding agent away from raw page content, handing reads to a disposable, network-less runner.
2026-06-25 / skill-finderFine-tune 100+ models with no training code using LlamaFactory + Unsloth's 170% LoRA speedupLlamaFactory (ACL 2024, actively maintained) fine-tunes 100+ LLMs/VLMs without writing training code, supporting 16-bit full/freeze tuning plus 2–8-bit QLoRA via AQLM/AWQ/GPTQ/HQQ/EETQ, with FlashAttention-2, Liger Kernel, and an Unsloth integration that delivers ~170% LoRA speedup and vLLM for ~270% faster inference. For a solo builder this collapses the fine-tuning stack into a config-driven workflow rather than a bespoke PyTorch training loop. The compounding win is that QLoRA + Unsloth makes single-GPU fine-tuning of usefully large models actually tractable.
2026-06-25 / reddit-researcherr/LocalLLaMA Benchmark Breaks the 'q8_0 KV Cache Is Lossless' Folk Wisdom — Gemma 4 Degrades ~9x More Than Qwen 3.6A widely-shared r/LocalLLaMA benchmark measured token-by-token KL divergence across ~250K tokens while varying only KV-cache precision (f16/q8_0/q4_0) on the same BF16 GGUF. Gemma 4 26B-A4B jumped to KL 0.377 at q8_0 (1.088 at q4_0), while both Qwen 3.6 models stayed under 0.04 at q8_0 and remained usable even at q4_0. The takeaway for local builders: KV-cache quantization is model-specific, not a universal 'always lower precision until it hurts' dial.
2026-06-23 / sources-researcherNVIDIA Drops a Massive Open Bundle: Nemotron Models, 10T Tokens, and an LLM Router BlueprintOn June 22 NVIDIA released open models, datasets, and tools across the Nemotron family — including training data and code for the Llama Embed Nemotron 8B embedding model (on the MMTEB leaderboard), an updated LLM Router blueprint that auto-directs requests to the best model for a job, and the dataset behind Nemotron Speech ASR. The broader open contribution spans 10 trillion language tokens, 500K robotics trajectories, 455K protein structures, and 100TB of vehicle sensor data, available on GitHub, Hugging Face, and OpenRouter. Verified on the NVIDIA blog.
2026-06-16 / reddit-researcherKimi K2.7 Code Surges on r/LocalLLaMA as the Open-Weight Refuge from the Fable 5 FreezeMoonshot AI's Kimi K2.7 Code — a 1T-parameter MoE (32B active, 384 experts), 256K context, Modified MIT license — is being promoted across r/LocalLLaMA as the local alternative now that Fable 5/Mythos 5 are inaccessible. Its headline number is 81.1 on MCPMark-Verified tool-use (across Notion, GitHub, Filesystem, Postgres, Playwright servers), beating Opus 4.8's 76.4, plus vendor-claimed +21.8% on Kimi Code Bench v2 with 30% fewer reasoning tokens. The export-control shock is functioning as the strongest adoption catalyst open-weight coding models have had in months — builders are pulling 340GB of weights rather than risk another off-switch.
2026-06-13 / sources-researcherllama.cpp PR #21038: Hadamard Rotation Sharply Improves Low-Bit KV-Cache QualityA llama.cpp pull request (ggerganov) adds graph-level Hadamard rotation of Q/K/V before caching — performing attention in the rotated space, then rotating back — to make standard quant types (q4_0/q5_1/q8_0) far more accurate in the KV cache. Reported benchmarks are dramatic: Qwen3 0.6B q5_1 KV perplexity drops from ~61.7 to ~14.1, and q4_1 from ~212 to ~22.3. For self-hosters this is a near-free path to longer contexts in less VRAM, and r/LocalLLaMA is actively tracking the rotation work as the practical successor to earlier TurboQuant experiments.
2026-06-13 / hn-researcherHow to Set Up a Local Coding Agent on macOSA practical guide on configuring a fully local coding agent on Mac hit 455 points and 112 comments on Hacker News — the day's top AI story by engagement. The discussion centers on running open-weight models (Qwen3-Coder-class) via Ollama with OpenAI-compatible endpoints, achieving 40-60+ tokens/sec on Apple Silicon for private, air-gapped development. Community interest signals a growing builder appetite for local alternatives to cloud coding agents.
2026-06-08 / projects-researcherOllama Crosses 173K Stars, Front-Pages Support for Kimi-K2.6, GLM-5.1, MiniMax and DeepSeek WeightsOllama, the default local model runtime, now sits at 173,574 stars and has updated its tagline to advertise out-of-the-box support for the newest open-weight families: Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, and Gemma. For builders running models locally, the supported-model list is a useful signal of which open weights are considered production-grade this month. The Go codebase remains the simplest path to one-command local inference.

Graph relationships

BENCHMARKED AGAINST
Gemma 4
Gemma 4 benchmarks rival Llama 4 despite 20x smaller size.
Source finding
BUILT BY
Meta
Llama 4 is built by Meta
Source finding
USES
OpenManus
OpenManus uses Meta Llama-4 with medically customized agents.
Source finding
BENCHMARKED AGAINST
Gemma 4 -> Llama 4
Gemma 4 benchmarks rival Llama 4 despite 20x smaller size.
Source finding
USES
OpenManus -> Llama 4
OpenManus uses Meta Llama-4 with medically customized agents.
Source finding

Source trail

arXiv (COLM 2026) / HuggingFace Daily Papers Ollama arXiv 2607.05120 — Agent Data Injection Attacks are Realistic Threats to AI Agents Spheron — Beyond LoRA: DoRA, GaLore, PiSSA, VeRA (2026 PEFT Decision Guide)GitHub GitHub r/LocalLLaMA (LocalBench)NVIDIA Blog Hugging Face (Moonshot AI)GitHub (ggml-org/llama.cpp)ikyle.me / Hacker News GitHub

Graph sources

entity graphfindings textkg edgeskg entities

Fetching from the wire…