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CHCharlesWwczoraj
Previously: https://news.ycombinator.com/item?id=48709744
https://swelljoe.com/post/will-it-mythos/: "Poor performer here, only found the one bug that almost every model found, despite its performance on other benchmarks being excellent for its size. […] It also performs poorly in a chat without tools, exhibiting an ehthusiasm for hallucination. I’m currently working on a replication of this with full tool access, including bash/Python, which may allow this model to be competitive."
RIricardobayeswczoraj
This is the first Qwen fine-tune that is not immediately rejected by the local LLM community, and in some cases even being recommended. Based on my limited usage, it is good, gives creative solutions to coding problems. I don't expect 9-35B models to one-click create full apps. Most people who were complaining did so .
NANarewwczoraj
From what I personally tested Ornith-1.0 35B is slightly better than Qwen-3.6 35B.
My tests are tasks that consist of adding/modify feature in a big C++ codebase.
The part that I find interesting is that the model is way faster than Qwen3.6 35B. It seems Ornith produce a smaller chain of thought.
On my test it can be 3 time faster to produce the answer.
I use it via llamacpp and codex-cli.
LHlhlwczoraj
I've been testing Ornith-1.0 35B (my own FP8-block quant) and I like it. It runs at >200 tok/s w/ vLLM on an RTX PRO 6000 (sm120), I've run >140M cached tokens of agentic coding work on it over the past few days. It seems to about somewhere between Qwen 3.6 35B-A3B and 27B, but the good thing: it overthinks/doom-loop a lot less than Qwen 3.6. When looking at the thinking traces I like its breakdown approach template.
It does good job on basic analysis, tasks, and some front-end/backend changes on a medium-sized Go codebase, but it reached its limits totally botching a longer (simple) kernel implementation job (about 100 iterations in Pi Agent harness) - this is the type of thing that stronger open models (Kimi K2.6, GLM 5.2) are able to do.
KEkennywinkerwczoraj
Can anyone explain what’s the story here? Is this just a re-skinned qwen? Who is deepreinforce-ai and why isn’t this model listed on their website?
How does it self-improve, does the model change on disk - or just during a single context run it gets better?
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Previously: https://news.ycombinator.com/item?id=48709744 https://swelljoe.com/post/will-it-mythos/: "Poor performer here, only found the one bug that almost every model found, despite its performance on other benchmarks being excellent for its size. […] It also performs poorly in a chat without tools, exhibiting an ehthusiasm for hallucination. I’m currently working on a replication of this with full tool access, including bash/Python, which may allow this model to be competitive."
This is the first Qwen fine-tune that is not immediately rejected by the local LLM community, and in some cases even being recommended. Based on my limited usage, it is good, gives creative solutions to coding problems. I don't expect 9-35B models to one-click create full apps. Most people who were complaining did so .
From what I personally tested Ornith-1.0 35B is slightly better than Qwen-3.6 35B. My tests are tasks that consist of adding/modify feature in a big C++ codebase. The part that I find interesting is that the model is way faster than Qwen3.6 35B. It seems Ornith produce a smaller chain of thought. On my test it can be 3 time faster to produce the answer. I use it via llamacpp and codex-cli.
I've been testing Ornith-1.0 35B (my own FP8-block quant) and I like it. It runs at >200 tok/s w/ vLLM on an RTX PRO 6000 (sm120), I've run >140M cached tokens of agentic coding work on it over the past few days. It seems to about somewhere between Qwen 3.6 35B-A3B and 27B, but the good thing: it overthinks/doom-loop a lot less than Qwen 3.6. When looking at the thinking traces I like its breakdown approach template. It does good job on basic analysis, tasks, and some front-end/backend changes on a medium-sized Go codebase, but it reached its limits totally botching a longer (simple) kernel implementation job (about 100 iterations in Pi Agent harness) - this is the type of thing that stronger open models (Kimi K2.6, GLM 5.2) are able to do.
Can anyone explain what’s the story here? Is this just a re-skinned qwen? Who is deepreinforce-ai and why isn’t this model listed on their website? How does it self-improve, does the model change on disk - or just during a single context run it gets better?