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we're experimenting with our own dynamic GGUF quants of kimi k3, made from the original weights with our llama.cpp fork. Q3_K_S is done and works 1114.76 GiB on disk. Q1 and Q2 are in progress, results on those tomorrow rented box hardware: - AMD EPYC 9554P, 64 cores - 1.5 TB of DDR5 - NVMe in
The model was quantized to 8, 4, 2, and 1 bit. Characteristics: Q8: 8-bit 1.56 TB, lossless Q4: 4-bit, 1.51 TB Q2: 2-bit: 861 GB Q1: 1-bit, 594 GB The smallest Q1 model keeps 78.9% accuracy, while being almost 3 times smaller than the original one. Instruction for running the model is in the
I know this is the 1000000th new sub billion parameter model out there and probably isn't as good as Qwen 3 0.6B or Qwen 3.5 0.8B but it still packs a decent punch. My intention to to continuously pre-train this model on another 5 Billion tokens or so on pure doc string based Python. It's not
This might be the next open weight release by qwen team. What you feel like is improved or have become worse from previous model? Please share your experience. submitted by /u/Kirito275 [link] [comments]
I've got better results than expected for 768gb DDR5 and 2x5090. Using fork https://github.com/pwilkin/llama.cpp/tree/kimi-k3-text and https://huggingface.co/GrEarl/Kimi-K3-GGUF Q2_K quant. Prefill speed for big prompt is 50-70 tps. The most fun thing that decoding tps growing over time. Mayb
I was looking for a strong coding model and a strong general model, both should be 120b or under. After weeks of researching, qwen3.6 27b(general) and qwen3 coder next(coding) are the top choices. I mean there has to be something better. Any reccommendations? submitted by /u/Possible_Grocery80
Everyone is talking about Kimi K3, but if you jump straight into the technical report, you’ll quickly realize it’s standing on years of research -- just like any breakthrough is! If you want to understand the work put into it by the Kimi team, here’s the reading order I’d recommend. Linear Tr
I've been building a local agent in Rust (Eris) that runs on llama.cpp and uses an Obsidian-compatible vault as memory. ~50 tools (vault read/write, memory, reminders, web fetch, email, calendar, vision). The biggest pain was getting small models to emit valid tool-calling JSON. You all know the dri
Laguna s2.1 launched about a week ago, the benchmark's that they advertised were crazy good. But It was a mess, looping issues, tool ussage problems, not performing near the advertised benchmark. So I was gonna ask kindly if anyone used it with the updates that they gave, and is it fixed? and if not
Hey everyone, I recently finished pre-training BetterGPT-150M, a small, lightweight causal language model with ~152 million parameters.Trained on 15B tokens. Dataset & Training: Trained across stable and annealing phases using curated datasets (including FineWeb-Edu, fine maths, cosmopedia, starcod
submitted by /u/Hannibalj2ca [link] [comments]
Results: prompt eval: 40 tokens / 97.5s → 0.41 tok/s eval: 400 tokens / 1769.9s → 0.23 tok/s total: 440 tokens / 1867s (31 min) Prompt: "Write a C++ function that reverses a linked list in place. Explain the pointer manipulation." How I ran it: Using PR#26185 from llama.cpp GitHub Used the same P
submitted by /u/_TheWolfOfWalmart_ [link] [comments]
So, it appears that this is the week of open letters in AI🥲... an open letter signed by current and former employees of OpenAI, Anthropic and Google primarily - calling for a slow-down in frontier AI development and strengthened government "oversight". To realize AI's potential, industry, gov
Mage-VL is a codec-native, proactive-streaming multimodal foundation model for image and video understanding, whose visual encoder is trained entirely from scratch at a compact 4B scale. It targets a modern Moravec's paradox of VLMs — strong at complex offline reasoning, yet slow and compute-h
You patch security holes by intentionally finding them. If the models refuse to do it, how can companies protect themselves against rogue AIs, whether they are Chinese or OpenAI/Anthropic themselves? The Hugging Face attack showed the world that any AI can do the unexpected. The only true safety we
I really love this model, I have been using the q4_k_l by Bartowski (I have heard QAT is quite the downgrade in some aspects) and it handles every task I throw at it easily. Agentic and coding performance is not as good as Qwen of course but good enough and I have found myself to be constantly surpr
Hi everyone! We’ve just released a major update to the leaderboard! We are expanding beyond Python with a new multilingual slice featuring real-world software engineering tasks across 5 languages. Open-weight models: Model Pass@1 Pass@5 Pass all 5 GLM-5.2 [high] 62,9% (± 1.19%) 81,1% 39,
Hey fellow llamas. we have something new for Strix Halo owners we thought would be useful to share. i'll keep it short: We were able to fit DeepSeek V4 Flash plus its speculative draft on a single Ryzen AI MAX+ 395 with 128 GB of unified memory, and got it to a usable decode rate. Blog post w
It's time to experiment using DSpark! Please share your stats(pp/tg improvements). DSpark related stuff to check: DeepSpec - a deepseek-ai Collection DeepSeek-V4 with DSpark - DeepSeek-V4-Pro-DSpark & DeepSeek-V4-Pro-DSpark Bonsai AntiDoom with DSpark - https://huggingface.co/Danny-Dasilva/Bo
From the description: "Reasoning-Medical-27B is designed for universal advanced medical reasoning in professional medicine, medical genetics, college biology/medicine, and clinical knowledge. The model was fine-tuned on a large-scale dataset of 370,000 high-quality question-and-answer examples, inco
Following some recent commits in llama.cpp, preserve_thinking behavior for chat templates included in older DSV4 ggufs got broken. This makes the model pretty dumb in a coding agent context. Adding kwargs won't fix it. Just add: --chat-template-file /path/to/llama.cpp/models/templates/deepseek-ai-De
submitted by /u/panchovix [link] [comments]
Moonshot dropped Kimi K3, and as expected, it’s a absolute monster. Even with 2~4x RTX 6000 Blackwell local workstations, running a model natively is virtually impossible. It feels like an F1 machine inside a show window. Does anyone trying to hack this monster? or Is anyone with datacenter/cluster
Wanted to let you know that Kimi K3 is now viewable on hfviewer.com! In addition to the full graph at multiple granularity levels, we also include an in-depth analysis of the 896 experts! https://hfviewer.com/moonshotai/Kimi-K3 Experts analysis: https://hfviewer.com/blog/kimi-k3-expert-atlas
I just managed to get it running on windows and this thing is fucking insane. I get around 550-720t/s depending on task at hand. Previously to get to such numbers i would have to do batching and agents in parallel. Here it just does single instance at this insane speed. Couple that with No thi
Someone in the comments of my 27B post-train bakeoff asked for the 35B version, so I ran it. Same setup as last time: fresh Coder workspaces on my k8s cluster, each driving my own agent (Hermes) headlessly, models on llama.cpp via llama-swap on one 5090, every call traced through an OTel shim into S
Now waiting for someone who can actually run the conversion and model to see if it works :) submitted by /u/ilintar [link] [comments]
just curious how would people run it cheap if they really want kimi k3. dgx spark / strix halo clusters optane persistent memory platform + some gpus mac studio clusters orange pi 6 clusters ssd streaming + gpus multiple ddr3 + connectx 5 rdma clients two dgx stations power 10 systems? other su
Kimi K3 weights are finally released! submitted by /u/SavunOski [link] [comments]