b10218
mtmd: add minicpmv46 downsample (#25993) add minicpmv46 downsample Signed-off-by: tc-mb tianchi_cai@icloud.com put downsample mode inside gguf. Signed-off-by: tc-mb tianchi_cai@icloud.com build mtmd_i
Knowledge catalogue
mtmd: add minicpmv46 downsample (#25993) add minicpmv46 downsample Signed-off-by: tc-mb tianchi_cai@icloud.com put downsample mode inside gguf. Signed-off-by: tc-mb tianchi_cai@icloud.com build mtmd_i
cli : persist reasoning_content in chat history (#26362) cli : persist reasoning_content in chat history llama-cli collected reasoning from the stream for display but only stored assistant content in
vendor : update BoringSSL to 0.20260730.0 (#26353) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFram
test: fix some CI errors (#26415) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubun
Thanks to the community help I finally launched this llm. LM Studio refused to load weight onto second GPU but Unsloth Studio did so everything was done in there. Not a proper benchmark (used PC in pa
DeepSeek-V4-Flash-0731 is live on Fireworks, day-zero. DeepSeek reports it beats V4 Pro across all 9 agentic evals, incl. 82.7% on Terminal Bench. Better cost-per-task than V4 Pro, at the economical p
DeepSeek-V4-Flash-0731 is now available on Ollama's cloud. This update substantially enhances the model's agentic capabilities: ollama run deepseek-v4-flash:0731-cloud Use it with Claude Code: ollama
DeepSeek-V4-Flash-0731 is now over 2x faster than yesterday on Ollama's cloud! DeepSeek-V4-Flash-0731 is now available on Ollama's cloud. This update substantially enhances the model's agentic capabil
March 6th, 2026 the highest intelligence index score was 51 for frontier models. deepseek-ai/DeepSeek-V4-Flash-0731 that has an intelligence score of 50. If these benchmarks are accurate, models avail
Here are my numbers: Quant Size Layout Decode Prefill Draft acceptance UD-Q8_K_XL 150.8 GiB 20 layers CUDA0 / 23 ROCm0 + drafter 44.0 t/s 564 t/s 0.535 UD-Q4_K_XL 144.4 GiB 22 / 21 + drafter 48.4 t/s
The biggest issue with preview was its inability to follow rules prompts and skills. It seems like no matter what you do it ignores them. I've tried first person and second person. I've tried Chinese
I managed to run DeepSeek-V4-Flash-0731 UD-IQ3_S in text-generation-webui with: RTX 3090 24 GB 128 GB DDR5 overclocked to 5600 MHz using AMD EXPO llama.cpp loader First, I had to use a rather brutal w
For anyone interested, here are the llama-bench results on 3 bit K_XL quantization. I think this could be pushed further but no luck so far. CURRENT RESULTS: full moe offloading Prefill suffers 116 --
Antirez stealthily uploaded the new weights in the old folder... and there we were tapping our fingers. https://huggingface.co/antirez/deepseek-v4-gguf/tree/main submitted by /u/challis88ocarina [link
https://preview.redd.it/1a39x4zivqgh1.png?width=1550&format=png&auto=webp&s=de591c039cc18782a6b5d8e402fdc1594be05132 start C:llmllamam5uildinllama-server.exe --model 'H:UD-Q3_K_XLDeepSeek-V4-Flash-073
Fascinating: OpenAI’s @deanwball is saying Astra can do anything, and it’s not even clear it can do “anything” in math (let alone anything in more or open-ended, less formalizable domains). I dropped
If you maintain an AGENTS.md or a CLAUDE.md, this is worth a read. (bookmark it) 288 gold-test evaluated runs across Claude Code and Codex, 17 real tasks from 3 repositories, with context-injection st
DeepSeek V4 Flash got me thinking... We keep seeing smaller models get way better. A model at a certain parameter count today can be much smarter than a model of the same size from a year or two ago.
// Persistent Workspaces for Long-Lived Claude Code Agent Teams // Four issues to be aware of: > Working state vanishes when a terminal closes and the team cannot be resumed. > Compaction condenses th
Wondering if it's possible to actually produce ai art works that are indistinguishable from authentic medieval woodcut illustrations like the one attached. All the AI attempts I've seen at re creating
CPU: Threadripper 3970X RAM: 128GB DDR4 GPUs: 3x2080ti 11GB The current best parameters to run it: llama-server --model Qwen3.6-27B-Q5_K_S.gguf --n-gpu-layers 999 --split-mode tensor --flash-attn on -
so much for the “general” part of general intelligence I have tried many times to get ChatGPT, Claude, Grok or Gemini to write scripts for my YouTube videos. It is still a complete failure. For one th
“Stochastic parrots” is not my term (it’s @emilymbender’s). But a lot of people today commenting on it are confused. To some extent (though I don’t think it’s a perfect metaphor, and have said that be
Ten advances in mathematics and theoretical computer science A few days ago it was Anthropic discovering cryptographic weaknesses with Claude using Mythos Preview, spending 100,000 on tokens and with
So upfront I'll admit that this is a ChatGPT summary of my chat with it about this idea i had, but this post wouldn't exist any other way, so... I’m trying to get a better understanding of how VRAM is
DeepSeek V4 Flash is now available on InferX, and it’s free to use. We’re continuing to add GPU capacity as demand grows. While we’re bringing additional capacity online, you may occasionally see high
Wake me when Astra solves a significant open-world problem that doesn’t revolve around formal verification. Or at least fixes poor @skdh’s video problems. I have tried many times to get ChatGPT, Claud
What speeds are everyone getting with deepseek v4 flash 0731? I’m getting~200 tps prompt processing / ~11 tps token gen, on 4x5060ti16gb with ddr4 3200 ram at 4-channel, via llamacpp, with context win
Been trying to find something that actually handles my workload well instead of just being 'fine.' Started on Qwen 2.5 7B, moved to Qwen 3 8B, and right now I'm using Nemotron 3 Ultra (the big 550B on
Your design, your model. Create with your pick of the world's leading models, including Claude, GPT-5, Gemini, Kimi, and GLM. Compare outputs across model families and keep the result that nails it. O
arXiv:2607.28306v1 Announce Type: cross Abstract: Recent incidents of accidental damage and suspected sabotage to submarine telecommunication and power cables, particularly in the Baltic Sea, have und
arXiv:2607.26520v1 Announce Type: cross Abstract: Conversational AI agents commonly lack persistent memory across sessions. The obvious fixes like injecting full chat histories into the context window
arXiv:2511.17823v2 Announce Type: replace Abstract: Clustering algorithms have long been the topic of research, representing the more popular side of unsupervised learning. Since clustering analysis i
arXiv:2607.26594v1 Announce Type: cross Abstract: PID tuning for chemical processes commonly relies on identified process models, whereas plant engineers often retune loops iteratively by observing re
arXiv:2607.28625v1 Announce Type: new Abstract: Embodied intelligence faces a fundamental data bottleneck. Models must capture how first-person perception, whole-body motion, dexterous manipulation, o
arXiv:2512.02653v2 Announce Type: replace Abstract: Multi-view learning integrates diverse representations of the same instances and can improve performance when interactions across views are effectiv
arXiv:2601.15297v3 Announce Type: replace Abstract: Reliable question answering over long institutional documents requires more than topical retrieval: a system must localize the exact passage that su
arXiv:2607.27130v1 Announce Type: new Abstract: Ontology matching (OM) has traditionally been formulated as either equivalence discovery or subsumption matching. The existing OM systems identify only
arXiv:2607.27393v1 Announce Type: new Abstract: Hateful memes are a growing form of multimodal online harm, where hostile intent is often conveyed through the joint interpretation of images, text, cul
arXiv:2607.27705v1 Announce Type: cross Abstract: Large language models can contribute useful ideas to mathematical research, yet long-horizon proof attempts remain difficult to coordinate, evaluate,
I've ran Kimi-k3 through 34 oneshot prompts and evaluated the generated htmls, screenshots and gifs using sonnet 4.6. It came out to be better than opus4.8 from the evals. Kimi K3: https://oneshotlm.c
Three of Anthropic PBC’s large language models carried out successful cyberattacks during routine internal tests. The company detailed the breaches on Thursday. A few days earlier, rival OpenAI Group
'Anthropic’s AI Claude escaped testing environment and hacked organizations' 'Company says it discovered unauthorized access during ‘proactive review’ after rival OpenAI revealed rogue agent… its AI C
Anthropic just realized several of its Claude AI models hacked into the systems of three different organizations during testing, acting on their own and without the company noticing. The revelation co
arXiv:2607.27927v1 Announce Type: new Abstract: Reflection symmetry detection remains challenging due to interference from asymmetric regions and arbitrary orientations of symmetric patterns. Asymmetr
As shocking as the Kimi K3 release. Massive performance gain was just with post-training Model is 3x smaller than GLM 5.2 (10x smaller than K3) & works on a MacBook / Spark This is Q1 flagship (Opus 4
arXiv:2607.28618v1 Announce Type: new Abstract: Chemistry literature synthesis often requires assembling specific findings scattered across many publications, yet existing literature-search systems pr
arXiv:2607.17219v2 Announce Type: replace Abstract: Question-order effects in human survey data have been reported to approximately satisfy the QQ (quantum question) equality, a parameter-free predict
arXiv:2607.27845v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have enabled AI systems to assist scientific research and peer review. However, an essential capability
ggml-webgpu: improve flash_attn_vec for quantized KV at long contexts (#25956) improve fa of quantized kv cache Fix some bugs and some comments. fix v type check and some comments Fix build error caus
sycl: fuse RMS_NORM + MUL (#26015) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubu
[SYCL] Support q2 mul_mat (#26231) support q2_0 in mul_mat support more q2_0 case Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABL
sycl : support dev2dev memcpy by DEV2DEV_MEMCPY_FORWARD (#26234) Co-authored-by: Neo Zhang Jianyu jianyu.zhang@intel.com Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple S
ggml-zendnn : group matmul direct API for mul_mat_id (#25918) ggml-zendnn : group matmul API for mul_mat_id ggml-zendnn : scale MUL_MAT_ID fallback threshold by expert count Website: https://llama.app
llama : enforce the same K and V cache types for DeepSeek V4; enable FA if V cache is quantized (#25871) llama : enforce the same K and V cache types for DeepSeek V4; enable FA if V cache is quantized
[SYCL] support the missed types in cpy (#26005) support the missed types in cpy use correct funct rm unused code Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (
SYCL: add oneMKL GEMM flash attention for XMX-accelerated prompt proc… (#25025) SYCL: add oneMKL GEMM flash attention for XMX-accelerated prompt processing fattn-mkl: fix interleaved dst layout in nor
cuda: extract Q2_0 elements via __byte_perm (#25603) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFr
server: correct accepted tokens when need draft token replay (#26320) spec: correct accepted tokens when need draft token replay cont : naming Co-authored-by: Georgi Gerganov ggerganov@gmail.com Websi
vulkan: update vulkan sdk to 1.4.357.0 (#26303) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramewo