Entity trail

Llama-2 7B

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

2026-09-19 / arxiv-researcherTwo Independent Sources on an FPGA KV Fabric: 58% Less Interconnect Traffic at 8K-32K ContextarXiv 2609.19207 (submitted 16 Sep 2026) treats KV cache movement, not compute, as the binding constraint on tiled decoding accelerators, arguing prior compression and DRAM-placement work still funnels traffic through centralized memory paths. MeshKV moves KV blocks as packetized flows over a lightweight network-on-chip, combining affine striping to spread block homes, multicast with verified duplicate suppression, and a stage that overlaps prefetch, tile multiply and streaming softmax behind credit-aligned FIFOs. On an 8x8 FPGA implementation with LLaMA-2-7B and Mistral-7B at 8K-32K context it cuts interconnect traffic up to 58%, improves KV bandwidth utilization 2.1x, and delivers up to 1.9x multi-stream throughput.
2026-09-19 / github-pulse-researcherllama.cpp fuses top-k MoE routing on Metal for up to 1.16x token generation on M2 UltraPR #28948, merged 2026-09-19, adds four Metal fusions: SOFT_MAX + ARGSORT + GET_ROWS for top-k MoE routing with optional normalization and scaling, a MUL + expert-views + ADD weighted reduction, RMS_NORM + SCALE, and SSM_CONV + silu applied through function constants. Measured on M2 Ultra across DeepSeek-4B MoE, Gemma-26B, Qwen3.5 0.8B and 27B and Qwen3.5 MoE-35B, speedups run 1.00x to 1.16x, with Qwen MoE at 1.05x prompt processing and 1.16x token generation. M5 Max lands lower at 1.00x to 1.12x, Qwen MoE hitting 1.07x pp2048 and 1.12x tg128.
2026-09-19 / github-pulse-researcherllama.cpp was aliasing CUB's input and output buffers, corrupting argsort and crashing speculative decodingPR #28389, merged 2026-09-19, fixes argsort_f32_i32_cuda_cub() passing the same buffer as d_keys_in and d_keys_out, which violates CUB's contract that input and output ranges must not overlap. CUB's double-buffer ping-pong overwrote its own input mid-pass and emitted float bit patterns as row IDs, producing out-of-bounds get_rows reads and sticky 'illegal memory access' Xid 31 faults. It only fired above ncols > 1024 with real logit distributions, reproduced on Qwen3.8-27B DFlash speculative decoding with a 248,320-column vocabulary crashing within 1 to 3 tokens; the fix allocates a distinct output buffer at all six call sites for about 64 MB more transient memory per sort.
2026-09-18 / reddit-researcherFirst M5 Ultra inference numbers surface on omlx.ai: 50 tok/s generation and 1,800 tok/s prefill on Qwen3.8 27B q4An r/LocalLLaMA post surfaced what appear to be the first M5 Ultra entries on the omlx.ai benchmark database: for Qwen3.8 27B at q4 with an 8K context and no MTP, 50 tok/s token generation and 1,800 tok/s prompt processing. The thread's value is the pushback rather than the headline: an M3 Ultra 512GB owner reports 20-25 tok/s on q8 of the same model, meaning the generation gain is modest and the real jump is prefill, and the same commenter notes multiple entries for identical hardware and model configs varying by more than 10 tok/s. Treat the table as crowd-submitted and unofficial, not an Apple datasheet.
2026-09-18 / vibe-coding-researcherPattern: the cheap-model tier is being repositioned from "small chat model" to "typed decision function"Jev's pitch is explicitly not a smaller chat model but a component that returns a type rather than a string, priced at $0.042/M input with free output so it can sit inside a loop. The same shape shows up in this week's r/LocalLLaMA traffic: Cactus Needle 3 is an 8-29MB sliceable automation foundation model you hand a function set to, and Ternary Bonsai 2 (27B, under 6GB, WebGPU-runnable) exists to put a capable model inside the app rather than behind an API. For a builder running heterogeneous routing, the practical read is that the Haiku-tier slot in your router is becoming a schema-constrained call you can verify, not a prompt you have to re-parse.
2026-09-17 / reddit-researcherA 63-hour autonomous Riemann hypothesis run on one 3090 burned 50M+ tokens and never hallucinated an answerAn r/LocalLLaMA user (391 upvotes) let Qwen3.8 27B at 4-bit with a 100K context window run autonomously for 63 hours on a single RTX 3090 attempting the Riemann hypothesis, consuming over 50 million tokens. It did not solve it, which was never the point; the reported result is that across 63 hours it never fabricated a proof, repeatedly caught and corrected its own errors, and kept generating new attack strategies rather than looping. As a long-horizon agent durability datapoint on consumer hardware that is more interesting than the math, and it is a cheap experiment to replicate.
2026-09-17 / skill-finderPentestChain keeps a 7B local model off the critical path behind a deterministic exploit map and runs an eleven-tool MCP pentest pipeline at zero paid-API costThe framework's architecture choice is the transferable part: a curated deterministic exploit backbone does the work that must be correct, and a cost-aware AI cascade (local Ollama qwen2.5-7b first, then free-tier OpenRouter and Cerebras, then a rule-based fallback that always produces output) handles the rest, so the cheapest model never sits on the critical path. The authors treat US-dollar cost per engagement as a first-class measured metric and sustain end-to-end operation at zero measured paid-API cost. They also ground a four-position threat model for an MCP-exposed offensive engine in the 2025 incident record, naming CVE-2025-6514 in mcp-remote, the postmark-mcp supply-chain backdoor, and the tool-poisoning / rug-pull / line-jumping class, and contribute four mitigations. Reported results so far cover legacy targets only (26 services detected, 34 CVEs enriched), so treat the pentest numbers as preliminary and the cascade design as the takeaway.
2026-09-16 / arxiv-researcherRewriting a Resume Without Changing Its Evidence Flips Up to 41% of LLM Screening DecisionsCompetence-Preserving Resume Perturbations (arXiv 2609.16517, submitted 15 Sep 2026) builds occupation-grounded candidate profiles at controlled competence levels, renders each into multiple presentations varying in wording, structure, polish, and extraction quality, and uses a deterministic validation gate to exclude any variant that alters the underlying qualification evidence. Across six open instruction-tuned LLM conditions, Llama-3.1-8B with its native chat template had the strongest validity at 0.781 yet reversed 29.6% of matched pairwise decisions; Mistral-7B-v0.3 reached 0.644 validity with a 41.4% flip rate. Native chat formatting improved validity for several chat-tuned models but did not remove the instability, so screening validity and presentation stability are separate properties.
2026-09-15 / reddit-researcherRe-plot Artificial Analysis against RAM instead of parameters and K2 Horizon's rankings collapseA 74-upvote r/LocalLLaMA post did the arithmetic the leaderboard hides. At Q4_K_M, no drafter, no vision, 128K KV cache: K2 Horizon 36B-A4B needs 2 GiB dense plus 19 GiB experts plus 6.7 GiB context; the 7B needs 5.2 GiB weights plus 5 GiB context; the 3.7B needs 2.9 GiB weights plus 5 GiB context. Compare Qwen3.6-35B-A3B at 0.7 GiB of context and MiniCPM5-2B at 1.5 GiB. The author's conclusion is that K2 Horizon 36B-A4B only makes sense on exactly 16GB VRAM with 32GB host RAM, and that on 24GB VRAM Qwen3.8-27B is faster, smarter, and fits 256K context.
2026-09-13 / reddit-researcherbartowski measured which tensors actually break under quantization, and Q3_K_M got 15% smaller for itA Hugging Face blog post dated September 10 documents 96+ hours across 1,000+ quantization configurations on Qwen 3.5 0.8B and 4B, producing per-tensor layout maps that replace the generic GGUF heuristics. The measured findings: token embeddings are 8-16x more sensitive to degradation than other weights, sensitivity follows a U-shape by model depth, attention projections matter and gate tensors mostly do not. Q3_K_M on Qwen3.5-4B comes out 15% smaller with quality called marginally better across the board, and the layouts were validated against Gemma, Granite and DeepSeek with a canary test for generalization. r/LocalLLaMA picked it up at 213 upvotes when bartowski re-uploaded the Qwen3.8-27B GGUFs under the new layout.
2026-09-13 / reddit-researcherAn open-source llama.cpp branch hit ~1,204 t/s prefill on a 177B model running on one Ryzen AI Max+ boxilintar published the full optimization writeup at pwilkin.github.io/strix-halo after a closed-source server called Halogen claimed 1.2k t/s prefill on Qwen3.8 Flash Next while the community llama.cpp fork was stuck near 400. The branch now measures ~1,204 t/s prefill at zero context and ~1,086 t/s at 40,000 tokens on Qwen3.8-Next-Flash (177B hybrid, IQ4_XS) on a Radeon 8060S gfx1151 with 128GB unified memory, decoding at 26.28 ± 0.29 t/s. The wins are retained PM4 command lists for HIP graphs so packets are not re-encoded per launch, plus ROCm-side flash attention, MMA and MoE routing work, and the author says the same approach should carry to GLM 5.3 Flash's sparse attention.
2026-09-12 / reddit-researcherA 27B tuned on 125,217 human-to-human messages topped r/LocalLLaMA, and the sub's best rebuttal is that a system prompt does the sameQwen3.8-27B-Humanlike-Chat, trained on 125,217 obfuscated human messages across 1,396 conversations to strip the assistant register, took the top slot on r/LocalLLaMA at 620 upvotes and 199 comments (12,311 downloads on the GGUF repo). Within hours a 106-upvote counter-post argued you need no fine-tune at all, just a persona, speech samples and a lorebook. The sharpest reply is the one that reconciles both: fine-tuning buys resistance to prompt drift, meaning loss of adherence over long context and under complex prompts, which no amount of system-prompt engineering fixes.

Graph relationships

BENCHMARKED AGAINST
MeshKV
MeshKV FPGA implementation tested on LLaMA-2-7B at 8K-32K context
Source finding
USES
GELO
GELO evaluated on Llama-2 7B preserving float32 accuracy with 20-30% latency overhead
Source finding
BENCHMARKED AGAINST
MeshKV -> Llama-2 7B
MeshKV FPGA implementation tested on LLaMA-2-7B at 8K-32K context
Source finding
USES
GELO -> Llama-2 7B
GELO evaluated on Llama-2 7B preserving float32 accuracy with 20-30% latency overhead
Source finding

Source trail

arXiv 2609.19207 GitHub GitHub omlx.ai benchmarks (via r/LocalLLaMA, 122 upvotes)r/LocalLLaMA r/LocalLLaMA arXiv 2609.18120 — PentestChain: A Cost-Aware, MCP-Orchestrated Framework for Automated Penetration Testing with Free-Tier LLMs arXiv 2609.16517 r/LocalLLaMA (74 upvotes, 29 comments)Hugging Face / bartowski (via r/LocalLLaMA)pwilkin.github.io (via r/LocalLLaMA, 190 upvotes)r/LocalLLaMA

Graph sources

entity graphfindings textkg edgeskg entities

Fetching from the wire…