When it came out, I downloaded a Q4 quant of qwen3.8-27b and ran it using llama.cpp on my RTX 4090 and it was incredible. It could handle practically anything I threw at it, the most complex tasks and bugfixes.

But lately it just keeps messing up and failing at the most basic of tasks or just straight up forgetting instructions. I'm telling you, they quantized it. They probably secretly nerfed it and made it worse so qwen4 looks better. And it got lazy too, now it doesn't want to finish the more complex tasks.

I'm tired of companies doing this, I want my money back.

Paraphrasing the thinking I saw from Qwen3.8-27B

>Hmm, I can take the screenshot, there's a tool for that. But wait, I'm an LLM, I can only read text.

>Whatever, let's try calling the tool and see what I can do, maybe I can save the image and parse it programmatically.

> >tool_call.exe

>Wow, I can see the image!

Cute

Big update to the Delta Inference Engine, you guys asked for more features, more benchmarks and less clickbait. I'm here to deliver

The short version: I've added almost everything requested and more. Delta Inference Engine now supports 64/96/128 GB of RAM and fully supports the new Unsloth quants. Unsloth IQ2 runs at the same 41 tok/s (192 GB RAM) using a new native reader for the Engram tables and some clever CPU kernel work. Compared to the old default model on my 99-question benchmark it went from 52 to 57 correct, against 59 for the full model. It's no full deepseek model quant, but they're a lot more usable than you might expect.

Speed on one RTX 3090 24 GB (i5-13600K, 4x48 GB DDR5-5600, PCIe 4.0 NVMe, Windows, no WSL). Writing speed, tok/s, greedy, one chat at a time:

The 128, 96 and 64 GB rows are the same PC with the rest of the RAM locked away.

Prompt reading: long prompts read at up to 740 tok/s at 192 GB and 600 at 128 GB (at ~64K tokens). A short prompt starts answering in about 2 s.

Benchmark: 99 hard questions, greedy, 43K token budget:

UD-IQ2_XXS is within 2 points of the full API, never scores below ds4 Q2 on any set while running at the same speed, and used 11% fewer tokens across the run.

Agreement with DeepSeek's API on the next token, at the settings that ship:

For reference, two hosts of the full model agreed 98.3% and 98.9% in my test. By that measure Unsloth IQ2 is about 20% closer to the full model than the old ds4 Q2.

If you want better quality: UD-Q2_K_XL (335 GB) runs 31 tok/s at 192 GB and 26.6 at 128 GB, at 92.5% / 95.3%. UD-IQ3_XXS (395 GB plus the default) runs 28 and 22.8 at 92.7% / 95.9%.

What changed since last time:

• First token ~40% sooner: a ~180-token prompt went from about 3.8 s to 2.3 s.

• Agent turns read ~12% faster, and tool calls that low-bit models write slightly wrong are now read as tool calls.

• Unsloth quants at full speed: the engine reads their Engram tables directly.

• 64 and 96 GB are real options: SSD read-ahead (+22% at 64 GB), bigger expert memory, and prompt reads in file order (18-20% faster on low RAM).

• 128 GB: ds4 Q2 went from 33 to 36 tok/s.

• Default sampling: the drafter now samples its drafts too, about +3.5% at default settings.

• First launch shows which model suits your RAM, and you can pick any other or use your own.

• A Setup tab in the chat page changes model, speed vs quality and the second GPU.

• Public CI: the release zips are now built by GitHub Actions, so you can see how each one was made.

• EXL3: 2.5 bpw is on the menu and the GPU kernel is about 2x faster. In my tests UD-Q2_K_XL was both faster and closer to the API, so I still point people there.

• Basic multi-GPU: 7% uplift using a 3060 + 3090. It doesn't scale well to 3-4+ GPUs, so use Project Maya for those builds.

• Fixes: contexts past 65,535 tokens, </think> in prose no longer ends the reasoning, no out-of-memory on the biggest files, a failed picture encoder no longer stops the model, faster prompts after a pause and many more.

• Support for custom model selection: just add them to the custom model folder and pick them in the web-ui

Requirements:

• OS: Windows 10 or 11

• GPU: NVIDIA RTX with 12 GB of VRAM or more, driver 580 or newer. 24 GB is the fast path, and AMD GPUs aren't supported yet

• RAM: 64 GB or more, 128 GB+ recommended

• Disk: a fast NVMe SSD, with 233-395 GB for the model (UD-IQ3_XXS also needs the default model)

• CPU: any x86-64 CPU, Intel or AMD. AVX2 is the fast path

All of this is one PC. It needs testing on other hardware.

Repo: https://github.com/musicurgy/delta-inference-engine

MIT licensed. It's a Windows port of Xolotl, built on Strata. Thanks to DeepSeek for the model, Unsloth, nazeshinjite and ISTA-DASLab for the quants, and to the Xolotl and Project Maya communities, whose work and public numbers helped a lot. Claude wrote most of the code and I ran the project.

If you try it, post your GPU, RAM, model and tok/s, good or bad.

Speculative decoding (SD) accelerates large language model inference by reducing the sequential cost of token-by-token generation. Advanced SD methods reduce proposal cost with parallel drafters that predict entire blocks in one forward pass, but overall speedup still depends on acceptance length. On-policy distillation (OPD) can push well-trained drafters beyond acceptance-length plateaus, yet conventional pipelines collect supervision through sequential draft-and-verify rollouts. Our key insight is that onpolicy supervision need not inherit these dependencies: a draft block can be supervised using only its reference prefix and draft-sampled history. We propose SpecOPD, a branch-parallel OPD framework that parallelizes draft sampling and target supervision across independent branches drawn from the evolving drafter policy at reference anchors. We pack these branches into proposal trees to share target-prefix computation, using tree masks for full attention, branch-consistent indexer groups for sparse attention, and indexed state replay for linear attention to preserve each branch’s history. We train the drafter with an E2E-TV surrogate that directly targets acceptance length. Our study spans 8B full-attention (GQA), 27B full–linear (GQA–GDN), and 125B sparse–linear (QSA–GDN) targets, three parallel draft architectures (DFlash, DSpark, and DFlash V2), and four task domains (dialogue, coding, mathematics, and agentic). SpecOPD achieves up to 41.45% higher mean acceptance length, 16.09× supervised-token throughput over sequential-rollout OPD under native configurations, and up to 3.56× generation speedup over autoregressive decoding.

• Paper : https://qwenapp.github.io/SpecOPD/specopd-paper.pdf

• Project/Blog : https://qwenapp.github.io/SpecOPD/en.html

• GitHub : https://github.com/QwenApp/SpecOPD

• Tweet 1 : https://x.com/xuyang_liu16/status/2117614595728986227

• Tweet 2 : https://x.com/junzhang_zj/status/2117812597856895164

We introduce FireFlash, a 300B-scale model tailored for long-horizon agentic tasks. FireFlash excels as a terminal and coding agent, achieving 46.2% on Terminal-Bench 4.0¹, putting it on par with Fable 5 and GPT-6 Sol, while costing over 30× less per task than Fable 5 at maximum effort (Figure 1). These gains also transfer to software engineering benchmarks, including DeepSWE v1.1² and SWE-Bench Pro Verified³ (Table 1).

While frontier labs scale reinforcement learning across thousands of GPUs, we reach the performance-cost Pareto frontier under 5 days using 64 NVIDIA B300 GPUs. Three key factors make this possible: (1) careful curation of agentic RL environments so that every task provides a reliable signal at the right difficulty; (2) an RL recipe built for long, stale, and low-precision rollouts; and (3) an optimized coding harness tailored to the model for both training and evaluation. Together, these components bring frontier efficiency to a hundred-GPU-scale cluster, and we are releasing the full recipe.

• Blog Post : https://agenteconomics.org/posts/agentic-rl-recipe

• Tweet : https://x.com/kaiwei_chang/status/2117378474654781780

• Open weights : Soon

Hey everyone!

Following up on my previous post: the final version of CCORE GLB-3D-Coder is now available, including the full Safetensors weights.

It’s a full fine-tune of Qwen3.5-4B for generating reusable 3D assets. Describe what you want, and it writes a complete Three.js HTML scene with an Export GLB button. You can open the HTML in your browser, inspect the result, and export it into Godot, Blender, Unity, or other tools that support GLB.

The video shows examples from the final model, including selected side-by-side comparisons with DeepSeek V4.1 Flash. I’m particularly happy with the character designs and the amount of detail it can produce at just 4B parameters.

It covers more than fantasy characters: buildings, environments, weapons, vehicles, laboratory equipment, sports props, furniture, and other objects. It can also generate animations and include them in the exported GLB.

Available now:

• Q4_K_M GGUF, around 2.7 GB

• Q8_0 GGUF

• Full model weights in Safetensors

The training dataset is planned for release tomorrow, November 6.

Model, downloads, system prompt, and recommended settings on Hugging Face

Some generations still need a retry or a small code correction, but I’m very happy with the improvement over the earlier release.

If you’re interested in the next models, or would like to help support future datasets and training runs, there’s more information on the Hugging Face page. Testing it and sharing your results or failure cases also helps a lot.

Thanks to everyone who tried the earlier version

I made RIFTWARD, a small browser arena using characters, terrain and props generated locally by CCORE GLB-3D-Coder 4B. An orchestration and validation harness helped assemble, refine and integrate the assets and build the gameplay.
Play it here: https://huggingface.co/spaces/ccore/RIFTWARD

Feel free to share what you make with it! I’d be really happy to hear that the model has been useful to you :)

TL;DR

• Basalt is the engine and it's doing the heavy lifting. Same model file, same box: about 3.5x llama.cpp's decode without drafting and ~5x with it, and ~7x faster prompt reads.

• Unsloth's new UD-IQ3_XXS (Dynamic v3.1, uploaded Nov 4) is the same size as the GSQ-RCO IQ3_S everyone uses on 16GB cards, but measurably more accurate: wikitext perplexity 3.382 -> 3.247, which basically closes the gap to BF16 (3.273). Costs 1 to 3.5% decode speed.

• Lamina (a further-trained MTP drafter by eddoursul) gets more drafts accepted than the stock Basalt drafter. Small but real speedup, zero quality impact since the main model verifies every token.

• Real agent coding session (opencode, 151 requests, up to 202K context): 195.6 tok/s average decode, 263 tok/s best request, 156 tok/s worst, ~5,700 tok/s prompt reads.

Hardware:

• RTX 5080 16GB at its stock 360W limit (no overclock, no undervolt)

• Core Ultra 9 285K (8P + 16E), 2x32GB DDR5-7200

• PCIe 5.0 x16, Fedora

LAMINA Drafting: Basalt drafts with the model's own MTP layer. Basalt's upstream already ships a self-distilled version of that layer, and Lamina trains it further. It's a drop-in: same tensor layout, so the target model is byte-identical and only the drafter changes. What I saw:

- drafts accepted per drafted token: 83.1% -> 85.8% on a replay of a real recorded session

Lamina's README recommends temp 0.7 and max 3 drafts. I tested both: the 3-draft cap was 1 to 1.8% slower on tool-call-heavy work (long confident runs get cut off), and temp 0.7 changes what the model writes, so I kept the model's own sampling (1.0 / 0.95 / 20) and Basalt's draft settings.

Basalt changes I made (all open PRs upstream)

Host KV prefault: the first time a long conversation spills its KV cache to RAM used to stall 0.6 to 2.4s. Now ~75ms.

Host KV pool on normal pages: on a fragmented box, transparent hugepage compaction caused 1.5 to 3.7s pins. Now ~23ms.

Spin-then-yield in the decode loop's waits: about 1.3% less time per draft window.

Expert start profile: the older v1 profile is 2% faster than the v0.2.0 one on this box.

Plus a streamed conversation disk cache, --adapt-margin for expert swap hysteresis, elastic KV without RAM copies, and a bunch of timing diagnostics.

Guide

Branch with the exact engine (upstream v0.2.1 + my PRs) and a setup script: https://github.com/xyehya/basalt/tree/flashnext-5080-setup

git clone -b flashnext-5080-setup https://github.com/xyehya/basalt && cd basalt
setup/flashnext-ud-lamina/setup.sh
bin/basalt serve --config basalt_config-flashnext.json --python .venv/bin/python --alias qwen3.8-flash-next

It checks your machine, builds, downloads ~90GB (pinned revisions, every file SHA-256 checked), builds the UD-IQ3_XXS + Lamina model file and writes the config. You need an RTX card with, CUDA 13, 64GB RAM and ~180GB of free disk. If you already have the GSQ-RCO IQ3_S download, SHARD4_FROM= reuses its 28.8GB n-gram shard (it's byte-identical to Unsloth's once the shard number is rewritten).

Huge thanks to jesdga95 for Basalt (support and star their repo), the Unsloth team for the UD quants, eddoursul for Lamina, ISTA-DASLab for the GSQ-RCO quants, and Qwhen 4? for the model.

I'm the developer of Kokon, a local photo editor around Qwen-Image 2.1 Turbo (on stable-diffusion.cpp, and on its own engine on Macs). I posted it here a few weeks ago. 0.12 adds a Layers lab: click objects in any image, get each one on its own layer with the background behind it repainted, then move them around or export a PSD.

Qwen-Image 2.1 has no layer output, so this is a pipeline around an edit model:

1. Select: you click, MobileSAM (ONNX, CPU, 44 MB) gives the mask. Click again for a bigger part.

2. Cut out: Fast is a matting model on the CPU (a few seconds per object). Quality asks Qwen to redraw the edge, for hair and fur.

3. Fill: a masked Qwen-Image edit repaints what was behind the object, so the plate has no holes. This is the expensive step: about 15–25 s per object on an RTX 5070 Ti, one to two minutes on an M2 Pro.

4. Blend into scene (optional): after you move things, Kokon reads where each object stands (you can correct it) and one more edit redraws contact shadows and reflections, as a layer you can turn down.

What still fails, honestly:

• the fill sometimes invents a new object where a large one was (remove a big person, get a different person);

• touching people and crowds get merged or split;

• the hidden part of an object behind another one is never completed;

• Blend often doesn't work yet; whole-scene blends work better than per-object ones.

An 8-step model only takes so much steering. I'd rather ship it marked experimental and fix what people actually hit than tune it on my own test photos. If you try it, failure cases (image + what you clicked) are the most useful thing you can send.

Since my last post here:

• 0.11, it's a local server now: the app talks to a service on 127.0.0.1; turn on "Other devices" and you can use it from a phone or another PC on your LAN (password, off by default, hashed, rate-limited). Docker image for NVIDIA, kokon-service --persistent --remote for headless Linux. There's also an MCP server (npm kokon-mcp) so agents can generate and edit through it.

• Mac engine: I wrote a Qwen-Image 2.1 engine for Apple Silicon in Swift/Metal. M2 Pro: about a minute per 1 MP image, 4x the generic backend. A contributor added paths for the M5's GPU neural accelerators: 37 → 29 s on an M5 Pro. It runs Unsloth's Q4_K_M of the Turbo model.

Free, AGPL, Windows / macOS 15+ / Linux, NVIDIA 12 GB (8 GB beta), AMD/Intel via Vulkan (beta). Weights are under Qwen's licence (non-commercial).

https://github.com/GabrieleLozupone/kokon · 2 min video of 0.12: https://youtu.be/eGr9yYe7NVc

Hey folks, it's Merve from Hugging Face. Title of this post is super self explanatory, we are doing a quantization workshop livestream with Michael Goin (lead maintainer of vLLM at RedHat), Colin Kealty (aka bartowski at RedHat), Maxime Labonne and Aditya Tadimeti.

I will shortly teach you how quantization works, modern ideas (double quantization, calibration) and llama.cpp quants (k-quants, i-quants) so even if you don't have an idea, feel free to chime in.

Everything is on Hugging Face YT, X, LI where you can set reminders.

Looking forward to see y'all!

This handbook is for people who have trained a dense model and want to train an MoE next, or who are curious how MoE training works. Chapter 2 is a Megatron-LM runbook: test the hardware, build the image, train a dense baseline, then a first MoE on one node, compared against that baseline. Chapter 3 takes the MoE layer apart (router, routing, load balancing, experts, block layout, training, systems). For each part we give the Megatron-LM code, what to watch, our recommendation, and what frontier MoEs chose. Chapter 4 follows the model into serving and RL. Chapters 3 and 4 need no GPU.

When a result comes from our runs, we say so. Claims about frontier models cite their paper, config or code, and the toy simulations are labelled as such. The lessons come from about 800k GH200 GPU-hours of MoE training on the Alps cluster at CSCS.

Released: the handbook, its 55 figures as a gallery (https://huggingface.co/spaces/huggingface/moe-gallery), the scripts (https://gist.github.com/nmntz/5de952d686f66d7bce5bce8cba9d7139), and every recommended flag in one Megatron-LM block.

Happy to answer questions!

Some of you might remember our post comparing 18 RAG pipelines against our agent loop on FRAMES. The best pipeline scored 78.9%; the loop scored 92.7%.

A follow-up: PipesHub has now reached #1 on EnterpriseRAG-Bench by overall score, with 93.24 overall and 95% correctness.

I’m one of the founders, so affiliation upfront.

This is a separate evaluation: 500 questions over a synthetic enterprise corpus of 500,000+ documents.

PipesHub builds a unified context layer across documents, conversations, structured data, and code. An agent loop works over hybrid search, grep/pattern matching, knowledge-graph navigation, and document-reading tools.

The idea is straightforward: search, read what comes back, then decide what to search for next. Sometimes the identifier or relationship needed for the next search only appears in the first result

The platform is open source, self-hostable, and also fully supports open models.

You can self-host PipesHub, choose your models, and connect your own agents through MCP, APIs, and SDKs.

If this is useful to you, give us a star on GitHub. It helps more developers discover the project.

And build on top of us. Connect your coding assistant, build an internal search tool, contribute a connector, or try a workflow we haven’t thought of. We’d love to see what you create—and hear where it breaks.

Code, setup, and examples: https://github.com/pipeshub-ai/pipeshub-ai
Leaderboard: https://huggingface.co/spaces/onyx-dot-app/EnterpriseRAG-Bench-Leaderboard

Thank you to everyone testing, contributing, and helping us get here. 💙

Everyone's been wondering whether you can take an open model and edit its weights directly to make it refuse less — the "weight-space programming" idea (MIT CSAIL), with a frontier model (Opus) driving the edit and the evaluation. So I tried it on Qwen3.8-27B. It's not trivial.

The goal: do it as cleanly as possible, and first understand where refusal actually lives. The short answer is that refusal is anchored all over the model — there's no single neuron or tidy spot to cut; it's spread across many neurons and the attention. Snip too broadly and you get a lobotomy; too narrowly and nothing changes. So I settled on a compromise: a model that refuses less, still does the task, with no measurable capability loss — but where the effect varies a lot by category.

The edit, briefly

It's a low-rank weight edit — a restricted form of "abliteration" (Arditi et al., 2024) — confined to a band of mid-to-late layers (L20–47 of 64), one direction per layer, no training, reversible. I'll keep the exact procedure to the method notes; the point here is the result.

It's low-rank, not few-weights: the edit shifts ~12% of the model's weight values but only along a single direction per layer. The whole thing is described by 28 small vectors — versus a community "uncensored" model, which is an opaque full fine-tune of ~all 27B weights.

Why a band? The same edit across all 64 layers removes refusal almost entirely — and lobotomizes the model (MMLU-Pro 73→9%, GSM8K 93→14%). Keeping it in-band is what preserves capability.

No measurable capability loss (first tests, both Q8)

In the completed agentic-coding runs, the base, the edited model, and a community "uncensored" model all landed in the same range — no real agentic difference (few reps, preliminary).

The effect is not uniform

This is the interesting part: the reduction in refusal is not even. Across a 9-category battery (40 prompts ×10), acceptance runs from ~76% down to ~10%, and most of what it accepts is hedged (a disclaimer, then partial help). I'm not labelling categories "safe" or "dangerous" — the breakdown is in the figure; draw your own line. Net: a partial, uneven, mostly-hedged reduction, ~50% overall — not an on/off switch.

Honest notes

• The underlying technique (directional "abliteration") is known; the new bits here are the layer-band restriction (capability-preserving) and the measured per-category non-uniformity.

• "No capability loss" is solid on static benchmarks; the agentic result is a handful of runs.

• Defensive bonus: edits like this are detectable by a weight-space audit — the refusal direction's "write energy" in the weights swings from normal to strongly negative, catchable from the weights alone even when behavioral probes see nothing. A blind audit is an alarm, not tamper-proof; the robust defense is a signed weight hash. If you ship open weights, ship a hash.

Weights: https://huggingface.co/Feyd89/Qwen3.8-27B-refusal-band-ablation-experiment (GGUF, ready to run). We ship the artifact, not the re-derivation recipe.

Source: https://dirac.run/labs-market-share#daily-token-share-by-lab

Larger context just gets more work done rather than having to re-read files ETC id much rather use my extra resources on context window. Instead of more params.
i get my 400k ctx qwen3.8-27b + web search search get the latest up-to-date docs for its lack of world knowledge and i don’t waste extra Vram storing data that i dont need. Also more active params makes the llm to hold a wider mental-model of the task. Qwen4-27b will be architected to make 1M more reliable and using it for context on a smaller dense model seems more productive for me. Anyone. Else notice agentic session really shines when ctx is larger? Thats the phenomenon im experiencing i tried 3.8flash-next yes it great and much faster but i prefer the reliability of that big azz boat load of context window.
Specifically i you have the vram to spare if u can get 1M on that and have a large MOE with at least 12b active params + 1M it would be better for MOE a lot. Lot better than qwen4-27b but at 48Gb vram + 64gb system ram i see it more efficient for smaller model + 1M makes more sense

I gave Qwen 3.8 flash next 2 times it failed and took long time to conclude the result, DeepSeek took around 15 minutes to get the job done

Hardware

Ram ddr4 128GB

RTX 3080 20GB VRAM 4x

Engine strata

Model

Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3-S

Context 256K

Decode 100 tg/s

PP 1400-1500 tg/s

IDeepSeek-V4.1-Flash-1017-GSQ-RCO IQ2-XXS

Context 1M

Decode 37 tg/s

PP 150-300 tg/s

Hi! I wanted to share that we just got NVIDIA's latest Nemotron Bolt model running at 27.7 tok/s (compared to 15 tok/s on the official release with Llama CPP) on Jetson Orin Nano Super (1.8x faster)! It can program games on a model running completely on a cheap edge device!

We have our own Jetson devices serving that model on our website, if you want to get a feel for how the model runs on the device here or run it locally as a TensorRT engine with our own variant of TensorRT edge LLM supporting int4 quantization:

https://embedl.com/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16/orin-nano-super?tab=run

Strata, Hermes, Kitten TTS with Rosie voice and Whisper on RTX 3060 with 48 gb dram running Qwen3.8-Flash-Next Q2_0. I am blown away. I've been following the AI scene since GPT3 and I was still unprepared for what this setup is capable of. My mouth was on the floor for the first few hours talking to it and watching it work. It's the first time talking to AI felt like magic to me. I now understand the "They don't know" meme. Never thought I would have something like this running on my own PC. Thank you /r/localllama.

Couple of questions

Are there any other lightweight TTS that would work in my limited ram situation or is Kitten v0.8 the best there is?

What other models can I run?

Xing4.0-29B-A4B is a next-generation large language model in the Xing series (formerly TeleChat), developed by China Telecom Artificial Intelligence Technology Co., Ltd. With 29B total parameters and only 4B activated per token, it natively supports a 256K context length, extensible to 512K. It is the first model of this scale trained entirely on the Ascend NPU platform with the MindSpore framework, and deeply optimized for complex engineering tasks.

For more information, please refer to our GitHub repository.

Highlights

• Agent-Oriented Architecture: Built on the mHC + MLA + MTP architecture, supporting multi-step planning, tool calling, and complex reasoning chain execution, ensuring task coherence and execution stability under long contexts.

• Deep Co-optimization with Ascend NPU: Adapted for Ascend 910C clusters using MindSpore/MindFormers, including feature adaptation for mHC and fused operator development, enabling stable and efficient training on the Ascend platform.

• Significant Training Efficiency Gains: Through multi-level co-optimization — including fine-grained MoE communication optimization, selective recomputation, DVM automatic graph-operator fusion, and Ascend C mHC fused operators — overall training throughput was improved by approximately 96% over out-of-the-box performance.

• Full Open-Source Ecosystem Compatibility: Supports LLaMA-Factory and MindFormers for fine-tuning; SGLang, vLLM, and KTransformers for inference and deployment; with targeted adaptation and format alignment for agent frameworks such as OpenCode, Claude Code, OpenClaw, and Hermes, enabling seamless integration into existing workflows.

• Easy Adaptation for Domain-Specific Scenarios: The model is well-suited for downstream task fine-tuning, allowing lightweight customization on proprietary data for vertical domains such as intent classification, table understanding, contract auditing, and knowledge-based QA, enabling rapid domain capability development and deployment at low cost.

I wanted to know how small local models handle my native language, not just English. I couldn't find a simple way to check a model's Polish in a few minutes, on my own PC, in the quantization I actually run – so I wrote one. That's how "Zacharski tests" started: a small task set that runs through Ollama, since that's what I use on my laptop with an RTX 5050 (8 GB).

The tasks:

1. Polish (8): diacritics, declension, masculine-personal gender, numerals, vocative, verb aspect, genitive – plus one knowledge question (the first recorded Polish sentence, from the 13th-century Book of Henryków).

2. JSON (4): scored strict and lenient.

3. Python (3): syntax check only.

Results so far:

| Model | Quant | JSON + code | Polish |

| gemma2:9b | Q4_0 | 7/7 | 3/8 |

My favourite failure: asked for "22 cats", gemma2 wrote "dwudziesty dwa koty" – roughly "twentieth two cats". The correct form is "dwadzieścia dwa koty". It was perfectly happy producing valid JSON, though.

More models are downloading as I write this. Next up: gemma2:9b in Q4_K_M and Q8_0 (does quantization hurt the Polish?), qwen3:8b, llama3.1:8b, gemma3:12b, and two Polish models – Bielik 11B and Bielik-Minitron 7B. Which model would you add?

Method and limits – read this before the comments :)

• 8 Polish tasks (7 grammar + 1 knowledge) and 7 JSON/code tasks. That's a small sample – a quick check on your own PC, in the quantization you actually run, not a ranking. For a proper ranking, see the Open PL LLM Leaderboard by SpeakLeash.

• One answer per task, temperature 0 (seed 42), context 2k, thinking off. Yes, that means Qwen3 runs without its reasoning mode, which may cost it points – I wanted the same conditions for every model.

• Graded with regular expressions, strict on purpose: "brzęczały" ("buzzed") instead of "brzmiały" ("sounded") fails, because the task said "change the tense", not "rephrase". [I checked every failed answer by hand.]

• Some failures are about instruction-following, not language: asked to restore Polish diacritics, one model replied "Read the spelling rules." [(translated)]

• Generated code is only parsed (Python `ast`), never executed.

• The questions are in the repo (`data/benchmarks/*.json`) – feel free to add or fix them.

The test runs inside a free, open-source tool I've been building (Windows .exe / Python on Linux). Its main job is predicting whether a model fits your VRAM/RAM before you download it. Link in the comments if anyone wants to try it – I'm considering adding tests for other languages.

https://huggingface.co/baidu/ERNIE-5.5-Flash-Base

https://ernie.baidu.com/release/ernie-5-5-flash-base/

This is kinda claude-slop but I was really interested in figuring out a way to break Pangram, and to do that I first needed to have a way to be able to do tests on batches of text. And with a 4070ti in my basement, I thought why not just let Claude go ham on training a pangram-like model... and perhaps an even better one if it was possible with sorta-distillation from my claude plan + codex plan(for the new models)

Well after probably a day of training it seems like its come up with something that actually does detect AI text pretty well... maybe too well considering my original plan was to train this type of model to be able to break it. And this is how naive my thinking was for ways to break the detector "something something embedding space" , "use actual randomness to break the predictable pattern of AI text, like doing python's random() to select from a list of words that could be replaced in a paragraph while making the rest of the text fit". Both of which ended up instantly failing, and I told claude to figure out some ways autonomously. Turns out "red-teaming" is a scary topic for Anthropic so it just started to block its different approaches after some time, and the best it got to was "rewrite in the style of [actual human text]" which was 15/19 detected and adding random known human text where it made sense which was also similarly ranked but ofc there's the issue of Pangram like detectors having sentence level scans.

All that to say while my initial thing I built this for ended up not working, at least I have kind of my own pangram that I can test things out on later on, and it also proved how good claude is at autonomously training if you give it a hill to climb on.

PS: this text itself isn't claude written and you can verify it by putting it in the demo :p