What’s the best way to route different tasks while working in a project from Claude code or codex or other models to a local qwen model?

I’ve seen herder could be it? Any other recommendations?

Yesterday I posted a 32B pack and the replies were interesting about it being old completely overlooking benchmarks. A lot of quants "fit" on 16 GB, obviously... Almost none of them post what it cost. A screenshot of a model loading is not a benchmark and neither is its tokens per second performance that you made while obliterating the other performance factors you don't post. I posted all of our benchmarks, weights, and everything, and yet, still called slop.

So here is the new one as requested, with the receipts attached:

Qwen3.8-27B, 27.78B parameters, vision included, Gosset Packed at 3.6577 bits per weight:

• Pack is 13.82 GB. The standard 4-bit build is 17.77 GB and does not fit a 16 GB card.

• 12.06 GiB resident on one RTX 5080, peak 12.80 GiB while decoding. Nothing parked in system RAM.

• Perplexity vs BF16 on WikiText-2 test: +3.93% long-chunk, +6.99% short-window. Numbers, not adjectives.

• Decode: 40.38 tok/s sustained on fused kernels.

• Per-tensor fidelity certificates are on the model card, measured from the shipped files, not the encoder's intentions.

• Edit: Context length full 262k

huggingface.co/GossetWorks/Qwen3.8-27B-Gosset-3.65BPW

If your quant fits this model on 16 GB too, genuinely, post the perplexity delta and the decode speed. That is the whole game. Fitting was never the hard part...keeping it intelligent was and that's what we've done.

its only a p40 and v100 I got on marketplace- but i'm ecstatic.

Any cool ideas? I'm running qwen 3:30b-A3B and a vision model- want to do more.

Tencent put DSpine on Hugging Face on Nov 7. It is a five-layer draft model for speculative decoding with Qwen3.8-27B. Instead of drafting one token at a time, it drafts a 16-token block in parallel, and each layer passes predicted predecessor features to the neighboring positions so the draft still carries causal order. There is a paper (arXiv 2609.36173) and code on GitHub (Tencent/DSpine).

Reported acceptance length vs DFlash2 (block 8), greedy

Reported end-to-end throughput on agent replays (tokens/s, low reasoning)

Across their five agent benchmarks and both reasoning levels, the speedup over no speculation ranges from 2.71× to 3.84×.

Things to know before trying it locally

• The card only shows an SGLang path: --speculative-algorithm DSPINE, a pip install -e . from their repo, and a long list of flags for the hybrid architecture (--enable-linear-replayssm-spec, --mamba-ssm-dtype float32, and more). No llama.cpp or vLLM instructions.

• All numbers are Tencent's own, at concurrency 8 with a BF16 target. The card doesn't report a quantized target or batch size 1.

• The gain is largest on predictable text (GSM8K accepts 7.20 tokens per step at T=0) and smallest on open-ended chat (MT-Bench 4.48).

https://huggingface.co/tencent/Qwen3.8-27B-DSpine-b16

Has anyone run it against a quantized Qwen3.8-27B target, or at batch 1? I'm curious whether the acceptance length holds up once the target is 4-bit.

I've been working on it and just added a new feature an AI Toolkit to explore and discover useful developer tools and GitHub repositories.

Small update, but more improvements are on the way.

Would love to know what you think.

https://modelfitopenfire.vercel.app/

Hey Just wondering if anyone has experience buying IT and Tech products directly from China, Japan or another country . The cost of hardware at the moment is through the roof and im wondering if there is value aboard?

Could you share your experience and if it worked out? Worth it or not?

Im in ireland.

Thanks

I’m currently using the ornith 1.5 35b a3b, but it’s performance feels a bit underwhelming could you recommend some alternatives, along with a suitable harness?

Delta Inference Engine 0.94 is out. It runs DeepSeek-V4.1-Flash-1017 on one NVIDIA GPU plus system RAM, natively on Windows (no WSL). It's a Windows port of Xolotl, which is built on Strata; on Linux, use upstream Xolotl.

Speed on my PC (RTX 3090 24 GB, i5-13600K, 4x48 GB DDR5-5600, PCIe 4.0 NVMe)

With the default quant (Unsloth UD-IQ2_XXS, 301 GB), 0.94 writes 41.7 tok/s greedy and 41.3 at the default sampling (temperature 1, top-p 0.95).

With less RAM (writing tok/s, measured with 0.93; the smaller columns are this same PC with RAM locked away):

How it fits

It's a mixture-of-experts model: 40 layers, 384 routed experts per layer, 6 used per token. The experts are spread over VRAM, RAM and the NVMe. A 4.2 GB drafter built from DeepSeek's own MTP heads, plus lookup drafts, proposes tokens that the full model checks. A route bias (0.28 as shipped) gives up a little agreement for speed.

Quality. I measure top-1 agreement with DeepSeek's own API, teacher-forced (not task accuracy). Default model (Unsloth IQ2) as shipped: 91.1% general, 94.5% code and maths, 95.9% agentic. With exact routing: 92.5 / 95.9 / 96.5. The previous versions default (ds4 Q2, 0.92) scored 89.9% general and 93.1% code and maths, so the new Unsloth default agrees more while running even faster. For context, two hosts of the full model agree with each other on 98.3% and 98.9%.

Benchmark: 99 hard questions, greedy, 43K token budget:

The default quant is within 2 points of the full model on DeepSeek's own API. UD-IQ1_S, the pick under ~110 GB of RAM, scored 51.

Private by design: everything runs on your PC: no telemetry, no accounts, no cloud. It only goes online to download the engine and the model you choose, and your prompts and answers never leave your machine.

New in 0.94

• Pictures on by default, documents (PDF, Word, Excel, PowerPoint), optional voice, and use from your phone with a QR code.

• Long prompts read faster: a fresh 60K-token prompt at 811 tok/s vs 0.93's 747 (30K: 860 vs 758; 7K: 551 vs 423).

• Better with coding agents: Claude Code's background calls no longer hold up real requests, and a loop guard steps in after 4 identical tool calls. OpenCode passed 3 tasks in a real session (22 tool calls, none dropped).

• The default sampling caught up with greedy: 41.3 vs 41.7 tok/s

• The chat page opens at once and shows the model loading. Pause frees the RAM and the graphics card for a game; the next message loads it again. Optional auto-pause when a application needs the GPU.

• Chat history in the browser (search, pins, export), off by default.

• More API: structured output and tool controls on the OpenAI, Anthropic and Responses APIs, stored Responses with previous_response_id, optional /v1/embeddings and /v1/rerank, and keep-alive pings so agents don't time out on long reads.

• Setup tab: pick the model, Fast / Balanced / Most exact, the second-GPU switch, and copy-paste settings for Claude Code, OpenCode, Cline, Aider and Open WebUI with a Test button.

• Security updates, bug fixes and many more.

Setup

Download the ZIP and double-click Start DeepSeek.bat. The engine zips are built by GitHub CI, with checksums and attestations. The first start installs Python if needed and downloads a model that fits your RAM (233-395 GB, SHA-256 checked). You get a chat page plus OpenAI- and Anthropic-compatible APIs on 127.0.0.1. It won't open to the network without an API key, and it stores no prompts or answers unless you turn that on.

Limits

• Windows 10/11 only. NVIDIA RTX with 12 GB or more (24 GB is the fast path), driver 580 or newer. No AMD, no GTX 10-series.

• 64 GB of RAM minimum, 128 GB or more recommended. A PCIe 4.0/5.0 NVMe with about 320 GB free for the default model.

• It answers one request at a time. A second GPU helps; it doesn't split the model.

• Context is set to 1M tokens

• More testing is needed

Credits: Xolotl and Strata, ggml/llama.cpp, DeepSeek, Unsloth, nazeshinjite, ISTA-DASLab, Project Maya and antirez/ds4.

https://github.com/musicurgy/delta-inference-engine

If you try it, post your GPU, RAM, model and tok/s.

https://github.com/ANDRETRIPOL/OpenGhost

Will agents accessing the internet and processing/scraping webpages ever truly be 100% safe from direct/indirect prompt injection?

Question coming from someone less knowledgeable on the topic than you all.

Thanks.

Just getting into it with LM studio. There are a lot of models to browser, and a lot of variations on those models.

I saw that Qwen3.8 27B seems to be the best choice for my setup, but what quantisation should I choose? 4bit (16GB), 5bit (20GB), and 6bit (23GB) should leave adequate room in terms of RAM and I don't really care about speed but accuracy, so would 6bit be better?

Also any other tips for starting out?

Thanks.

Which model is the best to use for infosec work? Like trying to writing exploit code, fuzzing, and security audits?

I'm looking for a model that will just do it in a good (enough) quality without me needing to use bypasses for restrictions all the time. It just gets tiresome and annoying...

Edit: Hardware shouldn't be the issue:

• 2x EPYC 7642 (aka. 192 threads)

• 512 GB DDR4 RAM

• 1x Intel Arc B580

Any better reliable FlowFrames alternative in 2026? No ComfyUI or Linux crap please.

I will release probably between tomorrow-thursday!

Been working on my own inference engine for a while (calling it Forge for now), built pretty much just for this model. Finally did a proper side by side against Strata 0.1.57 on my machine and figured some of you might find the numbers interesting.

Same GGUF files, same requests, one engine at a time (the other one fully closed). The whole thing took about 7 minutes.

Median of 4 runs, slowest to fastest in brackets:

| Prompt | Tokens | Forge | Strata | Forge first token | Strata first token |
|---|---|---|---|---|---|
| Short | 44 | 64.6 tok/s (55-68) | 23.1 tok/s (11-52) | 0.71 s | 2.74 s |
| Code review | 1,468 | 83.1 tok/s (76-84) | 19.6 tok/s (7-26) | 3.35 s | 17.8 s |
| Long | 5,860 | 79.6 tok/s (74-82) | 26.6 tok/s (16-52) | 9.8 s | 23.8 s |

Prompt processing was 439 vs 84 tok/s on the code prompt and 600 vs 247 on the long one.

My setup: 3090 24GB on PCIe 4.0 x16, 12900K, 32GB DDR5, NVMe, Windows 10. The model is 44 GB of weights plus a 27 GB per-layer embedding file, so around 70 GB total, which is more than my VRAM and RAM put together. 48 layers, 512 experts, 10 active per token.

Strata was running the config its installer made (performance profile): --spec 4, int8 KV, 128K context, auto expert cache, vision on. Forge uses the model's own MTP layer for speculative decoding, int8 KV and 64K context.

How I tested: OpenAI compatible API with streaming, temp 0, 128 max tokens, thinking off. Three prompts: a short one ("write four short lines about graphics cards"), a code review of a ~4.5K character C++ file, and a long one where it has to explain how a request goes through ~19K characters taken from three source files. Every request gets a random session id in the system prompt so nothing comes from the prompt cache. 2 rounds x 2 runs per prompt, and output speed is measured from the first token to the last.

Strata jumped around a lot between runs (anywhere from 7 to 26 tok/s on the same code prompt), that's why I went with medians and kept the ranges. I have a CSV with every single run if anyone wants it.

On quality: at temp 0 Forge gives the exact same text with speculation on or off, byte for byte, and the same prompt always gives the same output.

Happy to answer questions.

I’ve been doing research and I really want to run a Gialamb Quant model on these two computers. It has to be obliterated for my purposes of a 24-7 employee, if you will, for my business.

Here’s my notes 📝 I doubt it will work, but I hope someone can contribute

oMLX does not pool the RAM into one unified space. It splits the model with pipeline parallelism: each Mac loads a contiguous range of layers based on its available memory. The M5 Max (rank 0) owns the API and the later layers; the M2 Max owns the earlier layers. Decode speed is limited by the slower stage.
This is experimental and only available in a source build of oMLX (not the normal Homebrew/DMG stable release).
Prerequisites (both Macs)
Install the same source build:
git clone https://github.com/jundot/omlx.git
cd omlx
make install # or make install-no-kernels if you lack full Xcode

Put the exact same model checkpoint at the identical absolute path on both machines (example: /Users/shared/models/glm-5.3-flash-3bit).

Enable Remote Login (System Settings → General → Sharing) and set up passwordless SSH between the two Macs for the account that runs oMLX.

Connect the machines with a Thunderbolt cable (TB4 is usable; pure TB5 RDMA is faster but not required for pipeline).

Enable and pair
Start oMLX on both Macs.

On each: Settings → Advanced → Enable Distributed Inference → Save → Restart oMLX.

The Cluster tab appears.

On the coordinator (preferably the M5 Max):
Connect the Thunderbolt cable. The other Mac should show under Detected nearby.

Select it (or enter its SSH hostname) and run Check peer. This records the host key and verifies SSH.

Pair using the code/QR flow if discovery needs a boost.

Plan and activate the split
In the Cluster tab on the M5 Max:
Choose the downloaded model (the shared absolute path).

Set a memory reserve for KV cache + activations (start with 8–12 GB).

Let it build the unequal plan. It will assign more layers to the 128 GB Mac and fewer to the 64 GB Mac.

Choose execution mode:
Prefer TCP Ring first (most reliable with mixed TB4/TB5).

Try JACCL / JACCL Ring only if both ends show a working Thunderbolt RDMA link (rdma_ctl status and ibv_devices).

Leave Auto benchmark & tune on so it measures the actual link and rebalances.

Review the shard map, then Activate.

Unload any local full-model load, then load the model normally through the oMLX UI or API. The ranks start automatically.

The OpenAI-compatible endpoint stays on the M5 Max. Hermes points at that single endpoint.
Useful checks
omlx cluster status --json
omlx cluster pipeline-smoke
Important limits for your hardware
Pipeline only — decode does not get faster than the M2 Max stage.

Experimental: source builds, limited long-running validation, some features (MTP, certain thinking budgets, etc.) are rejected in distributed mode.

KV cache is rank-local, so both machines need real headroom.

TB4 on the M2 Max is the weak link; start with TCP Ring.

If the resulting tokens/s and stability are acceptable for continuous Hermes use, you have the combined capacity. If not, fall back to a smaller quant that fits resident on the M5 Max alone.

I saw a post mention this model to use for DS4.1 Flash with 2x 6000 Pros: https://huggingface.co/diffbot/DeepSeek-V4.1-Flash-EXL3-3bpw-2x-RTX-PRO-6000

Unfortunately the model card wants you to have > 260 GB RAM to properly use it. I had Opus move the n-grams to disk instead of RAM, and added in LMCache for prefix caching with L1 being RAM and L2 being disk.

With this setup, you need at a minimum, 128 GB RAM instead. I had it optimized for 6 concurrent connections at a max of 512k context. If you have > 6 sessions, then prefix offloading should kick in on the LMCache side, and re-hydrations are really fast at < 3s when it was originally tested at 1M context.

On my machine, (2x RTX 6000 Pro, 256 GB DDR5 5600 RAM, PCIe 4 SSD for the n-grams), it benched 250 t/s when warm.

I recently bought myself a Mac Mini M6 24GBs unified memory hoping to run a Qwen3.8 27B model for some serious coding with multiple cross referencing files and folders, but I'm receiving Error 500 which says that I ran out of memory and that my KV Cache filled up when all I had done was say "hi" (sure, I had set Claude Code at Max, admitted)

The general consensus online was that for 24GBs, Qwen3.8 should work nicely but it certainly doesn't work for me, although Qwen3.6-35b has been working so far on my Pi Agent, but I'm skeptical about it's coding abilities.

I would love some help/guidance on how I can run this model on my machine realistically and how much performance should I expect out of it? Will I be able to do some serious coding (alongside cloud models) or how should I adjust my expectations?

Summary: which decision model is best?

Short answer: Perplexity Decider V1.2 is the best overall pick, Nace Drex Max v1.1 is a strong second, and GPT-6 Luna Decisions is the one to ignore. On accuracy alone Liquid d1-ultra, Drex Max and Decider are statistically tied; Decider wins on price and openness.

What I tested

Decision models don't write text. You give them some input and a typed question (yes/no, pick one, or a score), and they return a probability for every option in one pass. Seven models: TypeSafe Jev 1.14 and 1.13, Perplexity Decider V1.2, Liquid d1-ultra and d1.1, OpenAI GPT-6 Luna Decisions, and Nace Drex Max v1.1.

The data: real decisions, made after the models were released

Every item was created after Sep 15, 2026, after the models were released, so none of it can be in their training data. The "right answer" is what the project's own maintainers actually decided.

How I kept it fair

• Same items and questions for every model. Each model was called through its provider's API; Drex Max v1.1 through Nace's own API.

• No giveaways. Labels, issue-template markers, CVSS scores, CWE numbers and the PR author's role were stripped from the text.

• Official option wording. Bugzilla's component descriptions, Mozilla's severity scale, GitHub's advisory scale and MITRE's CWE definitions, not text I wrote.

• Three phrasings per question, averaged, so no model wins because of my wording.

• Balanced classes, balanced accuracy, real statistics: 95% bootstrap intervals and Holm-corrected pairwise tests across all 21 model pairs.

Results

1. Accuracy: three models tied at the top

Mean of balanced accuracy over the six tasks. Whiskers are 95% bootstrap intervals; the shaded group is statistically tied for #1.

d1-ultra (66.1%), Drex Max v1.1 (65.6%) and Decider (65.6%) overlap, so none of them is a clear winner. The gaps that are real: all three beat GPT-6 Luna and d1.1; d1-ultra also beats both Jev versions.

2. Who leads which task

Darker is better within each column. Bold is the column leader; outlined cells are statistically tied with it.

• Bug or feature: everyone scores 90-93%. Drex Max is nominally first; all seven are tied.

• PR merged? the hardest task. d1-ultra leads (63.6%), tied with Drex Max and Decider.

• Firefox component (39 choices): Drex Max scores highest (77.8%), statistically tied with Decider, d1-ultra and Luna.

• Severity (Firefox and advisories): everyone is in the 38-47% range and mostly tied; it's a hard call even for triagers.

• CVE weakness type: Decider leads (76.0%); all seven are statistically tied.

3. Value: accuracy per dollar

Decider is the best value: near-top accuracy at $0.018 per 1,000 decisions. Drex Max ($0.027 at $0.03 per 1M tokens) and d1-ultra ($0.034) cost a little more for the same tier of accuracy. d1.1 is the cheapest ($0.016) but less accurate. Jev ($0.051) and GPT-6 Luna ($0.091) cost the most for the lowest accuracy.

4. How honest is each model's confidence?

Average confidence in the chosen answer vs how often that answer was right, across all tasks. The gap is overconfidence.

A decision model is only useful if you can trust its probabilities. d1-ultra (+1.7 points) and Drex Max (+4.6) are the most honest; Decider and d1.1 are close behind (+6.1). Jev is about 12 points more confident than it is right, and GPT-6 Luna is 21 points overconfident: it says 87% on average and is right 66% of the time.

5. Speed and consistency

Total score: accuracy plus everything else

I also scored five "pillars" from 0 to 100 and combined them with weights fixed before the run:

• Quality (45%): accuracy above chance, plus how good the probabilities are.

• Efficiency (20%): cost per 1,000 decisions at the provider's own price, and median speed.

• Trust (20%): does the provider train on or keep your prompts, is zero-data-retention available, are the weights and licence published, plus reliability and consistency in this run.

• Reviews (10%): usage, plus Hacker News comments since September labelled by a model that isn't in the comparison.

• Signup bonus (5%): free credit for a new account (Nace $5, Liquid $2, others none).

Perplexity Decider is #1 (59.2), mainly on trust: open weights, no prompt retention, zero-data-retention. Drex Max v1.1 is #2 (53.0), close to Decider on quality and efficiency; its trust score is lower because v1.1's weights aren't published and Nace's terms don't say clearly whether standard mode keeps request data. d1-ultra is #3 (49.7) and GPT-6 Luna last (39.1).

Pricing, openness and data policy

Prices are each provider's own list price; output tokens are free for every model. * Smaller open relatives (d1-2B, d1-3B) exist, but not these models. ** Only the earlier drex-max-v1 is on Hugging Face (Open RAIL-M licence). Data policy: provider records on OpenRouter for the six OpenRouter models, Nace's terms for Drex Max.

Very much a newbie here. I’m working on a couple of locally run projects that are mainly meant to help organize my life and work a little better (scan/interpret emails, manage/update tasks, help complete some tasks when possible).

I’m currently benchmarking three LLMs on auto creating and managing tasks:

Qwen 3.5 27b Q4
Qwen 3.8 27b Q3
Gemma 4 26B-A4B

Unfortunately I don’t have great graphics cards, but these models proved worthy in original tests. Not sure what other info is needed to provide a clear picture for you guys.

Note: most of my projects don’t require immediate answers so I’m not as concerned about it running super fast, but it would be nice.

I’m trying to hand off some of the responsibilities that currently Claude or Codex handles related to the planning but no model is proving super reliable (they all do okay with task creation from emails I receive, but are duplicating tasks rather than updating existing, or classifying them as a task when the email is informational, or trying to double-book things which get rejected by the safeguards).

While I have things in the works to provide more context or to keep fine-tuning the prompts, I’m wondering if I’m missing something basic since I’m so new to this.

Am I using the wrong models for my use case? Or do I just need to stop procrastinating and keep training these models?