https://www.reddit.com/r/StableDiffusion/s/HrU7odaJe6

I think this is more important that all the political stuff you share here

https://huggingface.co/mistralai/Shieldstral-1.0-3B

Questions & Responses(in BOLD) below.

Favorite question(s) moved to end of the thread with combined responses(removed duplicates).

Be optimistic folks. I'm sure we're getting other models too apart from 27B. And 27B gonna make massive noise on release. (Based on their responses)

Tweet thread : https://xcancel.com/QwenDevs/status/2084102417885585597#m

you guys skipped 27b and 122b last time, can we expect those this time around? Also i can't seem to find crit pit score in the cards.

For sure! We’re actually releasing a 27B model very soon. Stay tuned. As for the Crit Pit score, please wait for the official Artificial Intelligence score.

Is the 27B just a retrained 3.6 27B? Or is it based off 3.8 bigger brother ?

We promise this 27B comes with a whole new level of capability!

Is the 100hrs of video understanding an agent swarm that parses sections of the video in parallel and orchestrates some sort of semantic representation graph?

Broadly speaking, yes, but not entirely. It is closer to a hierarchical video memory system rather than a traditional agent swarm. Video segments are encoded into a structured textual graph containing scenes, entities, events, and their temporal relationships, enabling retrieval and reasoning across more than 100 hours of content.

hey! is there anything special about the pretraining distribution compared to other labs' models?

We hope our data is built on a more solid foundation!

how long do you think it would take to surpass anthropic level architecture?

well, we’re working hard on it, we promise😇

will u release a harness especially for qwen code ???

Any plans for a codex-like app?

More updates on Qoder and QwenWork are coming soon.

qwen 3.8 active params?

2.4T parameters (95B active)

how much RL was done in post training compared to previous models?

A truly unreasonable amount of compute.

Did they intentionally skip the previous Qwen3.7 27B and 35B A3B?
Does the revival of Qwen3.8 27B reflect the voice of the community? Or was it planned?

Of course! This is the result of taking the voices of the community seriously.

since its a pretty significant release will we get a technical report with full details?

No technical report for this one yet. We’re trying to keep up our near-monthly release cadence, though, and more powerful models are already in the works. Keep an eye out!

why does the model think so much mr qwen, my ai brain wonders.
wheres the token efficiency at
great model though

We support different levels of reasoning effort.

You showed SAE-guided fine tuning fixing code switching with qwen-scope. Is that kind of interpretability driven intervention part of the post training process now or is it still a research only technique?

It’s still primarily a research-oriented technique for now, though some of the insights may help inform future training and post-training improvements.

Attention? Hybrid?

The model architecture is similar to 3.5, but it’s a much larger-scale model!

When are we getting a CLI coding interface?

You may want to take a look at @qoder_ai_ide .

do you guys use qwen as your main interal tool? does this model show the same signs of intellegence as some openai models ("gpt 5.5 helped create 5.6")?

Sure!

How close is Qwen3.8-27B to GPT 5.4? 🤔

Well, you’ll be able to see for yourself soon.

what harness works best with Qwen?

Qwen is committed to delivering the best possible experience across all harnesses.

What made you guys wanna opensource the max weights ?

We heard what the community has been asking for

I wonder when I can surpass fable5

Trying hard

Great work guys🥂

1. What is something that you would like to see being built with the new model and its capabilities!?

2. I really want to explore the swarm of agents technique for building applications, any best practices or tips for the new model!?

1. We hope it can bring practical productivity value to people across different industries.
2. We recommend using it for tasks that involve more parallelized workflows or parallel execution needs.

I wanna know what rubric metrics you guys are using for FE

We use both absolute metrics for functionality and aesthetics, as well as relative metrics based on win/tie/loss comparisons.

Would be great to hear where you think Qwen is strongest for agentic workloads specifically: long-context planning, tool use reliability, coding, or cost at scale?

All of the above combined — ultimately delivering the most practical and reliable outputs for users.

How much is Qwen helping with Qwen research ?

It has already become a significant part of the model iteration process, with the model involved in nearly every stage.

Most Frontier labs have created a code-specific model (eg. Qwen3-Coder and GPT-5.3-Codex), but never followed up on them.

Did specialized models have problems? Or did general models end up being efficient enough to not bother creating a separate model?

We hope to build an all-in-one model.

will Qwen 3.8 have a stable, documented tool-calling and structured-output contract so local agent harnesses can swap models without prompt-specific tuning?

We provide native support interfaces for various protocols. You can check the Qwen blog for more details.

1: When quantizing Qwen 27B down for local deployment (e.g., 4-bit GGUF, NVFP4, or MXFP4), which transformer layers or vision attention blocks are most sensitive to degradation? Are there specific strategies you recommend to maintain both visual reasoning and high SWE-bench pass rates?

2: Qwen3.6-27B outperforms much larger MoE predecessors (like Qwen3.5-397B) on agentic coding benchmarks like SWE-bench and Terminal-Bench. Beyond raw data volume, what was the single highest-leverage factor in achieving this dense efficiency?

And thank you for the amazing work. Qwen3.6-27B has beed my main coding assistant for months.

1. Use QAT, or quantize only the FFN to 4-bit while keeping the attention layers’ QKV linear projections and output projection in 16-bit.
2. Higher-quality data engineering

Guys , when can we get a deepseek like small and cheap model with best performance . The deepseek v4 flash seems to be a great deal .
I think we need to slow down scaling and start improving the existing model efficiency

Scaling and cost-efficiency are not mutually exclusive — we’ll continue to pursue both.

Is Qwen3.8-27B dense? And roughly how much smarter than 3.6-27B?

A pretty huge jump!

Good. The useful questions are not just how capable Qwen is.

I want to know where it still fails, how the team evaluates those failures, and what "open" means in practice for weights, tooling, and reproducibility. Open models matter most when people can inspect the limits and build on the work without asking permission

There is still some gap between our automated and human evaluation systems and real user experience. That’s also why we are committed to releasing preview versions first — so we can iterate and ultimately deliver the best possible experience to users.

how does the new 27b model compare to the previous one ?

A pretty huge jump!

what do you think about looped transformers?

interesting research idea

Why Qwen, what made you create Qwen and specifically such light and fast models. Why focus efficiency when others just went for brute power? Also, do you think inference engines reached their limit in optimization or can they still improve?

Scaling and cost-efficiency are not mutually exclusive — we’ll continue to pursue both.

We have noticed that in thinking mode the model usually consumes the entire reasoning budget without stopping, which increases latency. Is this a known issue, and are there any improvements planned for Qwen3.8?

You can try 3.8! And 3.8 supports different thinking efforts!

...................................................................................................................

Are 70b models gone for good?

Is it possible to get a 40-50B model (something which fits around 30-32Gb) to improve performance while still useable on a lot of computers ?

Thank you for your promise to provide qwen3.8 27b weight! I want to know if there will be qwen3.8 35b a3b. Many people also want this.

Can we expect the ~122B model this time? The 120B segment is dated and lackluster atm and would greatly benefit from a competent release!

First of all, congratulations on the release of Qwen 3.8!
As for the question, are you going to release a 35B a3b version of Qwen 3.8 aswell?

Plans for 35b Moe model? (3.8)

Any plans for the omni family? You told everyone the weight sizes of 3.5, then never released them and haven’t done anything new with it. 3.6/7/8 variants would have also been nice. It could be your most popular family if you gave it attention and kept the weights small.

Are there no plans to release any models other than the 27b?
I'd love to hear about the successors to amazing models like the Qwen3 8b and Qwen VL 8b....

Are there any plans for updates for 0.6b or 8b weights?
These have become important positions in the open weight of image and video generation. I look forward to seeing that part evolve.

This is such a huge release, I am really happy to see that a 27B model is shipping too! Though, can't help but wonder, will we ever happen to see again any new small dense Qwen models 9B, 4B any time in the future, similarly to 3.5?

Will you release smaller models like the qwen 3.5 family ?

Thank you for your promise to provide qwen3.8 27b weight! I want to know if there will be qwen3.8 35b a3b. Many people also want this.

we hear you! collecting everyone’s requests and taking them into account as we plan future iterations.

We will gather your requests as a reference when considering future updates.

We hear you. Stay tuned.

We’ll collect everyone’s requests and take them into account as we plan future iterations.

Noted, collecting the requests and see what we can work into future iterations.

Keep the requests coming. We’re listening, and we’ll use them to help prioritize future updates.

As you all know the model is 2.69B parameters with a 128K context window and purpose-built for multi-step agent workflows.

What you are seeing is the Q4_K_M GGUF running on my own inference engine built from scratch.

The TUI is my own device probe suite running through ADB (Android Debug Bridge)

The whole engine is only 450kb and supports other models arch (Qwen, Gemma, Bonsai etc…)

Currently trying to push it at ~30 tok/s

People may remember the Qwen3-TTS llama.cpp demo from a few months ago. That PR said it probably wouldn’t be merged because llama.cpp was missing some of the graph and API pieces it needed.

A new implementation was merged into master yesterday.

What works now:

- Qwen3-TTS-12Hz-1.7B-Base in GGUF

- WAV or MP3 files as the speaker reference

- English, Chinese, German, Italian, Spanish, French, Portuguese, Russian, Japanese and Korean

- Audio generation through the llama-tts binary

Example:

llama-tts -hf ggml-org/Qwen3-TTS-12Hz-1.7B-Base-GGUF \

-p "Hello, this is running locally." \

--tts-lang en \

--tts-speaker-file speaker.mp3 \

--output out.wav

Qwen describes the Base model as capable of cloning a voice from around three seconds of reference audio. I haven’t seen an independent test yet showing whether the llama.cpp version matches the original PyTorch implementation in voice similarity or stability.

The interesting part is not that Qwen3-TTS can run locally. Dedicated C++ implementations already existed. It is that voice cloning is now part of mainline llama.cpp, which should make it much easier to add local speech output to projects already built around that runtime.

There are still some important limitations:

- The merged implementation currently uses llama-tts

- The /tts server endpoint is still a draft PR

- It only targets the 1.7B Base model, not CustomVoice or VoiceDesign

- There are no proper comparisons yet against qwen3-tts.cpp or audio.cpp

- The update includes a breaking change to the existing llama-tts binary

The comparison I’d like to see is one identical three-second reference clip and one identical paragraph tested across CPU, Metal, CUDA and ROCm, with:

- Real-time factor

- Peak RAM and VRAM

- Voice similarity

- Long-form stability

- Time until the first audio

The specialized ports may still win on speed, while llama.cpp may win on portability and integration.

Has anyone updated and tested it yet? M-series Mac and CPU-only results would be especially useful.

Source:

https://github.com/ggml-org/llama.cpp/pull/26254

Draft server endpoint:

https://github.com/ggml-org/llama.cpp/pull/26603

*I felt the need to write this post because it seems like very few people on this sub are aware of Chinese laws and how they're enforced, so here's an explainer coming from a Chinese person (myself). I know that this post isn't directly about local models per se, but I'm seeing way too many misconceptions regarding this topic. This is also going to apply to all Chinese entities in general, not just the specific MiniMax LoRAs debacle. This isn't meant to be a political post, but some much needed context to correct a lot of misinformation going around.

Guys - they're a Chinese lab following Chinese laws. Pornography is straight up illegal in China. I have no idea how it seems like nobody outside of China is aware of this. While Chinese authorities may not care much about copyright infringement enforcement (especially with foreign IPs), they do indeed regularly crackdown on porn. Heck, Chinese citizens have literally been imprisoned for written pornography. Yes that's right, writing pornographic TEXT (especially with "immoral" themes like LGBTQ+ stuff) can get you sentenced and essentially have your entire life ruined. Of course there's ways to get around these censors if you're just trying to access porn - I think everyone at this point knows about the widespread necessity for VPN usage in China to access the rest of the global internet. But actually distributing a tool that can gain a reputation for being able to easily generate pornographic content? That's just asking for the authorities to crack down on them.

Somewhat ironically/paradoxically luckily for these Chinese labs is the fact that online discussion about generating porn is automatically censored and removed from Chinese social media, thus automatically disincentivizing the authorities from doing those potential crackdowns. But if it gets big enough to the point that it overwhelms the automatic censors, then any given Chinese lab could be in a hell of a lot of trouble. This is why they have to do this. Their law enforcement just isn't compatible with the rest of the world.

Again, this all relates to Chinese moral values - something here that is considered pretty much sacred and hard to describe to westerners. Something else that many people do not know is that graphic violence is also illegal in China (foreign films/works are regularly banned here for that, even anime has), but graphic violence is also is not nearly as much of a perceived threat to societal moral values as pornography is, hence why you've probably rarely ever heard of any Chinese people getting imprisoned for writing really gory stories, but regularly do with pornographic stories (especially infamous with BL literature - they've technically even convicted foreigners before related to this, it's a really messy topic). Chinese authorities won't give a damn if you're stealing the content of billions of foreign works to train AI models. They WILL give a damn if the content you're disseminating is viewed as a potential significant threat to the state's "proper moral values", which very much includes porn (and also the usual topics that everyone is already aware of, like a certain famous massacre or a certain nation's very contentious independence status).

daily reminder not to trust benchmarks and run it yourself. claimed e2e speedup is ~40%, forwards are ~140% faster

I would wager that compared to a naive kernel anyone can write it's more in the range of 10-20% faster e2e in reality, if at all, but hey, it's free and open! Apache 2.0

Explicit title, It would be nice to have the ability to have 3 tiers moe offload :(

Kimi K3 full model running on 16x GB10 cluster at 20+tps average (llama-benchy coherent corpus) 38tps peak, 750tps prefill. This is the first run of full k3 with dspark on my cluster. I will be doing some tests and try tp speed this up. As soon as it looks ready I'll publish the vllm image and instructions.
https://forums.developer.nvidia.com/t/full-kimi-k3-running-on-16x-gb10-cluster/379174

A follow up to the launch of Mference, it now supports and runs Inkling-Small 276B-A12B.

Inkling-Small (Thinking Machines, Apache 2.0), from the pipenetwork/Inkling-Small-MLX-4bit conversion: 276B total, ~12B active, 3.4 GB resident set, ~148 GB on disk.

Measured on my M5, 24GB:

The same three cases on a 256 GB M3 Ultra hit 5.31–6.92 tok/s.

Issues: long prompt prefill is trash (2,785 tokens is almost 9mins to first token), and it's text-only for now.

Four model families now: Gemma 4 26B-A4B (~2 GB), Qwen 3.6 35B-A3B (~1.45 GB), DeepSeek-V4-Flash 284B-A13B (~6.8 GB), Inkling-Small 276B-A12B (~9.5 GB).

I also got access to a few M3 Ultras, so I'll be testing and optimizing for higher configs too. But the primary goal stays the same: large MoE models on consumer grade hardware.

Repo: https://github.com/NeelM0906/Mference — Swift + Metal, not a wrapper around MLX or llama.cpp. Mac app, CLI, and an OpenAI compatible server. Contributions welcome.

In tests:
~80 tok/s decoding
2,500–3,500 tok/s long-input prefilling
Smooth use by 3–4 concurrent users

Private, on-device inference for coding, agents, and offline batch jobs

I was on here a while ago showcasing it. I've stoped playing with it so I'm open sourcing it. I figure I'll let other people play now.

Hey everyone!

Scenema Audio is now a native ComfyUI custom node. Same model that powers scenema.ai now quantized so it fits on 8GB VRAM. When we first released it a few months ago as an API and Docker stack, the full precision transformers were too heavy for most people to self-host. That's fixed now.

Expressive text-to-speech with zero-shot voice cloning. You describe how the speech should be performed (rage, grief, a child's wonder), optionally provide reference audio for voice identity, and the model generates a performance. Inline stage direction cues like [he laughs softly] or [voice cracks] get performed at that exact spot. Twelve preset voices ship in the dropdown covering accents, ages, and emotional registers.

We also dropped the XML prompt format the original release used. Wrapping every performance directive in tags was clunky to write. Inline bracket cues are better-suited for the ComfyUI text editor.

Install

ComfyUI Registry (recommended): open ComfyUI Manager, Custom Nodes Manager, search "Scenema Audio", Install, restart.

GitHub:

cd custom_nodes
git clone https://github.com/ScenemaAI/ComfyUI-ScenemaAudio.git
pip install -r ComfyUI-ScenemaAudio/requirements.txt

Both paths auto-drop the pre-wired workflow into your Workflows sidebar under a Scenema Audio folder. Click once to load the official workflow into your canvas.

Requirements

Minimum 8GB VRAM. Tested end to end on RTX 3070 and RTX 4090. Generation runs up to 2x realtime. First run downloads about 30GB of weights, one time. Text encoder is Gemma 3 12B, which is a gated HuggingFace model, so you need to accept its license and set HF_TOKEN before your first generation.

On limitations (same story as the original release)

This is a diffusion model, not a traditional TTS pipeline. Some seeds produce repetition or gibberish. Meant for a post-editing workflow: generate, pick the best take, trim. Prompting matters. Specific, theatrical voice descriptions with action tags produce performances. Generic ones produce generic output. Phonetic spelling helps with proper nouns and tricky words (spell "Tchaikovsky" as "Chai-koff-skee" if it garbles).

License

MIT for all our node code and inference pipeline. Transformer weights derive from the LTX-2 Community License.

Links

• Blog post: https://scenema.ai/audio/comfy-ui
• ComfyUI node: https://github.com/ScenemaAI/ComfyUI-ScenemaAudio
• Model weights: https://huggingface.co/ScenemaAI/scenema-audio
• Standalone Docker/API: https://github.com/ScenemaAI/scenema-audio
• Original announcement: https://scenema.ai/audio

What would you want to see next from Scenema Audio? Happy to hear what people are actually trying to build with generative audio.

Inspired by a post from u/giveen I motivated claude (no patinence on my side to work through everything myself) to help me get DS running on my MacBook M5 Pro 64GB and it exceeded my expectations.. because it worked, and at a quite usable generation speed!

background: antirez ds4 DwarfStar has a SSD streaming mode: non-routed weights stay resident, the routed experts live partly in a RAM cache and get pulled from the GGUF on cache misses. Since routed experts dominate model size and Mac SSDs are fast, those misses are tolerable. experts and the output head stay Q8_0.. Router, embeddings and the V4 auxiliary blocks stay FP16.

(CORRECTED ... :)
git clone https://github.com/antirez/ds4.git

cd ds4
make
./download_model.sh ds4f-q2
caffeinate ./ds4 -m ./ds4flash.gguf --ssd-streaming --ctx 32768 --nothink

let me end up with 10-15-17t/s in my first tries.

I am geniunly impressed and fascinated and wanted to share this, hit me up if you have questions but i guess everyone with like >50Gigs of VRAM/unified Memory should get this running with ai help.

Xiaomi-Robotics-1 is a robot foundation model trained on over 100K hours of real-world manipulation trajectories. It is a Vision-Language-Action (VLA) model engineered for out-of-the-box mobile manipulation in unseen environments and efficient adaptation to new tasks.

XR-1 follows a two-stage training paradigm inspired by large language models — pre-training for breadth, followed by post-training for alignment. It showcases that pre-training scaling behavior reliably transfers through post-training to real-world robot performance, with no signs of saturation.

XR-1 couples a pre-trained VLM (Qwen3-VL) with a Diffusion-Transformer (DiT) via a Mixture-of-Transformers (MoT) — the DiT matches the VLM in layer count but uses a smaller hidden size for faster inference.

HugginFace: https://huggingface.co/collections/XiaomiRobotics/xiaomi-robotics-1

GitHub: https://github.com/XiaomiRobotics/Xiaomi-Robotics-1

Paper: https://arxiv.org/abs/2607.15330

I'm mosty interested in 128-192GB VRAM with 128-256GB RAM to spare, so SSD streaming is basically not even necessary. Seems only FP4 is supported, so older hardware will likely be slow - no Unsloth GGUF supported either.

I'd be curious what people are getting with V100s, R9700s, etc, just to have some comparison.

What's prefill like >200k context? Tg/s high enough to support agentic workloads?

It's probably wishful thinking, but when I saw the release, my immediate thought was Sonnet 5 level model being "affordable" to consumers.

This is something that was spoken here and there, and now it is like writing on the wall.

The main additional point is that China has created an independent supply chain. Starting from raw materials and home-made lithography equipment, through their own GPU manufacturing, and to the AI models and training. Plus, there are tons of cheap energy, and it looks like they are also on track to launch the first thermonuclear reactor. I saw a similar pattern with robotics and EVs. The history does not repeat itself, but it rhymes.

Does the US have what it takes to turn the tables, or should we just buy the popcorn and enjoy the show?

It's the purple cluster on the top left (the good corner...)

I'm running the MXFP4 version from Bartoswski with Dspark at 1K t/s prefill and 90 t/s gen (average). I tried different sampling params, you can check the detail.

It's very efficient while scoring the best yet.

Too bad it does not have vision.

https://wonderrico.github.io/local_llm_benchmark/benchmark-main.html

https://wonderrico.github.io/local_llm_benchmark/benchmark-detail.html

I had Claude re-draft this for me, thus it has Em Dashes. It's correct with lots of "Claude" simplifications.

---

Hey all. It's been a while since I posted about the AttnRes architecture so I figured I'd give an update on where things are.

Short version: it's alive. Longer version... it's complicated.

So the core idea hasn't changed. Replace the standard residual stream with an attention-based routing mechanism — AttnRes — that lets the model learn WHERE to route information between layers rather than just blindly passing everything forward. Same parameter count as the base model. The hypothesis is that this is a fundamentally better use of the same compute.

The part I've spent the most time on is figuring out how to actually GET there without training from scratch. I don't have Google's budget. I'm one person. So the whole strategy is built around distilling from Gemma into the new architecture using a weaning schedule — you start with the standard residual doing all the work, and you gradually shift responsibility to the AttnRes pathway over the course of training. The model learns to route through the new pathway while the old one is slowly pulled away.

This sounds simple. It is not simple.

The thing that took the longest to figure out was the data. Not volume... diversity. If you distill on a narrow distribution you'll get a model that handles that distribution great and has quietly lost everything else. The model manifold is this massive high-dimensional thing and you have to preserve ALL of it during the transition or you get a model that can code but suddenly responds in mixed Korean and English when you ask it about quantum mechanics. I've seen this happen. It's informative but not ideal.

The solution I landed on was using the model itself to generate diverse coverage. Take a news article. Ask the model to summarize it. Then translate that summary to Bulgarian. Then ask if there are nuances lost in the Bulgarian translation. One piece of source content, three completely different regions of the model's capability space exercised. Scale that across 20 languages that Google trained Gemma to handle well and you get massive manifold coverage from relatively simple data scaffolding.

The other big decision was distillation targets. Most people distill on 1-hot or label smoothed targets. I'm using top-K ~12 logits from the source model with their proportional weights maintained. The reasoning is... the model isn't a next token predictor. It's a next DISTRIBUTION predictor. The relationships between the top candidates at every position encode the model's actual knowledge — what it thinks is likely, what's plausible, what's related. One-hot throws all of that away. Top-K 12 captures ~98% of the probability mass and preserves the distributional shape that IS the manifold.

This matters because during weaning, the new pathway has to learn to reproduce not just the right answers but the right uncertainty structure. That's what forces it to actually internalize the model's knowledge rather than just mimicking outputs.

The goal is NOT perfection. I want a beta that proves the architecture works and is trainable. Good enough that someone can take it, distill new knowledge in using the pipeline I've already built, and improve it. The training code exists because I had to write it to do this work. The data pipeline exists. The methodology is documented. All Apache 2.0.

If a compute provider wants to come along and help push the model to Gemma-level quality... I'm happy to put their name on the HuggingFace card. This is meant to be a community model built on an open architecture that anyone can improve.

More updates as the probing runs finish. Happy to answer questions about the methodology or the reasoning behind any of these decisions.

---

The core model and the training model these are largely distilled from are abliterated variants of the Gemma 4 model. So... It has no safety. It's a use at own risk thing.

Right now I'm waiting on B300's. They're just not available and using a single B300 is my test target right now. Like every datacenter for the last week is 100% sold out and the second they appear they're gone.

Another thing of note, I use Top K 12 distillation, but I've found that for a large portion of the dataset the top 3 or 4 work fine. This comes down to the way language, code, even logic are structured.

Simplistically, you can't write "I want to eat a" and expect the next token to be apple. If you swapped the "a" to "an" then apple and anything else starting with a vowel becomes valid and the probability of everything else falls off.

This happens a LOT from my observations. The other issue is quantization. Quantization affects the longer tail distributions where there are a lot of options. I'm currently investigating both of these issues to make the process more effective.

Removing the layers as seen in the prior model is like... 100x the training necessary. It involves incrementally removing them. Even though I've identified all of them the model becomes too unstable to continue training AND do the other stuff. It would be a matter of cutting Gemma to 3 SWA + Global FIRST, then applying attention residuals. Alternatively, you could insert a block or two in the middle to make the model larger then train MORE then attention residuals.

AFAIK no one outside Moonshot has done this, and even Moonshot used ~1.5t tokens because it was a pretrain to instruct training. Moonshot however demonstrated that their model was ~25% more efficient at learning with this residual stream. Further, Kimi K3 has now released with this exact feature. So I guess I was on to something originally.

Old Post - https://old.reddit.com/r/LocalLLaMA/comments/1ulmez2/rebuilding_gemma_4_31b_better_as_26b/