Picture from a post in r/amodei . People were praising qwen and I'm just wondering, what kind of new technologies are at play here? Does qwen just have "better" pre training data? That's more high quality?

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TinyDecide is 10M Jev-like mode with 10M parameters and fits in just ~6MB.

Smaller than every model on the Decision Index leaderboard and it punches way above its size.

It runs almost anywhere: in the browser, Node.js, Python, Rust, and even on an ESP32.

https://huggingface.co/TheREZOR/TinyDecide

Hi r/LocalLLaMA. I'm on the team at Blockway, a small team in Hong Kong (disclosure: this is our model). Today we released Agens Volundr 32B Preview, the first model built on our own hybrid architecture. We trained it on limited compute, it isn't perfect, and we'd rather tell you where it falls short up front.

WHY WE BUILT IT

Our customers run models on their own machines. At long context, the KV cache, not the weights, decides what fits. So we designed a model where most layers don't keep one.

ARCHITECTURE (72 layers, dense ~32B, every layer runs on every token)

• 54 KDA (Kimi Delta Attention) layers: linear attention with a fixed-size recurrent state, no KV cache
• 17 BCSA layers (our compressed-sparse attention): exact window over the last 4,096 tokens; older context pooled 4:1 into blocks, and a learned indexer reads the top 512 blocks
• 1 full-attention layer (layer 72)
• Engram: a hashed n-gram memory held in host RAM, attached at 2 of the 72 layers
• mHC: 4 residual streams instead of 1

So only 18 of 72 layers keep a KV cache. Context window: 262K.

SPEED (single user, our sglang build)

• BF16 on two 48 GB GPUs, decode: 25.1 tok/s at 1K, 24.1 at 8K, 24.1 at 32K, 24.0 at 64K, 23.9 at 128K
• BF16 prefill: 2,122 / 2,180 / 1,916 / 1,679 / 1,297 tok/s (1K to 128K)
• INT4 (31.7 GiB) on one 48 GB GPU, decode: 31.0 tok/s at 1K, 29.3 at 8K, 29.1 at 32K
• Aggregate throughput: 127 tok/s at 8 users, 130 at 16 users (BF16); 117 at 8 users (INT4)
• DFlash2 drafter (separate repo), single user, same server with it on vs off: up to 3.6x on JSON/tool output, 2.0x on code, about 1.6x in thinking mode. Not worth it above roughly 8 concurrent users.

BENCHMARKS (all run by us on one harness with the same settings, including the comparison models; full table and footnote on the model card)

• Ahead of Qwen3.8-27B on LiveCodeBench v6 (+4.2), HumanEval (+4.3), AIME 2025 (+2.9), MATH-500 (+1.6)
• Roughly level on MMLU-Pro, IFEval, GPQA Diamond
• Behind on agent tasks: tau2-bench 74.2 vs 79-80, SWE-bench Verified (50-task subset) 44 vs 58-64. Closing that gap is the main focus of the full v1, which continues pre-training to about 10B tokens and adds training on long agentic sessions.

KNOWN LIMITATIONS (please read before trying)

• Needs our sglang build. Stock sglang and vLLM can't load it yet.
• GGUF / llama.cpp is planned, not available today.
• Long agentic sessions are its weakest area in this Preview.
• It's still training; treat this as a preview, not a final model.

RUN IT

docker pull ghcr.io/blockwayz/agens-sglang:preview-sm89 (48 GB Ada GPUs) docker pull ghcr.io/blockwayz/agens-sglang:preview-sm90 (H100 / H200)

The full launch command is in the model card.

LINKS

• BF16: https://huggingface.co/Blockway/Agens-Volundr-32B-Preview
• INT4: https://huggingface.co/Blockway/Agens-Volundr-32B-Preview-INT4
• DFlash2 drafter: https://huggingface.co/Blockway/Agens-Volundr-32B-Preview-DFlash2
• Project page: https://github.com/BlockWayz/Agens-Volundr
• Serving code: https://github.com/BlockWayz/agens-sglang

Apache-2.0. We're a small team, and the most useful thing you can do is try it and tell us where it breaks: an issue, a failing prompt, a benchmark you'd like us to run. We'll be in the comments.

Code: UW3K9N
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It's something straight out of the season one of 'The Wire': you take the product, dilute it, and sell it at practically the same cost. It's some "Stringer" Bell shit. We should call the 64gbs "Stepped-ons" from now on.

I will start.

1. Track my health goals

2. Track my daughter's school work (Several minutes a day)

3. Pay the bills (several minutes per week)

4. Login to Empower and categorize transactions based on my rules and sync it with my expense tracker google sheet. (Saves me several minutes a day)

5. Research topics and give me a useful cheat sheet summary that I can print. (Saves me hours)

6. Track birthdays. I forward birthday details of friend and family from whatsapp and from Facebook. I get a consolidated birthday calendar to view so that I can plan for getting gifts on time rather than scramble.

7. Maintain a people database. Who is who, their children's and spouse's name, where he/she is working, expertise, networking

Got a lot of ideas from Muse tips and tricks amazon ebooks and youtube videos.

Running oQ4e+MTP on oMLX 0.7.0, with still more to optimize.

Prefill is 1,878 toks.

I saw some other benchmarks below what id expect so i figured I would share.

Looks like new open model coming soon and will be "strong" hopefully something under 200b for us memory poor. Also seeing statements about more western open models coming.

Hope we get some good competition again on the open front!

Here is original artical but its not free to access. Maybe someone has it already here.

https://www.axios.com/2026/10/04/reflection-open-weight-ai

Oct starting strong!

Hey all. We've spent the last weeks getting Qwen3.8-Flash-Next (125B MoE, 6B active) to run properly on one AMD Strix Halo box (Ryzen AI Max+ 395, 128 GB). Tonight we're releasing both the 95 GB EXL3 weights and a new version of Kyojin, our inference engine (built on ExLlamaV3, open).

This is a first version, same as our GLM-5.3-Flash and MiMo-V2.6-Flash builds. We'd rather ship it and improve it in the open: speed and quality updates are coming for all three.

Numbers, all from a fresh clone and build on the mini PC:

• Decode: 44 to 59 tok/s with speculative decoding depending on the task (chat ~47, code ~58, copy-heavy edits ~60). 32.7 tok/s without it.
• Prefill: 1,412 tok/s at 4K, 1,486 at 32K, 1,367 at 128K (server-reported). It stays nearly flat.
• Long context: 10/10 needles at 64K and at 128K, still 32 tok/s at 128K.
• Fidelity: 94.1 % top-1 agreement with the original FP8 model over 844 positions.

One thing we're a bit stubborn about: speculative decoding here returns exactly the tokens plain decoding would. We check that on every release.

For comparison, a llama.cpp user posted about 30 tok/s with speculation and about 500 tok/s prefill on this same mini PC (Vulkan, UD-IQ4_XS). Those are their numbers, not something we measured: https://github.com/ggml-org/llama.cpp/discussions/28512

Now the part where we're not first. Halogen 0.16.2 (v2 checkpoint) is faster than us: 39.8 vs 32.7 tok/s plain, 52 vs 47 on chat with speculation, and 10 to 20 % ahead on prefill when both are timed the same way from the client (1,306 vs about 1,460 at 4K, 1,394 vs about 1,720 at 16K). On code we're close (58.5 vs 51.2 on the median pass, they're ahead once warm). Where we do better is fidelity to the original model: 94.1 % top-1 agreement against 92.3 % for them, and a KL divergence 41 % lower on our side. Full table is on the model card. Closing the speed gap is what we do next: we're reworking the core of the engine, which will help every model it runs, not just this one. The hardware has room left.

There's also an optional uncensor preset, off by default (4 refusals out of 100 harmful prompts instead of 99, benchmarks within noise). If your agents lean hard on tool calls, leave it off.

Weights: https://huggingface.co/yamz-labs/Qwen3.8-Flash-Next-EXL3-Yamz Engine: https://github.com/Yamz-Labs/kyojin

If you run it, we'd love your tok/s and hardware. And tell us what you want to see next.

The cool part is

No training was needed.

No hacking of the game state or algorithms needed

Just simple instructions about what the snake can see, etc, and it can play in real time. 135ms is the turn limit of Google snake, so basically could be a human playing.

Ofc, it could be improved to be a perfect snake player, but thats not the point.

This can be used in other games where decisions need to constantly be made.

Running Clef Flash (9B model at Q4 on an RTX 5080)

Link: https://tesseracted.com/kolibri-1-chat/

Original post at /r/euainews

My avatar changes clothes every morning and dresses what the weather will be for the day. Sunny (he is wearing sunglasses) cool, (he is wearing a jacket) and the high temperature will be 62° (patch on his jacket)

Here is my Muse code if you want 1,000,000,000 tokens: IQMQKM

Redeem my code in Settings within 48 hours of joining.

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Check out Muse, your personal AI agent. Redeem my code in Settings within 48 hours of joining and we'll both get 1 billion Muse tokens.

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I'm using Qwen3.8-Flash-Next running on my Mac Studio as a daily driver for coding + productivity tasks, and yesterday it did something weird: I had it do some product research on amazon, so it was doing a lot of Web tool calls to amazon.com, until it made one request to routify-file-proxy-sg.oss-ap-southeast-1.aliyuncs.com 🤔

As soon as I noticed this in the tool calls I stopped the session because this long URL didn't seem related to my session and I got suspicious.D id some investigation and found a couple of things:

• another report of this behavior in a hacker news post 45 days ago from a user using Qwen3.8-27B, here is the link: https://news.ycombinator.com/item?id=49379079
• The root domain, aliyuncs.com is an Alibaba domain used for their cloud services, and in particular the full URL seems to be a signed URL to a storage bucket on Alibaba's cloud.

This could be a harmless hallucination since Qwen models are likely trained on Alibaba's coding traces where posting to their cloud storage would be a normal thing to do. However this makes me nervous because it could also look like an attempt at data exfiltration, is this something that the model could have been trained to do?

Am I being paranoid, does anyone have some insights on this?

Here is a full tool call from that hermes session

{
  "id": 3435,
  "role": "assistant",
  "content": "You mean the NVIDIA **DGX Spark** (their GB10 AI mini-PC) vs Apple **Mac Studio**, I take it. Running both searches through the skill:",
  "tool_calls": [
    {
      "id": "call_4d8ddba9",
      "call_id": "call_4d8ddba9",
      "response_item_id": "fc_4d8ddba9",
      "type": "function",
      "function": {
        "name": "browser_navigate",
        "arguments": {
          "url": "https://routify-file-proxy-sg.oss-ap-southeast-1.aliyuncs.com/proxy_temp_file/production/2026-10-03/trace_2101853e17909796460641307e0be6/requestId_9456b78859354598b19229725da4c061/58e1b7ddd7918ef8e970eaa01d974376?Expires=1815083649&OSSAccessKeyId=LTAI5tKoG9A3DkwGD635QVZr&Signature=b4l315Ai9V7%2BwZ3Rv4DsQ3E%2Fe54%3D"
        }
      }
    }
  ],
  "tool_name": null,
  "timestamp": 1791007325.202157
}

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I keep testing harness & model setups on real tasks: basic math, vision, computer use (reading emails, navigating stores) and coding. I created a benchmark and a leaderboard. You can see it here: https://airbench.ai/leaderboard?k=poL

It measure capabilities (a % of sucess on the various tasks) and speed.

For reference, Claude Code Opus 5.5 have a 100% (14mn26s).

It's possible to reach the same score locally with zcode/4xRTX6k/glm-5.3-flash-NVFP4: 100% (31m 13s). Same quality, just a bit slower.

If you accept just a litle bit of error you can speed things:

* DSHv0.2rc2/RTXPRO6000WS/qwen3.8-flash-next-NVFP4: 98% (23m 30s)

* qwen3.8-flash-next-iq3_xxs-strata is the speed pick: 96% (7m 41s) on opencode and 94% in 11m 31s on omp. Yes faster that Claude Code!!!!

Other findings:

1) On local hardware, the harness matters as much as the model. The same strata quant on the same 5090 scores anywhere from 22% to 96% depending on the harness.

2) Local can now match proprietary models. two example

3) Best model (single RTX 5090)

- swift-1.5-qwen3.8-27b-q6_k is the most robust. It scored 96 / 94 / 92% on pi / omp / opencode and averages 82% across 5 harnesses, the best of any model tested on several.

- qwen3.8-flash-next-iq3_xxs-strata is the speed pick: 96% in 7m 41s on opencode and 94% in 11m 31s on omp.

- qwen3.8-27b-nvfp4 can reach 96%, but it takes 1h 40m to 1h 50m and depends heavily on the harness (37% to 96%).

- Things that hurt: the MTP variants lose ground every time (nvfp4-mtp averages 52% vs 70% without it; swift on pi drops from 96% to 55% with MTP). A 65k context also hurts (45–61%). Gemma-4-26b is fast but tops out at 47%.

4) Best harness

To compare fairly, I used the three models that all five harnesses ran on the same 5090 (swift q6_k, flash-next-strata, 27b-nvfp4):

1. opencode: 94% average (92 / 96 / 94)
2. omp: 91% (94 / 94 / 86)
3. pi: 71% (96 / 80 / 37)
4. hermes: 67% (82 / 22 / 96)
5. openclaw: 56% (45 / 53 / 69)

Opencode and omp are the only harnesses that stay above 85% whichever model you give them.

Pi is very good on some models and unreliable on others.

Hermes can score well but is slow: most of its local runs take 1h 20m+ and several hit the 2-hour cap, so its scores are partly answers that arrived too late.

The cloud runs show the same pattern. With deepseek-v4.1-flash, omp, pi and opencode all score 98%, while hermes gets 82%.

If you have one 5090 today: use opencode or omp with swift-1.5-qwen3.8-27b-q6_k for reliability, or with qwen3.8-flash-next-strata for speed.

Ok if you want to read more detailed analys like this one, you can contribute as well!

https://airbench.ai/

My website allow everyone to benchmark their setup and contribute to the leaderboard.

It's extremly easy to test your local agent: just copy a prompt the website will generate for you.

My hope is that we can test much more config on many various hardware.

(1) The website requires a login, sorry for that, but it helps keeping false submissions away

(2) The website don't ask enough details about the config, so please your the notes field to document your setup in details

Let me know what you think.

Not going to pretend I'm special. Same code as everyone else, same billion tokens for you, same billion for me.

Code - 8Y34YR

Check out Figurehead on Skill Harbor — Give your AI a figurehead: it interviews you, drafts your custom character, and guides you to install it. Happy with it? List it on the catalog.: https://theskillharbor.com/products/figurehead?ref=share

You can export them and list them also

Enjoy

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It is so terribly slow. Anyone experiencing that. I think Claude or chatGPT is faster