b11364
model: support nimble decision model (#29844) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/52332773 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Si
Knowledge catalogue
model: support nimble decision model (#29844) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/52332773 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Si
ggml-cpu : fix soft_max_back wrong output when dst aliases src1 (#27096) ggml-cpu : fix soft_max_back wrong output when dst aliases src1 GGML_OP_SOFT_MAX_BACK is listed in ggml_op_can_inplace, so the
ggml-quants : avoid invalid rounding in qkx3 scale search (#29817) ggml-quants : avoid invalid rounding in qkx3 scale search The imatrix scale search can produce an infinite, NaN, or otherwise out-of-
spec : add probabilistic sampling for simple draft and MTP (#27694) Make the drafter probabilistic and the target verify by rejection sampling Drop stale spec_draft_q before drafting Fallback to argma
CUDA: fuse shared experts into MMVQ (#29184) CUDA: fuse shared experts into MMVQ check if buffer is null move stride_col_dst to fusion args Website: https://llama.app Attestations: https://github.com/
model: add support for clef decision model (text-only) (#29831) init support for clef (text only) more static graph clean up nits nits 2 Update gguf-py/gguf/constants.py Co-authored-by: Sigbjørn Skjær
qwen4exp : halve the indexer score memory (#29825) qwen4exp : halve the indexer score memory The indexer scored all heads in one product and rectified a copy of it, so two [n_pool, n_idx_h, n_tokens]
ggml-openvino: update to 2026.4.1, optimize performance, expand ops, improve device listing. (#29852) ggml-openvino : Qwen3.5 MoE perf (#312) Squash of ravi9#312: ggml-openvino: add detailed inference
graph: gather the recurrent states once so the reserve covers every split (#29856) build_rs gathered the extra states (n_rs - n_seqs rows) with their own get_rows. The worst-case reserve has n_rs == n
ci: fix flaky ADD_ADD f16 by using the fused ADD tolerance (#29904) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/52418791 macOS/iOS: macOS Apple Silicon
chat : honor json_schema in Ling 3.0 parser (#29813) chat : honor json_schema in Ling 3.0 parser Ling 3.0 only built a grammar for tool calls and did not handle inputs.json_schema, so response_format
common : add common_is_tty() helper and fix deprecated warnings on Windows (#29860) Signed-off-by: Adrien Gallouët angt@huggingface.co Website: https://llama.app Attestations: https://github.com/ggml-
server : fix laya abort by limiting n_batch to n_ubatch (#29903) server : fix laya abort by limiting n_batch to n_ubatch Fixes #29902 Assisted-by: Claude fix(review) : rm tests, embeddings cond Websit
vendor : update cpp-httplib to 0.59.0 (#29886) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/52476117 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple S
mtmd : fix deprecated strdup warning on Windows (#29863) Signed-off-by: Adrien Gallouët angt@huggingface.co Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/
Still don't have a good cooling solution, but here are few benchmarks. I lowered the power limit (TDP) to 145 watts each. I changed the firmware on one MI50 to activate the miniDP port. Did have to us
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Reupload: I'm adding this again, since previous post had a totally stupid title which made people assume this app is some kind of cloud/proprietary models UI - this is totally not the case here. In ve
There are quite often posts here complaining that Krea 2 is too predictable / different seeds give almost the same output / you need to type in extremely long prompts to get good images / there is no
This is actually two models: Qwen Image 2.1 Fix v2.0 and Qwen Image 2.1 Fix Opinionated v1.0. The former surgically targets Qwen's weird noise and messy details with almost no effect on composition, a
Benchmarking an LLM here with a NVIDIA RTX 5070 12 GB VRAM here I had been working on a llama.cpp based expert streaming setup for Qwen3.8-Flash-Next 177B (UD-IQ3_XXS) on Windows. Benchmark is about 1
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I wanted to see how far I could push a fairly ordinary laptop with a huge MoE model. Turns out, Qwen3.8 Flash Next 176B can run on: RTX 3080 Laptop — 16GB VRAM 32GB system RAM SSD No 128GB/256GB RAM w
The Economist's article on Effective Altruism said that some EAs 'even think the welfare of AIs themselves will one day be of vital importance.' It's not just some random EAs; it's coming from the mor
Hi guys, I am pretty happy to announce MegaCapybara. Purpose build engine for RTX5090 that is focused on Qwen3.8 27B (more will come later). GITHUB (Engine) HUGGINGFACE (weights) Why ? 1. It beats Nin
There seems to be a whole category of extremely narrow inference runtimes appearing: Strata, ninfer, DwarfStar, Splash, llamAmpere, gufo, etc. They deliberately give up the thing llama.cpp/vLLM are gr
Innanzitutto, due cose per evitare fraintendimenti. Non sto cercando di sostenere che un modello o un'azienda siano migliori. Non ho alcun interesse personale in nessuno di essi. Volevo solo verificar
https://unigen-x.github.io/unifolm-wla.github.io/ Unitree Robotics released UnifoLM-WLA-1.0, their new general-purpose humanoid foundation model. Key points: • 6B parameters • Trained on ~2,500 hours
🎵 Wave of distillation 🎵 US labs say Chinese labs distill their models. And Europe's new “sovereign model” is fine-tuned on data generated by… GLM and Qwen. Small bird, fast wings, Kolibri is here. 78
I've been using GPT-6 Astra to create 70+ page documents from 100,000+ characters of source material, and it's jump in processing is great. Here's the weird part, when I give it a massive task it work
I’m more used to not interacting with AI so much, and when I do it’s usually Claude who is polite but acts more like a coworker or professor, it’s critical of my ideas and functions more helpfully. Re
I want the smartest local model I can run that also does not refuse. Not a tiny 'uncensored' 8B. Not a censored model with a jailbreak prompt. Tried dolphin-llama3:8b with a custom SYSTEM prompt. It c
What modle works on a 16 GB laptop i5 intel I have an old machine lying around and tried running a few models and non seem to be good, the response time is 4 mins and above Models tested: Google Gemma
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