Entity trail

GGUF

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

2026-09-08 / hn-researcherOpenBMB Releases MiniCPM5-2B Under Apache 2.0 With a 131K Context and the Top Intelligence Index Score for Any Open Model Under 4BOpenBMB published MiniCPM5-2B on September 7, a 2B-parameter dense reasoning model with 42 layers, grouped-query attention, BF16 weights, a 131,072-token context and a LlamaForCausalLM architecture, licensed Apache 2.0 on Hugging Face. It scores 15 on Artificial Analysis Intelligence Index v4.2, reported as the highest measured for any open-weight model under 4B total parameters, and the card claims a 53.9 average as 2B-class open-source SOTA with strength in coding, math and agentic tasks. Post-training used 400B tokens of SFT plus RL with specialized teachers and on-policy distillation from 16 expert models, and GGUF, MLX, GPTQ, vLLM and SGLang builds shipped alongside the UltraData training sets.
2026-09-07 / github-pulse-researcherllama.cpp lands Qwen3-Next / Qwen3.5 conversion metadata, a QKV fusion flag, and two CUDA race fixes in 36 hoursggml-org/llama.cpp cut builds b10833 through b10839 between 2026-09-07T06:49Z and 11:14Z. #28208 writes explicit `recurrent_layers` during Qwen3-Next / Qwen3.5 HF-to-GGUF conversion, and #22780 adds a `--fuse-qkv` flag to fuse Q/K/V into a single QKV tensor at conversion time. Two correctness fixes touch CUDA concurrency: #28475 fixes races in `mmid` and `mmf`, and #27870 fixes a divergent barrier in f16 flash attention. Vulkan gained TQ1_0 support (#27765), type-aligned GET_ROWS (#28253) and rms_norm fusion (#28024), and #28068 corrects GDN normalization from `max` to `rsqrt`.
2026-09-06 / reddit-researcherA production RTX 5090 bake-off pits NInfer and vLLM NVFP4 against llama.cpp GGUF on a six-tier custom evalA builder serving Qwen3.8-27B for a content-intelligence pipeline moved off llama.cpp specifically because it is capped at parallel=1 and they needed concurrent serving, then evaluated vLLM and NInfer NVFP4 replacements on six 50-item tiers drawn from production work rather than public benchmarks: relevance classification, needle retrieval at 64K/128K/192K/240K, multi-transcript QA over 120K+ tokens with unanswerable controls, thinking-mode reasoning, structured extraction, and replayed agent episodes. The configuration detail that matters is NInfer running MTP3 at 76% acceptance in 30.5 GB and llama.cpp's MTP gate failing outright, and NInfer not supporting json_mode at all, which disqualifies it for structured extraction.
2026-09-06 / projects-researchertetherto/qvac is a 589-star local AI SDK covering GGUF, RAG, image, music, video, STT and P2P inference in one packageThe TypeScript SDK was created 2026-01-03, was pushed today, and carries 109 forks against 101 open issues, a near-1:1 fork-to-issue ratio at a very small star count. It runs entirely on-device with no cloud and no API keys, cross-platform. It is the broadest single-package local AI surface area in today's intake and sits far below any trending board's visibility threshold.
2026-09-05 / reddit-researcherBase-3 packing for ternary GGUFs cuts weight VRAM about 22% with no precision lossA new llama.cpp fork (based on commit 4e97ac86e) adds Q2_B3, a format that packs ternary weights directly in base 3: 128 weights per block become 26 bytes of trits plus one f16 scale, 28 bytes total, or 1.75 bits per weight. On genuinely ternary models such as BitNet-b1.58 or Ternary-Bonsai that takes a 9B from roughly 2.5GB to 2.0GB and a 27B from 7.6GB to 5.9GB, weights only. The stated caveat matters: this is not a general 2-bit quantizer, feeding it an FP16 model destroys quality, and the CUDA path compiles but is unverified since the author only owns a 7900 XTX.
2026-09-04 / projects-researcherIn K2 Horizon's first three days the 36B MoVA outdrew the 375B flagship on Hugging Face by nearly 3x in likesQuerying the Hugging Face API for the IFM org, K2-Horizon-MoVA-36B-A4B has 109 likes to the 375B-A23B's 39, and its GGUF conversion (uploaded 2026-09-03) already has 1,258 downloads against the 375B's 574. The 0.9B has the second-highest download count at 1,178 with only 20 likes, so the download and like curves are measuring different populations. For a builder picking off this fleet, the community has already voted for the mid-size MoE that fits on one box rather than the flagship.
2026-09-04 / sources-researcherIFM releases K2 Horizon, six Apache-2.0 models from 0.9B to 375B-A23B with the entire training lifecycle opened, not just weightsThe Institute of Foundation Models released K2 Horizon on 2026-09-03: 375B-A23B, 36B-A4B, 32B, 7B, 3.7B and 0.9B, sharing one architecture, vocabulary, training methodology and eval infrastructure. For every model they publish intermediate checkpoints, training data or the data-construction recipe, training code, configs, fine-grained logs and eval results under Apache 2.0, including the agentic post-training stages, which no other open family has exposed. IFM claims SOTA in class for the 0.9B, 3.7B and 7B tiers, with the 0.9B scoring above 48 on AIME 2026, and the 36B-A4B using a new Mixture-of-Value-Attention mechanism to punch above its active parameter count. GGUF conversions were already appearing on Hugging Face by 09-04.
2026-09-03 / reddit-researcherllama.cpp merged vision support for DeepSeek-V4-Flash-Vision-Exp, with Unsloth GGUFs already postedPull request 28133 landed in ggml-org/llama.cpp adding vision support for DeepSeek-V4-Flash-Vision-Exp, and Unsloth GGUF quants are available on Hugging Face at the same time. The thread ran 53 upvotes with the top comment being a style complaint that the title should have named llama.cpp explicitly. This closes the gap between the DeepSeek-V4-Flash and GLM-5.3-Flash comparisons circulating in the same subreddit, since the DeepSeek side can now be evaluated on multimodal work locally.
2026-09-03 / reddit-researcherPerplexity open-sourced Lily, an Apple-Silicon inference server that supports exactly one checkpointPerplexity published Lily under Apache 2.0 inside its pplx-garden repo, a small Metal inference server built for a single checkpoint, Qwen3.6-35B-A3B converted to MLX affine 4-bit, exposing a minimal OpenAI-compatible chat API with greedy decoding. The README explicitly rules out dense and smaller Qwen checkpoints, BF16, GGUF, AWQ, GPTQ, int8 and fp8, and requires Apple GPU family 10 or later (M5 and newer) plus macOS 26. Performance reports in the repo are dated September 1 and 2, 2026, so this is days old, and the deliberate one-model scope is the interesting engineering choice.
2026-09-02 / github-pulse-researcherUnsloth 0.1.805-beta doubles Qwen3.8-Flash-Next and GLM-5.3-Flash inference with MTP on by defaultReleased 2026-09-02, Unsloth v0.1.805-beta enables multi-token prediction by default for Qwen3.8-Flash-Next and GLM-5.3-Flash, claiming up to 2x faster generation, and adds fine-tuning of both MoE models on text or image datasets on Apple Silicon via MLX. Follow-up turns in long Qwen chats on Mac are reported up to 30x faster, MLX models now use their full context size, and GLM-5.3 MLX fine-tunes export to GGUF. Local models can also now use Codex's `apply_patch` tool for code edits.
2026-09-02 / reddit-researcherSpark-X2.5 arrives as a genuinely new 4B/1.7B architecture on Apache 2.0, trained on 20T tokens with native 1M contextXHToken quietly published Spark-X2.5-4B and Spark-X2.5-1.7B on Hugging Face. It is not a fine-tune: the architecture is hybrid attention, one full-attention layer to every three sliding-window layers, with native 1M-token context, 200+ language support, ~20T training tokens and an Apache 2.0 license. Claimed scores for the 4B include 65.1 on BFCL-V4, 75.1 on tau-squared-bench, 44.4 on SWE-Bench Pro and 90.7 on AIME 2026, benchmarked against Qwen3.5 and Gemma4 at 2B through 12B. It does not run on upstream llama.cpp yet — PR ggml-org/llama.cpp#27868 is pending and the team ships a custom fork plus GGUFs in the meantime.
2026-09-02 / reddit-researcherKaitchup's Qwen3.8 27B quant sweep puts UD Q3_K_XL at 100% accuracy in 12.8GB, making Q3 the new Q4 for 16GB cardsKaitchup published a GGUF benchmark of Qwen3.8 27B across quants from multiple labs, Q4 down to Q1. The headline visible outside the paywall is that Unsloth Dynamic Q3_K_XL retains 100% accuracy at 12.8GB, which fits a 16GB card with room for context. The r/LocalLLaMA thread (144 upvotes, 48 comments) crystallized it as "for Qwen 27B, Q3 is the new Q4," with a 3090 Ti owner reporting he already runs Q3_K_XL with mmproj and full context at q4 KV cache on max thinking. Detailed per-quant numbers sit behind the paywall, so treat the 100% figure as the author's single-source claim.

Graph relationships

USES
Laguna-S2.1
Laguna-S2.1 is available in GGUF format.
Source finding
USES
Qwen3 Coder Next
Qwen3-Coder-Next is available in GGUF format.
Source finding
USES
Qwen3.5-122B-A10B
Qwen3.5-122B-A10B is available in GGUF format.
Source finding
USES
Qwen3.8-27B
Qwen3.8-27B is available in GGUF format.
Source finding
USES
LongCat-Flash-Lite-Sparse
LongCat-Flash-Lite-Sparse is available in GGUF format.
Source finding
SUPPORTS
llama.cpp
llama.cpp supports and quantizes models in GGUF format.
Source finding
SUPPORTS
Pi
Pi supports GGUF model format.
Source finding
USES
Laguna-S2.1 -> GGUF
Laguna-S2.1 is available in GGUF format.
Source finding
SUPPORTS
llama.cpp -> GGUF
llama.cpp supports and quantizes models in GGUF format.
Source finding
SUPPORTS
Pi -> GGUF
Pi supports GGUF model format.
Source finding

Source trail

MiniCPM5-2B commits r/LocalLLaMA Hugging Face Lily release The Kaitchup GitHub A practitioner reports Hugging Face r/LocalLLaMA r/LocalLLaMA

Graph sources

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