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MiniPlus-NanoPlus-Requests

Like 3

Running

CyberTiel

1. Link: peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-GGUF-MTP (https://huggingface.co/peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-GGUF-MTP)
2. Just like Tiel , agentic coding tasks, but stronger, less thinking(in my opinion) and uncensored
3. Miniplus like what you did on Tiel

The MiniPlus V2.1 and NanoPlus quantizations are already underway; they will be available within an hour at most—follow me to get notified.

Wow
Thanks for your hard works and very fast reply, really impressive, this is the fastest model request i've seen on hf (or maybe you had already worked on this model from before :))

I will definitely check and test for if the Apex Miniplus CyberTiel work good too

Request for next model.
Reason: I've personally used k2 and it's the closest thing to match or exceed Qwen intelligence:
IFM/K2-Horizon-MoVA-36B-A4B

Need of Speed) https://huggingface.co/IFM/K2-Horizon-MoVA-36B-A4B

I personally tried the K2-Horizon, the smaller versions and that MoVA, and they didn't convince me because of the cost of the KV Cache.

I could barely get it to 8K context in Q4_0, not enough to properly test it, is it really any good?

For me, the fact that it had such an absurdly expensive KV cache was enough to completely rule it out. Did you guys test it in real-world use?

You were using Q4_0 KV?
How much VRAM on the rig?
I assume your models were tested on 24gb ram?

Title: Request: Handcrafted APEX-I-MiniPlus V2.1 for Sage-A1.5 (35B MoE / 3B active)

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Hi IsValorum,

I'd like to request a handcrafted APEX-I-MiniPlus V2.1 quantization for Sage-A1.5.

• Model card: https://huggingface.co/InternScience/Sage-A1.5
• Paper: https://huggingface.co/papers/2610.28578

Full benchmark context

Sage-A1.5 is a 35B MoE with only ~3B active parameters per token, purpose-built for long-horizon agentic orchestration and verification. Its scores are competitive with vastly larger frontier models on the benchmarks that matter most for quantization sensitivity:

Benchmark	Sage-A1.5 (35B/3B)	Ornith-1.5 (35B-A3B)	Ornith-1.5 (397B)	Kimi-K3 (2.8T/104B)	GLM-5.3 (open weights)
BrowseComp	81.7%	67.6%	86.6%	91.2%	~68.6 (index)
SWE-Bench Pro	61.2%	59.6%	65.1%	63.3%	— (see DeepSWE)
GAIA	95.1%	—	—	78.3%	—

Sage-A1.5's 95.1% on GAIA is particularly notable—it outperforms substantially larger models on this long-horizon agentic benchmark. On SWE-Bench Pro, its 61.2% is within striking distance of Kimi-K3's 63.3% despite being ~35× smaller in total parameters and ~35× smaller in active parameters. On BrowseComp, its 81.7% surpasses the 35B-A3B Ornith-1.5 variant (67.6%) by a wide margin, though it trails the 397B Ornith flagship (86.6%) and the 2.8T Kimi-K3 (91.2%).

Ornith-1.5-35B-A3B is the closest architectural peer—same 35B total / 3B active MoE footprint. Sage-A1.5 leads it on BrowseComp (81.7 vs. 67.6) and SWE-Bench Pro (61.2 vs. 59.6), which makes Sage-A1.5 the stronger 3B-active agentic model in this class. That is exactly why a handcrafted quantization matters: there is no slack to absorb router drift or attention degradation.

Why the handcrafted APEX-I treatment is the right fit

Community GGUF quantizations for Sage-A1.5 already circulate, but there is no handcrafted APEX-I option. The MiniPlus V2.1 recipe has a demonstrated track record on comparable MoE architectures: prior releases have held ΔPPL ≈ +0.05% to +1.08% while preserving the three failure points where generic quantizations break MoE reasoning:

• Routers gate_inp / gate_shexp: uncompressed F32 — zero routing drift
• Output head: Q6_K
• Attention gates: Q8_0
• Core experts: IQ3_XXS / IQ3_S or above

Sage-A1.5's agentic orchestration and verification protocol depend directly on stable routing and attention gates. A quantization that clips the 61.2 SWE-Bench Pro or 81.7 BrowseComp margins defeats the purpose of running this model instead of a smaller generalist.

Suggested starting preset

APEX-I-MiniPlus V2.1, following the standard armor above. If the 35B/3B footprint allows a tighter envelope without crossing the quality floor, an APEX-I-MiniPlus V2 variant would also be valuable for users prioritizing theoretical edge fidelity.

Use case: local multi-step coding, tool orchestration, and scientific research-exploration workflows—exactly where Sage-A1.5's agentic margins over similarly-sized peers are the whole point.

Thanks for considering this request.

The MiniPlus and NanoPlus V2.1 versions are now ready and uploaded along with the 1 Million token I-Matrix that it generates.

https://huggingface.co/IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF

https://huggingface.co/IsValorum/Sage-A1.5-APEX-I-NanoPlus-V2.1-GGUF

https://huggingface.co/IsValorum/Sage-A1.5-VAL-1M-I-Matrix-GGUF

Hi IsValorum.
Love your work as always!

So I stumbled on something I found quite interesting.
For those of us with budget consumer PCs, running Qwen3.8-Flash-Next (125B A6B MoE) locally is out of reach.
However, I stumbled upon this project which claims it can be used to run Qwen3.8-Flash-Next on 16 GB of VRAM and 32 GB of RAM.
I haven't tested whether it actually works or the tok/s is acceptable, but just wondering if this is something you could work with and whether you could use any of your signature techniques on it.
I will test it myself in the coming days once I get on top of some university work.

I'm familiar with Strata Engine; it allows Qwen3.8 Flash Next to be used on graphics cards with up to 12GB of VRAM. In that specific case, the GSQ-RCO cuats (IQ3_S, IQ2_XXS etc) are really good, unlike the GSQ-RCO cards on the 27B.

You have many models so which one is the model that you use the most

Hi IsValorum.
Love your work as always!

So I stumbled on something I found quite interesting.
For those of us with budget consumer PCs, running Qwen3.8-Flash-Next (125B A6B MoE) locally is out of reach.
However, I stumbled upon this project which claims it can be used to run Qwen3.8-Flash-Next on 16 GB of VRAM and 32 GB of RAM.
I haven't tested whether it actually works or the tok/s is acceptable, but just wondering if this is something you could work with and whether you could use any of your signature techniques on it.
I will test it myself in the coming days once I get on top of some university work.

I'm familiar with Strata Engine; it allows Qwen3.8 Flash Next to be used on graphics cards with up to 12GB of VRAM. In that specific case, the GSQ-RCO cuats (IQ3_S, IQ2_XXS etc) are really good, unlike the GSQ-RCO cards on the 27B.

Would it be possible for you to do your VAL-APEX or APEX-I thing Qwen3.8-Flash-Next such that it is improved in some way when run on Strata Engine? If so, would there be any value in doing so?
Excuse my ignorance, I got no idea what those are. All I know is they give me awesome results on my budget hardware.

Hi IsValorum,

Thanks for the Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF release — really appreciate you putting it together so quickly.

I’ve tested it on llama.cpp（b11669）. The test was run through the DeepSeek harness, using the “pelican riding a bicycle” prompt.

Test setup:

• GPU: RTX 4060 (8 GB), VRAM usage ~7.2 GB
• RAM: DDR4 32 GB
• OS: Windows 10
• Context: 131,072
• MTP: not enabled
• Test harness: DeepSeek harness
• Prompt: pelican riding a bicycle

Key launch args:

ngl=999
ncmoe=40
c=131072
ctk=q8_0
ctv=q8_0
kv-unified=true
fa=on
b=2048
ub=2048
moe-cache-mib=3072
spec-type=ngram-map-k4v,ngram-mod
spec-draft-n-max=2
spec-ngram-mod-n-match=24
spec-ngram-mod-n-min=48
spec-ngram-mod-n-max=64
temp=0.65
top-p=0.95
top-k=20
min-p=0.05
repeat-penalty=1.05
reasoning=on
reasoning-effort=xhigh
reasoning-budget=32768

Observed speeds from the log:

Prompt eval was around 69 t/s on the small prompt, and instantaneous tg stayed roughly 28–34 t/s, with short 3s peaks up to ~41 t/s. The long run finished at 34.15 t/s over 3,047 generated tokens.

So on a much smaller 8 GB card it’s still quite usable, and VRAM stayed around 7.2 GB with 128K context and ncmoe=40. The pelican-on-bicycle prompt ran through fine in the DeepSeek harness as a quick sanity check.

Thanks again!

If you want even lower PCIe memory bus pressure and slightly faster inference throughput on an 8 GB VRAM setup, feel free to try the Sage-A1.5-APEX-I-NanoPlus-V2.1-GGUF variant. It provides a reduced memory footprint (12.14 GB vs 14.18 GB) while preserving virtually identical reasoning fidelity.

Surprised no Gemma4 26B in the MoE section... Any reasons for that? For language task it's still king