r/huggingface

The Qwen3.6 update is here. 35B-A3B Aggressive variant, same MoE size as my 3.5-35B release but on the newer 3.6 base.

Aggressive = no refusals; it has NO personality changes/alterations or any of that, it is the ORIGINAL release of Qwen just completely uncensored

https://huggingface.co/HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive

0/465 refusals. Fully unlocked with zero capability loss.

From my own testing: 0 issues. No looping, no degradation, everything works as expected.

To disable "thinking" you need to edit the jinja template or simply use the kwarg {"enable_thinking": false}

What's included:

- Q8_K_P, Q6_K_P, Q5_K_P, Q4_K_P, Q4_K_M, IQ4_NL, IQ4_XS, Q3_K_P, IQ3_M, Q2_K_P, IQ2_M

- mmproj for vision support

- All quants generated with imatrix

K_P Quants recap (for anyone who missed the 122B release): custom quants that use model-specific analysis to preserve quality where it matters most. Each model gets its own optimized profile. Effectively 1-2 quant levels of quality uplift at ~5-15% larger file size. Fully compatible with llama.cpp, LM Studio, anything that reads GGUF (Ollama can be more difficult to get going).

Quick specs:

- 35B total / ~3B active (MoE — 256 experts, 8 routed per token)

- 262K context

- Multimodal (text + image + video)

- Hybrid attention: linear + softmax (3:1 ratio)

- 40 layers

Some of the sampling params I've been using during testing:

temp=1.0, top_k=20, repeat_penalty=1, presence_penalty=1.5, top_p=0.95, min_p=0

But definitely check the official Qwen recommendations too as they have different settings for thinking vs non-thinking mode :)

Note: Use --jinja flag with llama.cpp. K_P quants may show as "?" in LM Studio's quant column. It's purely cosmetic, model loads and runs fine.

HF's hardware compatibility widget also doesn't recognize K_P so click "View +X variants" or go to Files and versions to see all downloads.

All my models: HuggingFace-HauhauCS

Also new: there's a Discord now as a lot of people have been asking :) Link is in the HF repo, feel free to join for updates, roadmaps, projects, or just to chat.

Hope everyone enjoys the release.

credit to Guilherme34 for the image (looks a bit ai generated to me)

Check here : https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled

Source : https://x.com/lordx64/status/2045911863947309270?s=46&t=3RQrxdWbeu3T78IwKM8AnA

Our newest Terminal-Bench 2.0 submission, powered by Claude Opus 4.7, reached 80.2% accuracy across 89 tasks with 5 attempts per task (445 total trials), and the run has passed validation.

We view this result as meaningful for three reasons:

1. Evaluation depth: the score reflects repeated performance across a broad task set, not a single-pass run.
2. Execution realism: Terminal-Bench tests agents in terminal-based workflows where success depends on tool use, state management, multi-step reasoning, and reliable completion under realistic constraints.
3. Validation rigor: passing validation matters because reproducibility and benchmark integrity are critical when evaluating agent systems.

As the space matures, we believe the most important progress will come from systems that are not only capable, but also consistent and dependable in real operating environments. This result is a strong step in that direction for WOZCODE.

Submission details:
https://huggingface.co/datasets/harborframework/terminal-bench-2-leaderboard/discussions/148

Hey,

I wanted to share a model I trained on a subset of AudioSet + some additions from Pixabay Sounds.

It's a very small CNN that is quite decent at detecting audio alerts and runs well even on microprocessors.

Link with the model and more details on how it was trained: https://huggingface.co/PaulPlayStudio/audio-alert-detector

[deleted]

Hi everyone, Alex, frontend developer here, finally having some time to dip my toes into running ML models in the browser. I'm building a proof of concept segmentation / BG removal app in the browser with onnxruntime-web / transformers.js.

I hope this is the right place to post this. If not, please direct me to the right subreddit :)

I am able to run SAM3 in the browser on WebGPU no problem, and also Bria's RMBG-1.4 (great model!) runs fine. However, RMBG is not MIT licensed, and I wanted to build a fully free stack, so I ended up with BiRefNet.

Unfortunately, I did not get the BiRefNet lite model 1024x1024 to run on either WebGPU (not enough storage buffers) or WASM (Out-of-memory error). So, I managed to figure out how to resize the model to 512x512. I took a lot of trial and error, since BiRefNet uses deform_conv2d, which is not available any more in a modern Python stack. I had to run it through docker (ouch!) to get the right export.

But, with this new export it works in onnxruntime-web, which makes me very happy! It is unfortunately a little low on resolution but it runs reliably on my Macbook Pro M1. I'm curious if this is at all useful to anyone, and if the model card is in a format that is clear and useful. Also, if anyone has any idea on how to get the resolution higher without crashing the onnx runtime, that would be amazing.

Here is the link: https://huggingface.co/studioludens/birefnet-lite-512

Any feedback is more than welcome!

https://www.youtube.com/watch?v=2D_EAcDgPEI

Reachy Mini is a collaboration between Pollen Robotics, Hugging Face, and Seeed Studio. All open source, including the body files. I got a beta developer unit through the Rerun office and have been playing with it for the past few weeks.

A few things I didn't expect going in:

- The multicolor 3D printing for something like text on a curved surface is genuinely tricky to get right

- The app ecosystem is more interesting than I thought. The constraint of no hands and no legs forces creative solutions

- Running a local model vs. connecting to a cloud LLM is a real tradeoff for a home robot, especially if kids are involved

The full code walkthrough (TensorFlow + PyCharm setup) is coming to the PyCharm channel as a companion video.

Been working on this for a bit and figured it was ready to share. KIV (K-Indexed V Materialization) is a middleware layer that replaces the standard KV cache in HuggingFace transformers with a tiered retrieval system. The short version: it keeps recent tokens exact in VRAM, moves old K/V to system RAM, and uses K vectors as a search index to pull back only the ~256 most relevant V entries per decode step.

Results on a 4070 12GB with Gemma 4 E2B (4-bit):

• 1M tokens, 12MB KIV VRAM overhead, ~6.5GB total GPU usage
• 8-10 tok/s at 1M context (GPU time)
• 70/70 needle-in-haystack tests passed across 4K-32K
• Perfect phonebook lookup (unique names) at 58K tokens
• Prefill at 1M takes about 4.3 minutes (one-time cost)
• Decode is near-constant regardless of context length

The core finding that makes this work: K vectors are smooth and structured, which makes them great search indices. V vectors are high-entropy and chaotic, so don't try to compress them, just retrieve them on demand. Use K to decide which V entries deserve to exist in VRAM at any given step.

No model weights are modified. No retraining or distillation. It hooks into the HuggingFace cache interface and registers a custom attention function. The model has no idea it's talking to a tiered memory system. Works with any model that uses DynamicCache. Tested on Gemma 4, Qwen2.5, TinyLlama, and Phi-3.5 across MQA/GQA/MHA.

There are real limitations and I'm upfront about them in the repo. Bounded prefill loses some info for dense similar-looking data. Collision disambiguation doesn't work but that's the 4-bit 2B model struggling, not the cache. Two-hop reasoning fails for the same reason. CPU RAM scales linearly (5.8GB at 1M tokens).

Still actively optimizing decode speed, especially at longer contexts. The current bottleneck is CPU-to-GPU transfer for retrieved tokens, not the model itself. Plenty of room to improve here.

GitHub: https://github.com/Babyhamsta/KIV (can be installed as a local pip package, no official pip package yet)

Happy to answer questions about the architecture or results. Would love to see what happens on bigger models with more VRAM if anyone wants to try it.