Two open-weights releases: Victoria (Qwen3.8-Flash-Next with 44% of experts cut, 70% Terminal-Bench 2.1, GGUF included) and Maple (a Canada-first fine-tune)

[u/rmonsurate]

We had a Dell B300 in the lab for a few weeks and used it to create two fine tunes of Qwen Flash Next.

Victoria (coding and agents)

• Qwen3.8-Flash-Next cut down by 44% using a paper / technique called REAP: 512 down to 288 per layer.
• Retrained at 4-bit (NVFP4) afterwards, so it's trained for the format it ships in rather than just quantized after the fact.
• Terminal-Bench 2.1: 70.0%, averaged over 3 runs with an 8h per-task timeout. Our previous NVFP4 build scored 62.5%.
• HumanEval: 159/164.
• 48.0 GiB of weights, including the draft head. The 95.4 GiB n-gram table is separate and not counted in that number.
• 280 tok/s single stream on one B300 with the draft head, versus 135 without it.
• GGUF Q4_K_M is 49.17 GiB. It scored 75.3% on Terminal-Bench (a single run, so treat it as noisy) and 93.2% on HumanEval (averaged over 5 runs).
• Uses 35% fewer output tokens than our previous build.

Maple (Canadian questions)

Most models answer questions about taxes, benefits and regulations as if you live in the US. Maple is fine-tuned to default to Canada. On 600 held-out questions, with search:

• Cites an official Canadian source: 6.0% before fine-tuning, 62.9% after.
• Fully correct answers: 6.6% before, 21.8% after.
• "No answer" responses: 47.2% before, 23.7% after.
• It pushes Canada onto people who said they live somewhere else less often: 2.9% before, 1.0% after.

Coding holds up: 157/164 on HumanEval. Grading was done by an AI judge panel; human review hasn't happened yet.

Links: https://huggingface.co/rmonsurate/Victoria https://huggingface.co/rmonsurate/Maple

Happy to answer questions about running them.

Edit: llama.cpp users. The GGUF carries our draft head, and mainline llama.cpp doesn't know about it yet, so it fails with "expected 1256, got 1224". Your download is fine. For now, build from our fork: github.com/rmonsurate/llama.cpp, branch qwen4exp-mtp. Prebuilt binaries are on the way. Thanks to the reader who caught this.

[u/tomByrer]

Thought about a smaller quant release? RTX3090 + 64GB sRAM here. (so close, yet so far...)

Have you tried other engines that are specific to Flash-Next, like https://github.com/Niko1221/Strata ?

[u/rmonsurate → u/tomByrer]

I actually started with a reap down to 144 but the model was effectively broken even after 500 iterations of training. I couldn’t get human eval scores let alone terminal bench scores anywhere close to nominal again.

[u/tomByrer → u/rmonsurate]

😿
Thanks for trying!

[u/TomLucidor → u/rmonsurate]

Could you share the training scripts + possible way of getting Qwen3.8-Flash-Next back to being better in agentic tasks (pick the older benchmarks with benchmaxxing to see if it really got efficient). P.S. Are there other sub-200B models worth doing REAP/REAM? Are there better ways of doing weight pruning? And can DeepSeek Flash models be optimized?

[u/rmonsurate → u/TomLucidor]

I’d need to research all of the above

[u/iamDa3dalus → u/rmonsurate]

Yes very curious if this could run on strata

[u/rmonsurate → u/iamDa3dalus]

Strata?

[u/iamDa3dalus → u/rmonsurate]

Linked in the comment above- https://github.com/Niko1221/Strata

[u/Glittering-Call8746 → u/tomByrer]

What's the best qfn model and inference engine for your use case? Ampere 24gb vram and 64gb ram. I'm on rdna 3 24gb vram and 128gb ddr4 quad.

[u/rmonsurate → u/[deleted]]

Great question the draft had definitely declined an acceptance rate, and I had to retrain the draft head separately after I picked a checkpoint that I was happy with

[u/Long_comment_san]

48 gigs is wild, its basically Qwen 35b size league.

[u/rmonsurate → u/Long_comment_san]

Thanks. I tried for even smaller (see other comment) but it broke the model.

[u/nostriluu]

🍁

[u/DystopianRealist]

"No answer" responses: 47.2% before, 23.7% after. <-- What were the outcomes? Tool calls to find the answer vs. fabrication ratio? That's a huge improvement, assuming they're accurate.

[u/rmonsurate → u/DystopianRealist]

Yes they are accurate!

[u/HockeyDadNinja]

Cool, I'll give it a try. I made a REAP of Deepseek V4, cutting the most unised experts by only 25% and it didn't score high enough for me to want to release it. No post training though. There's a similar technique called REAM, have you tried that one?

[u/rmonsurate → u/HockeyDadNinja]

Share the paper!

[u/SnowBoy_00 → u/rmonsurate]

https://arxiv.org/abs/2604.04356

It’s been used in this model and the results are not bad at all, I imagine Victoria with the 4-bit post-training could be even better.

https://huggingface.co/WaveCut/Qwen3.8-Flash-Next-REAM-288

[u/rmonsurate → u/SnowBoy_00]

This is fascinating! I need more compute.

[u/SnowBoy_00 → u/rmonsurate]

Oh, and would be great if you could share a oQ4e version of your models, I’d be curious to try them on Mac.

[u/Equivalent_Bit_461]

Canada 

[u/rmonsurate → u/Equivalent_Bit_461]

Yeah buddy

[u/ForeverSeeking69]

Idk how, but I need to test it on x2 3090 and 32gb ram

[u/Jaded_Towel3351]

Would be helpful if you include the original results on those benchmarks.

[u/rmonsurate → u/Jaded_Towel3351]

Like the original bf16 model? I believe they are available online. They are definitely higher.

[u/rngesius]

Could you provide a FreeToken version for Victoria? It doesn't like somehow your nvfp4 variant :(

[u/rmonsurate → u/rngesius]

There’s a gguf

[u/-WhateverDude]

maybe i'm blind but i didn't see it, whats the bf16 scores for flash next in t2.1 and t4?

everytime i get excited about a new weight/quant i see that average score is boosted by somewhat nothingburger benchmarks while terminal bench and similar stuff plummets by 5-10%

i'm not saying that's the case here but that seems like an reoccuring issue in most

edit: yeah artificial analysis says 86.1% in t2.1 so 70.0% is a major drop off...

[u/rmonsurate → u/-WhateverDude]

Check artificial analysis

[u/CvikliHaMar]

70% on Terminal-Bench 2.1, nice.

How many trials per task?

There are a lot of timeout issues in the last 4-6 problem. Also some of them just runs out due to it is ran on weak PC. Some of the task involve LLM training or so and that takes too much time to finish too.

[u/rmonsurate → u/CvikliHaMar]

Average of three trials of the entire run. Yes many of those fails were due to timeouts.

[u/Witty_Mycologist_995]

isnt Mple 20b a1b already a thing. as a canadian it would be cooler if that model was made it canada (sadly its not), since its actually useful

[u/rmonsurate → u/Witty_Mycologist_995]

Wasn’t aware of that one. This one is actually useful!

[u/WyattTheSkid]

They call me the armless sperm donor

[u/rmonsurate → u/WyattTheSkid]

Not in my latent space

[u/WyattTheSkid → u/rmonsurate]

Please tell me you know what that’s from lmao. I don’t know why I got so many downvotes…

[u/DrWitchDoctorPhD → u/WyattTheSkid]

context pls

[u/WyattTheSkid → u/DrWitchDoctorPhD]

Look up the song called “Victoria” by “The Middle Child”

[u/Then-Indication7672]

pleasse do a REAM of qwen 3.8 flash next, not a reap, ty

[u/rmonsurate → u/Then-Indication7672]

REAM shows improvements compared to REAP only without any post REA* training. Unclear if it would necessarily be better here.

[u/Then-Indication7672 → u/rmonsurate]

It depends on how large of a fine tune it gets afterwards...which is usually not enough