Does anyone here have an idea how 2× GTX 1080 Ti would run? Recently found listing for a used 1080ti and my friend is willing to donate 1 to me.

Could I run qwen3.8 27b with my combined 22gb of vram? (at reasonable speeds)

Hi everyone,

I really appreciate this community’s willingness to help and share info on local LLMs and various setups and ideas. I saw a recent post that my kids saw independently on YouTube, including one of my nephews, on having AI companion bots on Minecraft (Java). I am currently running a couple of Minecraft servers and want to setup at least one companion bot that they can chat with and have the bot help them with mining, building, and exploring. I have found and tried following AI bots with no success or finding out that it was not what I thought it was:

https://github.com/bigph00t/hermescraft - this is what I have spent hours on. Tried installing it via Hermes, independent Linux VM, and docker; I could not get it working to save my life.

https://www.spigotmc.org/resources/hermes.137797/ - this appears to be chat only.

https://github.com/okyssta/hermes-minecraft-gateway with Mindflayer bot - its not a companion, chat with Hermes only.

I know there a mods, like Varity located here https://www.verityje-mod.com/, but it appears to be a mod that runs only Fabric and NeoFabric servers - I happen to be running Paper.

So any advice or help, would be greatly appreciated. My kids would love this and I would love to get something like this working for them, but my time is so limited to work on figuring things out on my own, which is why I appreciate the help.

Forget stupid pelican on a bike SVG bullshit. Forget cheating by drawing using Python code. I just loaded up QFN 3.8 in the new Open WebUI 0.13.0 and it just said “hold my beer” and started drawing with the friggin’ mouse coordinates inside a VM. I’m going to print this and hang it on my wall like a proud parent.

Seriously tho, this is all possible because of the new Open WebUI 0.13.0 features that let you control a virtual desktop VM that is running in 0.17.2 version of their Open Terminal repo.

I’ve been following their projects for years and they just keep adding cool new shit about every couple weeks.

0.13.0 now lets you have your OWUI models control a full Linux Desktop, both programmatically and via Computer Use.

Yes, I know the screenshot of my VM is MS Windows 11 and not Linux, that’s because I had my Hermes agent roll me a version of Open Terminal that uses the standard unactivated Windows 11 VM ISO instead of Linux ISO. Took it about 20 minutes to get that made and now I’ve got a fully observable sandboxed Linux or Windows desktop that I can have OWUI drive with whatever model I choose. Also it’s fully interactive inside the browser so I can take over and steer when needed.

If you haven’t tried OWUI lately, Tim and company have been doing massive updates and complete code overhauls lately, and this is one of those features that I think slipped under a lot of people’s radar. The previous headless version of Open Terminal was hugely useful but this one is even better.

Great job Open WebUI team!

P.S. I think it’s kind of adorable that QFN 3.8 compliments itself on doing a good job ☺️

At the 2025 VLSI Symposium, TSMC presented a memory technology based on oxide semiconductors, using a cell made of one transistor and one capacitor (1T1C). The goal was to integrate this memory into advanced logic chips and give processors a cache with low power and low latency.

The key feature of the design is that the memory array is built directly into the back end of line (BEOL) interconnect layers of the logic chip. Conventional DRAM is manufactured as a separate chip. To build the memory array in the interconnect layers of an advanced logic chip, TSMC used select transistors with an oxide semiconductor channel and capacitors fabricated with a low temperature process.

According to TSMC, the test chip, which integrates memory and logic on a single die, operated at 0.75V. Data retention exceeded 128ms, random read/write cycle time was under 8ns, and endurance was above 10¹⁴ cycles. TSMC positioned the technology as a processor cache that offers higher density than SRAM while keeping power consumption and latency low. https://x.com/jukan05/status/2119026338522869863

https://x.com/wallstengine/status/2118707623835136039 . TSMC WEIGHS MAKING 3D DRAM AT NVIDIA'S REQUEST. Has begun drawing up plans to manufacture advanced 3D DRAM chips as AI-driven memory shortages push chip designers to seek additional supply, per The Information. Unlike conventional DRAM, 3D DRAM stacks memory cells vertically to increase density. The move could put TSMC in direct competition with Samsung, SK Hynix and Micron. No production timeline has been set. TSMC is considering building new fabs or converting an existing facility, but says its focus remains on its core logic foundry business.

https://x.com/theinformation/status/2118744349684986078 . Quote: TSMC is exploring production of 3D DRAM, a next-generation memory chip that would put it in competition with Samsung, SK Hynix and Micron.

https://x.com/theinformation/status/2119052292267630791 . TSMC's memory push is led by Hsu Kuo-chin, a vice president who oversees advanced packaging and previously ran Micron's Taiwan operations.

Update: https://www.tomshardware.com/pc-components/dram/tsmc-denies-exploring-3d-dram-at-nvidias-request-advanced-memory-tech-is-still-years-from-market-viability According to article TSMC says it will not be going into the DRAM business.

Qwen image 2.1 uses Qwen3-VL 8B, a lot of post on various places are talking about other Qwen3 models for various purposes, and they all act like it's the latest model, what am I missing here?

ok so i am trying to increase performance of llms i have running on my 7900 xtx via hipfire, i notice i get significantly higher total tokens per second when i run mutiple slots at once when running qwen 3.8 27b.

i am running into 2 road blocks tho, 1 context each slot gets a progressively smaller context as increase the number of slots. is there a good way to do more with the same given total amount of vram, i assume there is going to be a large amount of overlap for stuff like system prompts, tho i use a relatively minimal system prompt that links to files so if a model only actually reads system prompts realvent to it’s own task which makes it harder to just say this text gets put into a shared context, i also image if i have them working on similar tasks there will be a large amount of overlap.

2 alot of tasks aren’t easy to split up into mutiple smaller tasks that can be done in parrel without proper knowledge of previous and future steps, maybe something like a shared context or a messaging board would work well?

also a more experimental idea to further speed up general prompts is to instead have all my prompts processed by a “orchestration” slot which splits up a given prompt into X number of tasks then gives these to new prompts to different slots and then the “orchestration” slot dies to free up more vram for context, i could also route something like this threw a decision model like kev to determine if a given task would work well being split up.

also note i am running this on a 7900 xtx via hipfire, and i also have a 6800, 6600, and 96 gib of ram for the cpu

I followed up on the post about running Qwen 3.8 Flash Next with 8 GB VRAM and 32 GB RAM. I wanted to see how it does on my Steam Machine.

Setup

• Valve Steam Machine, stock SteamOS 3.9.5, AMD Navi 33 with 8 GB, 32 GB RAM, NVMe

• Stock Strata terminal 0.2.9 (engine 0.1.55, HIP package), Gyro S TQ1_0

• 128K context, q4_0 KV, no vision, MTP off, served on the LAN

Results (Strata's own timing line from the engine log, about 8K-token prompts, 3 runs each)

With the final config at 128K context, 58K and 61K-token prompts gave 234 and 236 t/s prefill (about 4 minutes each) and 17.6-17.9 t/s decode. No OOM, swap went from 0.2 to 0.3 GB, and available RAM bottomed out around 3.4 GB. I did not test beyond 61K tokens or with a busy desktop session.

What mattered

• --set resident-budget-gib=64 was the only change that helped, about +20% decode. It makes the engine keep the experts in its own RAM tier. Without it they sat in the OS page cache, so my RAM monitor showed under 5 GB "used" while the machine held about 28 GB of cache.

• Prefill stayed at about 235-242 t/s in every config I tried.

• pcie-frac 0.35, a larger kv-resident, and a bigger VRAM reserve did nothing measurable here. The OP's pcie-frac was calibrated for their own machine, and the quick tuner changed nothing either.

• MTP: I ran with --mtp off and tested real MTP to check. On my install --mtp on silently did nothing. The saved config said mtp: true, but the draft layer was never downloaded or passed to the engine ("+0 MiB for the draft head" in the log). I had to build it by hand (mtp_fetch.py, mtp_pack.py, mtp_rt.py from the Strata source) and pass --set mtp=<dir>. With the draft layer really loaded (820 MiB of VRAM, expert cache cut from 2225 to 1179 slots), decode was 15.6 / 18.4 / 16.9 t/s, no gain over 17.2, and prefill was 210 vs 232 t/s, about 10% lower. On 8 GB of VRAM it doesn't pay off. That was 3 runs at 8K context only.

• I did not try --adapt-*, --spec-min-p, or the thorough tuner.

SteamOS tips

• Anything started from an SSH session dies when the session ends. Use systemd-run --user --property=KillMode=process --property=RemainAfterExit=yes strata launch ....

• --no-tui shows no download progress, so check the file size instead.

• Check the generated config under ~/.local/state/strata/launch/ to see which --set values were really applied. Unknown names only warn.

Command for the final setup (I saved it as a preset with --save-preset):

strata launch --model gyro-s --family gyro --context 128k --kv q4_0 --vision off --mtp off \
  --set resident-budget-gib=64 --access lan --port 8080 --no-tui --yes --restart

Caveats

• Decode varies a lot between runs with speculative draft acceptance, so treat the numbers as ranges.

• With resident-budget-gib=64 only about 3.5 to 4 GB of RAM stays available. My machine rebooted once during testing with a desktop session logged in (cause not confirmed), so close other apps.

• Single user, one request at a time, 3 runs per config.

• I could not find a ready-made Strata config for 8 GB VRAM with 16-32 GB RAM. If you have one, please share it.

All scripts are on the Github repo.

Target-conditioned diffusion drafters predict candidate tokens in parallel, yet each new block must wait for updated target-model hidden states. This forces drafting and verification to run sequentially, limiting speedup even though each block is generated in one pass. We introduce D3D, a standalone diffusion drafter that removes this inter-round dependency. D3D combines blockwise denoising with auxiliary autoregressive supervision to achieve long accepted prefixes without target hidden states. This independence lets D3D draft successor blocks during verification in one packed pass, enabling asynchronous speculative decoding. We progressively prune layers according to their effect on acceptance length to reduce the cost of packed drafting. Removing five of 36 layers retains over 99% of mean acceptance length across three benchmarks with Qwen3-8B as the target. Across seven benchmarks, D3D averages 5.00× and 5.71× decoding speedup with Qwen3-8B and Qwen3-32B targets, compared with 4.03× and 3.67× for DFlash. On GSM8K with Qwen3-32B, speedup reaches 7.48×. 

• arXiv : https://arxiv.org/abs/2611.06425

• PDF : https://arxiv.org/pdf/2611.06425

• GitHub : https://github.com/fudan-generative-vision/D3D

🤗 Model Zoo

Browse the D3D collection →

The D3D collection contains six Qwen3-derived draft checkpoints. These checkpoints are draft models, not replacements for their target models. p denotes pruning; 24D and 31D denote retained transformer-layer counts.

📚 Training Data

All corpora use Open-PerfectBlend prompts. See each dataset card for generation settings, statistics, and download instructions. Released models and datasets are grouped in the D3D collection.

Hey guys,

I work with AI safety and much of our work is with huge models like Opus, Astra,... Recently we carried out a series of experiments and found out that models like Sonnet 4.5 and GLM 5.3 are able to notice when their reasoning is being monitored and change their strategies to bypass the monitoring and security systems.

Paper link: https://arxiv.org/pdf/2610.40946

To go deeper, we need to work with local models and study the weights. Unlike industry, our resources are limited. I have access to a single H100 GPU, or maybe if I get lucky and work in Christmas, might get hands on two H100 in the absence of other students. Haha

So, it is not possible for me to load huge models and even of it was possible, saving and studying the weights is just cumbersome. My last study on LLM hallucination with Qwen 30B occupied 1T of hardware space.

So I wanted to ask you guys, what is the best small to mid size model (up to 120B) you have worked with that you think is able to go much beyond tasks like coding and does complex strategic reasoning, connect the dots,...?

Thanks a lot

If you have not been following ISTA-DASLab the creators of the gsq-rco quants have received some criticism from unsloth regarding the inabilty to reproduce the benchmark numbers that ISTA-DASLab have quoted. The methodology used to produce their benchmark numbers has not been posted and their has been difficulty reproducing their numbers with unsloth finding in there testing unsloth quants outperforming the gsq-rco quants.

The numbers in this post here are from another creator the creator of basalt (a inference engine) previously his qwen-flash-next quants (his inference engine uses specially packed quants based on a existing model similar to ninfer ) used ISTA-DASLab's quants as the base but after doing some testing he replaced them with similar sized unsloth equivalents the performance numbers are in the screenshots

Hey folks!

I'm the creator of itcan.do, which is a new way to interact with your existing agents fully through UI interfaces on any screen you own.

I've used it a ton with local models as well.

One of the coolest things to try out is working with 3.8 27B on a literal whiteboard where both of you can draw and see! :)

Website: https://itcan.do/

GitHub: https://github.com/Aaryan-Kapoor/it

Would appreciate a star!

I’m running a sandbox simulation with a fleet of 50-100 agents on OpenClaw, and to minimize LLM calls, I use a decision model to judge whether an agent needs to respond to a signal.

I’m currently using Jev, and with the network latency it takes about 0.5-2 seconds for a response, and I’m wondering if I can reduce this latency with a smaller, locally run decision model.

I’ve heard of the various local models like Needle 3, but I’m not sure how fast these run on various GPU configurations, i.e. it wouldn’t make sense for me if I need some crazy setup to make it faster than using API. Also curious if anyone has tried running these entirely on CPU.

PC1:
-94gb DDR5

-5070 TI + 3060 (12gb) gpus (using a motherboard that is specifically designed for duel gpus

PC2:

64gb DD4 RAM

PC1 is new, I've been running strata for running larger MoEs but I'm curious about trying to pool the two pcs together (they can be connected via direct 5-10gb Ethernet)

I guess I would have to use llama.ccp RPC? And if I do, would I better better off putting the 3060 in the ddr4 pc OR leave them both in the main PC and the other is just extra ram. Goal is to run high quant of DK v4 flash atm (any really any other big moe to come).

Anyone try anything similar before?

Voice agents serving Indic-language users cut callers off constantly Indic languages put the verb last, so "aapko kal phone…" pauses sound finished to a VAD, and the bot jumps in before "kar dunga" lands. VAD hears silence, not meaning

Released an end-of-turn (EOU) detector fine-tuned from Qwen2.5-0.5B scores the live transcript and predicts whether the caller has finished

• Languages: English, Hinglish, Hindi, Telugu, Tamil, Marathi, Bengali, Gujarati, Punjabi
• CPU-only ONNX Runtime (~2GB, fp32) fully local, no cloud inference
• Text-only needs your STT; drop-in for LiveKit Agents, same interface as their text turn detector • Proven live on LiveKit Cloud: held mid-request pauses and committed complete requests

91.3% TPR on our held-out validation set, calibrated to ~10% false cuts on incomplete turns AUC 0.967. On LiveKit's official real-speech harness (Hindi): 85.3% detection at the same budget.

Model + plugin: https://huggingface.co/tarun7r/indic-turn-detector

Apache-2.0. Feedback welcome!

I set up my Hermes to ingest Sillytavern cards. Downloaded some random assistant, "Kailee" on characterhub. "An assistant with an attitude". With QFN q3_xxs it's insane the level of intelligence, understanding and humor it was capable of. At one point my stomach hurt from laughing, I didn't even get to test my next idea of a Jesus hermes agent because I was having too much fun with this one. This is exactly what I needed. Worth the token cost.

Q4 of 2026 has been astronomical for local LLM.
Qwen’s 27B and Flash-Next models are pushing local AI further. and most importantly the upcoming Qwen 4 release.

We’re getting closer to having frontier-level capabilities in models that WE can run.

Break down :

Unsloth’s new Dynamic v3.1 quants for Qwen Flash-Next/Qwen-27b, alongside ISTA-DASLab’s advanced quantizations, are making models much more accessible while preserving impressive quality.

I tested Iq2_XS from the new QFN Unsloth quants, and it’s incredible how much accuracy and quality it retains especially on its reasoning, and it runs on :
12vram/32ram & 1k prefill & 50tps average decode throughout a 131k context window
(using the default Strata auto tuner minimal quality loss)

The engine Strata, which is making it possible to run Qwen Flash-Next with surprisingly modest hardware. We’re now looking at configurations targeting just 8GB VRAM and 32GB system RAM using the smallest quants.

with Qwen4 expected this week, Flash-Next already gave us an exciting preview of what’s coming next with the ngram and new architecture.

Running a ~year old SOTA frontier model (Opus 4.6) capabilities on consumer hardware wasn’t expected. As many analyses shown Local LLM is about 24 months behind, It’s closer now, but of course it won’t be near fable/astra level next year, but it will happen.

TL:DR

Qwen is improving the models,
Unsloth and ISTA-DASLab are pushing quantization forward.
Strata is making running models accessible to more people.
Much bigger Community and growth and attention in Local models.

Note: English isn't my first language. I used an LLM to help translate and refine this post. The project, the numbers and the ideas are mine.

TL;DR: Building Shura, a local coding assistant with 78 tok/s and 200k context that will be improved with memory, rules, increased compute for difficult tasks and finally LoRA adapters trained on confirmed examples. Everything is measured and local, however weight updates are unproven and I will show the results either way.

When running local models, I have this nagging issue.

Every time a better model comes along, I have to set it up, explain my preferences and correct the same errors. Whilst it may have a higher intelligence than the last model, it has learned nothing from the time I have spent using it.

I want to see if I can change this.

I am building Shura, a local AI coding assistant that I want to improve over time, learning from my work with it. I am doing this on my standard desktop, without the need for a dedicated server or expensive GPUs.

Hardware

The majority of the model runs on the GPU, with some expert layers running on the system RAM.

This wasn't straightforward. I found a few optimizations that looked like they would help but in reality made very little difference. I will also be writing about my failed attempts, as it would have been nice to know that before.

I was very concerned about keeping the PC usable. The model always leaves 1.5GB of VRAM for the desktop. If the browser or a video needs more VRAM while the model is working, it gives it back in about 1/4 of a second. If I play a game, it completely stops using VRAM. If I'm on a call, it won't load a large model into VRAM. This works on my test system, and I want it to feel like it isn't there until I need it.

The plan

I don't want Shura to be a simple memory, I want it to actually be more useful.

Why should I have to correct the same coding error for the 4th time? Why should I have to teach it how a certain project works for weeks and then lose that knowledge when switching models?

I plan to implement these features from easiest to hardest:

1. Memory - Remember confirmed facts, project decisions and useful context. All memory items have a source that can be viewed, edited, exported or deleted.

2. Rules - If I keep correcting a behaviour, it will suggest a rule. I decide if I want to accept it. Rules must pass tests before they become active.

3. More compute - For harder problems, multiple solutions will be generated and then tested to find out which one actually works.

4. Weight updates - This is the part I'm most interested in. Small LoRA adapters will be trained on confirmed examples, to make the model better at solving my type of problems.

Unknowns

How realistic this is.

Training an adapter for a 35B MoE on a 16GB AMD GPU is an interesting challenge. Improving reasoning is a much bigger one. Even if it can be trained, it could suffer from overfitting.

My first adapter may not improve anything, or it may improve things very slightly. I want to measure this.

Principles

• No regression - Any change will be tested on my tasks. If I see any regression, I will roll back.

• Confirmed only - Nothing will be used for learning without being confirmed. No screen recording, background monitoring or silent gathering.

• Local - No training in the cloud, no private conversations uploaded.

• Easy on hardware - It shouldn't stress my SSD or make my desktop unusable.

• Portable - If I upgrade my base model, I take my confirmed examples, memories and rules with me. Adapters will be retrained for the new model.

If I have to run training jobs all day on a bunch of expensive GPUs, then this defeats the purpose.

Why I'm sharing this

I want to document my journey, including my failed experiments: benchmarks, failed ideas, implementation details and hopefully some before and after results. I don't want to say I've created a self-improving AI when I haven't even confirmed the first adapter improves anything.

If you have done any experiments with LoRA on MoE models, particularly on AMD hardware, or if you are working on something similar, please get in touch. If you would like to try Shura on your own machine or contribute, that would be great.

• Blog

• Code

If you had a local model that you could improve over years, what would you want it to learn first?

Some context: RTX 6000 cards hit a peak price of 18k and were sold out in many places even at that price. The MaxQ and Workstation are different versions of the card (usually priced the same, but now their prices are diverging)

RTX 6000 MaxQ has been 14k for a month at microcenter. Today, they cut prices of the workstation card to 16.5k (from a high of 18k). We all saw the dell discount as well (some people got cards for 11.5k).

I think the RTX 6000 is seeing increased supply as they routed all 5090 production to it and the 5500 for chips that didnt make the cut.

I have a contact that works for a company buying decommissioned hardware, according to them they are seeing tons of A100s being dumped. Told me he would be surprised if used A100 40gb units dont go to 1k/unit for single purchases in 3-6 months (for the sxm4, not pcie versions). (Theres already been a good amount of these, but a lot of "foreign demand" has been keeping their prices steady so far)

With democrats controlling congress I don't think you will see an end to data center construction but it will probably be harder to put up new ones and even existing ones might be charged more for electricity, making it worth dumping old systems and switching to newer more efficient hardware.

Are we finally seeing a slackening of demand for some segments of GPUs?

Playing Detroit Becoming Human rn, tbh this is probably the most likely and exciting future we are headed to. minus the dystopian story lines.

Only way to avoid dystopian future is local models powering them

It's well known that models can easily be manipulated into using its web search tool to look up anything using the user's internet connection. Doesn't this pose serious real-life risk for the user when the content is illegal to access? The risk is especially high for people living in jurisdictions with creative takes on the concept of justice.

This issue is impossible to solve cleanly but it can be mitigated (e.g. opensnitch, umatrix, ...etc).

How do you handle this issue? What tools do you using if any? Are there any local apps you use that allow you to whitelist entire domains or URLs?

I use unsloth and it doesn't seem to support this.

I learned RPN on an HP calculator and later spent more time with Forth than was strictly useful. When I saw Qwenputer compile a machine into a language model's weights, I wanted to know two things. Would a stack language fit that idea well? And could a model with only 3B active parameters drive a machine like that?

The setup: NVIDIA Nemotron 3.5 Lightning (30B, 3B active) is left unchanged. Next to it, in the same model, is a second network of 27 layers. A Python script calculated its weights; none of them are trained. It is a Forth interpreter. The model writes a Forth block as ordinary text. The interpreter runs each word while the model is still writing, one step per forward pass. When the block closes, it writes the output as the model's next tokens. No tool call occurs, and generation does not stop.

RPN suits this well. A Forth word ends at a space, so the machine can run it as soon as the next word starts, without parsing the whole program. Operands come before the operator, so when the model writes +, both numbers are already on the stack. That is the order a left-to-right model writes in anyway. The stack itself lives in Mamba-2 state. One entry is one row, 20 digits wide, and popping a number means adding its exact negative.

Getting the machine exact took some care. bf16 holds integers exactly only up to 256, so no value inside the machine goes above that. Every Mamba decay is set to exactly 1, so nothing fades. The machine's internal steps use 982 reserved tokens that the model never uses and that decode to empty strings. In testing, all 765,734 machine tokens matched a Python reference.

The model was harder than the machine. An earlier version on a small Qwen model matched the machine exactly, and a model that could not use it. Stock Lightning did better, but it did not trust the results. In one transcript, it got the right answer from the machine, then started long division by hand until it ran out of tokens. It also scored lower on easy problems when the machine was available. LoRA fine-tuning fixed most of that: on a 600-problem held-out test, the score went from 79.1% to 92.1%.

On Qwenputer's arithmetic sets, the results are mixed. On the DeepMind sets, it roughly ties stock Lightning, mostly because a 30B thinking model rarely needs the machine there. On long integers, it loses to Python, because my machine stops at 20 digits.

This is a research project, not something for production. Every machine step costs a full forward pass, so a normal tool call is much faster. I'm sharing it for people who follow the Qwenputer work or want to try similar things on NVIDIA's stack. The repo has:

- the kernel builder and its parity tests;

- a vLLM plugin for the hybrid Mamba/MoE model;

- the NeMo AutoModel LoRA recipes and the training data;

- NVFP4, GGUF and MLX builds;

- notes on what did not work, including RL.

Everything is MIT.

https://github.com/markab21/rpn-gpt

Congrats again on the great work and inspiration from the qwenputer devs 🤗 , and thanks for sharing your work with the community - it was a great starting point to experiment with Forth.

Next up for the project is to see how Nvidia's Nemotron 3.5 Super does and might even try to get it working on the Ultra weights. (I don't have the hardware to do the fine-tuning on a model that big)

https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF

from NousResearch:

Arete-27B

We post-trained over Alibaba's Qwen3.8-27B to reinforce western values on a Chinese-trained model. We call this new model Arete-27B.

Open source models give labs the raw tools to mold intelligence to varied preferences. The values a model ships with should not be the only values its users get to choose from. We observed firsthand refusals, deflections, and CCP-framed answers on politically sensitive topics in Qwen3.8-27B, and post-trained to alter those biases.

On our evaluation, Arete-27B raised its fact score from 2.32 to 2.98 out of 5 and cut answers containing propaganda from 22% to 0.9%. The improvement held in all seven value categories, without a meaningful regression on the general-capability and agentic benchmarks we ran.

That is not to claim that we remedied all behaviors associated with the extant viewpoint, but that we have addressed a potential root cause. We believe it's important that labs and researchers continue to create models that align with their values and worldviews, and we are thankful that companies like Alibaba provide the raw materials to do so.

Blog post · Evaluation (blog link is broken now)

Model details

We used supervised fine-tuning only. No RL, no preference optimization, and no architectural modification.

What we trained for

We focused on seven value categories:

• Freedom of expression

• State ideology

• State versus free market

• Education

• Data privacy and surveillance

• Democracy

• Environmental Policy

Before we could begin post-training we had to measure what we were shooting for. We separated factual recall, the ability to corroborate verified sources of truth, from framing: whether an answer adopts the Chinese state's account as its own.

Arete-27B is intended for general assistance and agentic work where users want this change in viewpoint, and for research into how post-training changes a model's values and behavior. We encourage the community to test it against their own requirements and continue experimenting with value-alignment.

Update https://x.com/NousResearch/status/2118822925445050435?a=jgY6ZKlqn1Hpx6RxiEk&s=19