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Can you still do something with a 2050 or something like it? I mean for office work, loading embedding, reranking and chat models not at the same time but is anyone still using smaller models and have any good ones come out? I feel like small models are abandoned, I don’t care much for world knowled
I wonder someone will figure out a way to do this with 27B? Throw Qwen3 on this page for demo https://kishida.github.io/webdemos/llkvapprox/ Edit: sources (thank you u/pmttyji for finding them! Blog Post : https://nowokay.hatenablog.com/entry/2026/09/11/120001 HuggingFace : https://huggingfac
After noticing that it is ranked among MUCH larger frontier models in the EQ-Bench Creative Writing benchmark and the Hemingway-bench, I decided to give it a try and was very impressed. I didn't do very formal testing, but I did ask it to emulate the style of several different writers, including He
Github Repo. Blog post. 💡 TL;DR (from the Github Readme) Spend less without making the agent do less useful work. SoL-Pi is a standalone extension for Pi that packages four reusable efficiency mechanisms discovered through scaled auto-research loops. It reduces repeated model turns, context replay,
Title says it all. https://github.com/gjabdelnoor/Day1DeepseekV4.1-CPU The goal is pretty simple, I like having infinite slow tokens from the bioinformatics machine in the lab to run overnight or over-week agentic jobs, paired with a watcher that kills it in 15 seconds if someone else needs it for
https://preview.redd.it/3z56sdq1yqoh1.png?width=1362&format=png&auto=webp&s=5c9848a396a4bd2ec472a6b4761f90263feb44cc It's on par with 5.6 sol but for <1/10th the price on their evals submitted by /u/ihexx [link] [comments]
The downside of uncensoring a model is that it is known to potentially damage it, but CyberTiel is an even more capable software engineer than its censored TielCoder base, while allowing offensive security research. This was achieved by quantizing with an improved imatrix, baked from a curated
submitted by /u/paf1138 [link] [comments]
Long time lurker, but I'm finally upgrading to 128GB VRAM, and I'm trying to figure out what to run. I had been leaning towards Qwen3.8 Flash-Next at ~Q4, and I generally prefer to not run anything below Q4. But I feel like the reception to Flash-Next has been a bit "meh", so I'm considering GLM 5.3
Hey y'all! We've released a new model in our lineup: GigaChat-3.5 Reasoning. It's a 432B-A28B MoE with Gated DeltaNet for long-context efficiency. We trained domain experts (code, math, general, etc.) with CISPO and then distilled them into a single model via on-policy distillation. In our eva
I like to benchmark new models that come out on motion videos. So here's a test I did for deepseek v4.1 flash. And I have to say flash has probably graduated from being a Luna class model to nearly an Opus class model with this release, at least with motion videos. Prev. example I did with Kim
submitted by /u/incarnadine72 [link] [comments]
Hoping to see smartest medium size models soon & later with all available optimizations/architectures/etc.,. Thanks Deepseek! Ex 1: 30-50B MOE + 10-15B Engram + DeepSeek-V4.1-Flash type KVCache Ex 2: 15-30B Dense + 10-15B Engram + DeepSeek-V4.1-Flash type KVCache EDIT: Updated Engram to 10-15B
People keep on getting confused about this, so I looked at the safetensors on hf. To be precise, the main model about 551.566B parameters with 40 layers. The FFN experts total to 543.582B parameters, and the rest of the model (attention, shared experts, etc) are 7.984B. Huggingface lists the model
OpenAI has decided to fully shut down a protein design project I'm working on for a client. Needless to say, open weight models are the only way forward. submitted by /u/Terminator857 [link] [comments]
Here we go again, DeepSeek is back again with a new model V4-1 Flash A multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for contexts of up to one million tokens Market crash as a service submitted by /u/tiguidoio [link] [comments]
Original Source from DeepSeek WeChat Official Account: https://mp.weixin.qq.com/s/qg0NU3NNUbp1co2PdkAPAg Today we're officially releasing the DeepSeek V4.1 Flash model. It is the smallest model in our brand-new model architecture series, with native multimodal visual understanding. The new ar
submitted by /u/t4a8945 [link] [comments]
Getting anywhere from 10 - 20 tps. Thinking Off . Took about 4 mins and 7 mins. Running on about "IQ3_S - 3.4375 bpw" 37.03.960.932 I slot print_timing: id 0 | task 2665 | prompt processing, n_tokens = 12516, progress = 0.98, t = 37.08 s / 337.55 tokens per second 37.04.777.411 I slot print_t
I run these models on Kaggle notebook, so not all TTS models, such as the ones that use conda env, are compatible (Or I just haven't found a way for them to work on Kaggle). I currently use a fork from Chatterbox called Chatterbox Audiobook. It is like a workstation really optimized for getting the
Cosmos3 INT4 T2I + I2V on Apple Silicon — code, weights and a Grok comparison GitHub - https://github.com/gtrg55/cosmos3-quant-mlx-cuda HF weights - https://huggingface.co/JuliaML/Cosmos3-Super-Text2Image-4Step-INT4-G64-BF16 Single clip took approximately 5m on M4 MAX 128 GB Mac Cosmos3 - a 64
Model available here: Qwen3.8-27B-Uncensored-Genesis-V1-MTP-GGUF This model is a practical realisation of things described in this paper, but adapted by me for machine learning: https://arxiv.org/pdf/1311.0851v1 I am trying to solve the problem: why LLM models even for simple questions write walls o
I have been a dwarfstar fan for awhile and I really liked glm 5.3 flash but needed it to be materially faster to feel good using it. In the screenshot you can see the outcome of using the model with a claude code harness at ~200k depth, with many tool calls and averaging over 38tps output. Yes
Running the full deepseek-ai/DeepSeek-V4-Flash-Vision-Exp on consumer Ampere — 10-12x RTX 3090, SM86-compatible vLLM build. 285B MoE, FP4 experts + FP8 attention, 157 GB weights. Highlights: - **60+ tok/s** decode, DSpark spec (k=3) on 10 GPUs (TP2xPP5), at a 240 W cap - **120+ tok/s** on 12 GPUs (T
In our chat app "harness" we recursively generate summaries, L1 → L2 → L3. L1 summaries are more factual extraction than coherence, then get rolled into a more storytelling L2. Then we keep a tail of always ~10 raw messages with timestamps. We're not coding so this keeps context really clean like 5-
new model of nex benchmark seems like good https://huggingface.co/nex-agi/Nex-N2.5-Max submitted by /u/Lordaizen639 [link] [comments]
From the email "Early access to new models During the program, approved users can try two next-generation Xiaomi MiMo models for a limited time: MiMo-X-Pro-Preview and MiMo-X-Flash-Preview. The MiMo-X series is optimized for real professional workflows, with a focus on complex reasoning, multi-agent
I'm pretty sure I still wont be able to run it, but I wanna try dammit! submitted by /u/thebadslime [link] [comments]
https://www.reuters.com/technology/us-accuses-chinese-ai-firms-industrial-scale-theft-ai-technology-2026-09-08/ https://www.nbcnews.com/tech/tech-news/us-accuses-china-ai-developers-deepseek-alibaba-copying-american-ai-rcna596696 submitted by /u/External_Mood4719 [link] [comments]
Hi, I'm the co-creator of this Qwen3.8-Flash-Next engine support in MLX-serve. I've been tuning this one to run both fast, efficient and correct up 1m context using kv cache 8 bits in M5 Max 128GB. Qwen is working well at very long context as showed in the video (a snapshot at ~760k context),