Ornith-1.5: From Self-Scaffolding to Self-Improvement(ornith.ai)
155 pointsby CommonGuy8 hours ago16 comments
  • montroser5 hours ago
    Hoping this is real. It's too bad to see the signals from Qwen that they will not be releasing a 35B-A3B for the 3.8 lineup. The MoE architecture makes a huge difference for being able to run these local models on reasonable consumer hardware.
    • smcleod2 hours ago
      The smarter 27B is so fast with MTP I've found I really don't need the 35B-A3B. You get around 70tk/s on a M5 Max lowering to around 40tk/s at higher context sizes.
      • seanmcdirmid2 hours ago
        I've benched 3.8 27B being significantly slower and less quality than 3.6 35B-A4B (both 4-bit quant, MTP, both using turboquant 4-bit served by oMLX), to the point that I'm not even using it right now (on an M3 Max). What's your use case and what did you observe? I might be missing something.
        • smcleodan hour ago
          I believe you mean 35B-A3B, there was no such thing as A4B. I use 27B and other models for software development, and quite a few research or similar agents. I cannot imagine a world where the old 35B-A3B model is smarter / more capable than 3.8 27B - the difference is night and day for coding at least. Where 35B-A3B was fast and felt like a Haiku model, 27B feels like a strong Sonnet when given the right tools. I don't use turbo quant so can't comment on that, but with the A3B model you're using you probably won't get much from using MTP with small MoE models like that.
          • xscott44 minutes ago
            People over-quantize things, muck with the temperature and other settings based on superstitions or results from models they think are similar. There's lots of ways to make 3.8 27B dumber.
      • quinncoman hour ago
        I only get ~4 tok/sec on a M1 Pro with MTP.
      • Muromec2 hours ago
        Does 27b mean it fits one 32GB GPU?
        • smcleodan hour ago
          It doesn't mean that, but yes it would (with a 5 bit quant).
    • dofm3 hours ago
      Quick tests suggest it’s pretty good at reasoning and tool use (keen to search to check its thinking) and it seems to waste much less time thinking, too.

      So it feels very fast.

      But it does not seem to be better than Qwen 3.6 35B at coding. A bit worse, I think, though I will test it more.

      If you have a machine that can fit a 35B model in VRAM, I would suggest testing Muse Glimmer with (from memory)

        Reasoning strength: low
      in the system prompt.

      Despite being a dense model, this is actually capable of solving code problems faster than the Qwen MoE, despite having only one fifth of the raw token performance.

    • mirekrusin4 hours ago
      Personally I find speculative decoding much better strategy than MoE – performance wise it's there at 90-100 t/s on 2x4090, great intelligence – really great fit.
      • d4rkp4ttern4 hours ago
        A lot of people, including me, don’t want to bother with GPUs, they’d rather run it on their M1-M5 MacBook. For example the 35B-A3B is very usable even on a M1 64GB MacBook.
        • mirekrusin3 hours ago
          Speculative decoding also works on Mac, 64G is more than what I have, m5 max should handle up to ~40 t/s with optimized setup (and with a lot of vram you can get great wins on concurrency – that harness can take advantage of for single user task as well), but agree memory bandwidth in mac or spark is still too slow, next gen for both will be great hardware to have for sure.
          • smcleod2 hours ago
            I get around 70tk/s on the m5 max, with 5bit AWQ / oQ5 slowing only to around 40tk/s at higher context.
      • c0m470533 hours ago
        MoE is great on systems that lack the VRAM to host the full model. On my 16GB VRAM system, I can get 100 tok/s with Q4 Qwen 3.6 35b a3b, and 15 tok/s with 27b.

        MTP is a trade-off, as it pushes some more of the model off the GPU.

        I have managed to get usable quants of Laguna S2 and even DeepSeek V4 flash on this setup.

        There is clearly some intelligence loss compared to similar sized dense models, but I feel like it stomps on the 9-12b models I could run fully on GPU

    • parsimo20104 hours ago
      Honest question/suggestion for the HN audience- Since Qwen released the weights for Qwen3.8 2.4T-A95B and we already have the staring point of Qwen3.6 35B-A3B, couldn't someone distill the bigger model and make a "pseudo" Qwen3.8 35B-A3B? Sure, it wouldn't be an official Qwen release but couldn't someone improve on Qwen 3.6 and get the thing everyone is asking for?

      I am calling this a suggestion for the audience because I don't have the will/resources to do this.

      • WASDx3 hours ago
        "Qwen3.8 35B-A3B" and 4B/9B variants are already on huggingface distilled by hobbyists.
      • halJordan2 hours ago
        Yes, of course. But no one really wants to be the guy actually renting an entire B300.
        • boznz2 hours ago
          ..And there lies the problem.
    • hgoelan hour ago
      The way it was said made it sound like they had something better than a 35B-A3B coming.
    • verdverm3 hours ago
      I'm running qwen3.8 27B dense on reasonable hardware (oem spark)

      tbh, I have stopped using MoE in the name of speed, the dense (with more active parameters) makes a real difference in output quality

  • rbanffy25 minutes ago
    It’s time for me to upgrade the main server in my home lab and I’m thinking about which machine should I have.

    What kind of hardware you’d need to run the 397B one at an acceptable speed?

    • colingauvin20 minutes ago
      3 DGX Sparks with 400 Gb interconnects in a loop.
  • hxiian hour ago
    Interestingly, in my own benchmark and testing (in the hopes of finding a good-enough local model to run a personal assistant agent), Ornith-1.0-9B was worse than Qwen3.5-9B which according to their scores should've been reversed.

    I will definitely pass Ornith-1.5-9B through the gauntlet as well!

  • lsban hour ago
    The page has comparisons with Qwen 3.6 27b and I’d love to see comparisons with Qwen 3.8 27b, the newer one is much more capable!
  • prometheus19925 hours ago
    Can't wait to try this. Ornith1 (9B) was a really nice model. I have been running it locally using - https://github.com/deepanwadhwa/samosa-chat
    • _defan hour ago
      What did you use it for? I've found it somewhat capable but not worth to actually use it (1 9B, that is)
  • jonesy8275 hours ago
    I've been using the 35B-A3B today for some web scraping work, and it has been on par with Qwen3.8 27B at a much higher speed and at a higher quant (q4 vs q8). I'm impressed.
    • jakswa4 hours ago
      I had to go down to UD-Q3_K_XL for Qwen 3.8 27B to get it to fit in VRAM and be usable, but I worry I'm gutting its intelligence somewhat. I too am interested in faster + more-usable alternative that can exchange blows with the Q3-dumbed 27B.
    • jadbox5 hours ago
      I need someone to run actual benchmarks between the two.
      • swatcoder5 hours ago
        Benchmarks are the BMI of model evaluation.

        They may have utility in trying to look at the whole landscape of models, but are very misleading when it comes to making 1:1 comparisons or in developing confidence at to how a given model will deliver on your workflow.

      • gertlabs3 hours ago
        These models have gotten a fair amount of attention -- we're hoping it's enough to get them added to some reliable inference providers and OpenRouter, at which point we'll run them on our full benchmark suite.
      • NitpickLawyer4 hours ago
        Only relevant benchmarks are those you make yourself, targeted specifically for your workflows. Anything else is just number go up on a pretty graph, and every model out there is probably benchmaxxed to hell on the public ones anyway. Keep yours private.
  • bigcat123456786 hours ago
    How is ornith-1.5's base model developed? Is the base model one of the Open weights models, or one pre trained by ornith team from scratch? I couldn't find information to answer this question in the article.
    • goldemerald6 hours ago
      It looks like they post-trained Qwen3.6. Interesting to see how far they could improve it with they harness/algorithm.
  • 6 hours ago
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  • esafak4 hours ago
    Apparently this is Jiwei Li's new company. https://ai.miraheze.org/wiki/Ornith https://www.innovatorsunder35.com/the-list/jiwei-li-2020/

    I wonder what their angle is going to be; the scene is crowded, and they don't do serving.

  • colingauvin4 hours ago
    397 is just too big for two Sparks even at NVFP4. Wish they had made this just a tiny bit smaller.
    • kees994 hours ago
      Ornith-1.5-397B is derived from Qwen3.5-397B-A17B via post-training. That process preserves exact parameter count.
      • ggcr3 hours ago
        They should've included Qwen3.5-397B-A17B in the benches then :/
      • colingauvin4 hours ago
        Ah thanks for the background. Well, time to learn how to quant things down!
  • nextaccountic5 hours ago
    Is this open weights? Or planned to be
  • garo-pro4 hours ago
    Across five cases it reliably claims to be Claude without being able to name a specific version.
    • wgd3 hours ago
      Nobody (with the probable exception of Anthropic given their work on character training) really trains models on their identity and Claude is the only AI persona that's well-defined so if you put yourself into the AI's shoes it's a pretty reasonable guess that it might be Claude. I've had basically every open model claim it's Claude when the topic comes up.
      • orbital-decay3 hours ago
        Anthropic changed their character training policy many times, and the name is probably separate from it anyway (besides the bits from the constitution etc). Name training usually comes last in all models, if at all, and it's pretty shallow. Certain Claude models say they are Qwen or Deepseek when asked in Chinese, for example.
        • wgdan hour ago
          Yeah, Claude is actually surprisingly unsure of his identity considering that their most recent publication on their constitutional AI training literally had graphs demonstrating how certain properties differed based on whether they were phrased as questions about "Claude" versus "You", but it's actually that which makes me fairly confident that they're probably doing _something_ to try and close that gap.

          More generally, their current approach to constitutional AI pretty much only makes sense if they believe that they can first teach the model what the Claude character is like and also teach the model that the persona responding is Claude, so I figure that has to be part of the pipeline even if they're not very good at it.

  • tangjurine5 hours ago
    This looks cool
  • jakswa5 hours ago
    I'll be comparing the 9B vs Ling 3 Tiny (8B-A1B) as a scout model. Ling tiny is so fast but can be a little too dumb. Hope the 9B strikes a good middleground even if dense/slower.
  • wyre4 hours ago
    This is exciting.

    Their 9B model benchmarks competitively with Sonnet 4 which is pretty cool to have such a small model compared to one that came out 10 months ago.

    I’m curious how providers will price their 397B model.

  • htrp4 hours ago
    Another day another startup claiming some vague version of RSI to try to close their round.