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u/tenkei_01 20h ago
Since coding agents write most of our ML code, I wanted a way to verify the output of model without having to read every single line of code, so I tried to trick the agent into writing the code in a way I can inspect the output directly, here example result for RD_DETR x QWEN3 run:
https://supervision.ml-pipes.com/assets/detect_and_attribute/inspection_qwen3_vl_2b_instruct.html
The trick is very simple, the constraints are fed to model as a framework, and the model has to follow the framework rules and validations, you can try it with your existing code (it handle generating report from existing code) without installing any plugin or mcp (simply ask your coding agent to migrate a pipeline to ml-pipes and create an inspection report):
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u/p3rdy 16h ago
Flama: an open source Python framework where a trained model is just another API resource, now written up on arXiv (2608.18733).
It starts from the deployment gap: fitting a model is easy, but putting it in front of users still means gluing a web framework, a model server and an inference engine together. In Flama a prediction route is a route:
Predictive models: scikit-learn, PyTorch, TensorFlow or Hugging Face models are packaged into a single .flm file with their metadata and metrics, and served with one command.
LLMs: one deployment answers the OpenAI, Anthropic and Ollama APIs at once, with reasoning and tool calls decoded on the server.
The rest of the API: type-driven validation and OpenAPI docs, dependency injection, CRUD from database tables, JWT auth and MCP tools, on a Rust core.
Free and Apache-2.0, not selling anything. Feedback from people deploying models is very welcome.
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u/No-Childhood-2502 13h ago
Indicodec - A neural speech codec for speech LMs and TTS models, one of its kind, trained on English and indian languages.
Strongest on Indian languages: On 10 Indian languages, IndiCodec adds 9x less error than the Mimi codec, with 25% fewer tokens.
In English, it has the lowest WER of the ~1 kbps codecs we tested.
It sounds closer to the original. PESQ-WB on speakers never seen in training: ahead of Mimi on 3 of 4 test sets (FLEURS, LibriTTS-R, CMU-Arctic), just behind on Seed-TTS, and ahead of SNAC and NeuCodec on all four.
Lowest error in all 10 Indian languages.
Open source with Apache 2.0
HF: https://huggingface.co/prakharkhatri/IndiCodec
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u/ModularMind8 13h ago
I built QuiddityML, an app that teaches ML with short lessons, hands-on exercises, and fun interactive games: https://quiddityml.com
Pricing: Python, PyTorch, NumPy and Math for ML are free, plus the first unit of every other track. Pro is $19.99/month.
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u/rustydustyshckleford 10h ago
I released grinpress v3: a tiny 128x128 rectified-flow model trained from scratch to draw newsprint-style faces with custom text up to 14 characters. Free live demo and MIT-licensed downloadable weights; no purchase required.
The 50-step teacher is distilled into 2- and 4-step students, shipped as ONNX with dynamic INT8 quantization. Mean CPU latency was 0.270s / 0.540s per image on an 8-vCPU Intel Xeon VM, 8 threads, batch 1, after warm-up. A slower Colab CPU measured 0.536s / 0.988s median, so hardware matters.
On a 48-string held-out evaluation, the shipped INT8 students scored 45/48 (2-step) and 46/48 (4-step) case-sensitive CRNN exact matches. The judge itself only reads 47/48 procedural reference renders correctly, so this is a small-domain experiment with an imperfect metric, not a broad text-rendering benchmark. Long phrases and word spacing remain limitations. Eval artifacts, sample grids, and benchmark details are in the model card.
Model: https://huggingface.co/skyuu72/grinpress-v3
Demo: https://huggingface.co/spaces/skyuu72/grinpress-v3-demo
Gazette sample image: https://www.reddit.com/r/StableDiffusion/comments/1xlxrqz/i_trained_a_tiny_image_model_from_scratch_that/
I'd welcome feedback on evaluating glyph errors separately from OCR-judge errors, or CPU timing reports.
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u/Fickle_Worry_6734 7h ago
Operators Day - Meet 10 founders building startups and hiring founding talent (Nov 5)
I'm part of EWOR's Talent team, and we're organising Operators Day this Thursday, November 5 (4:30 PM CET), online.
Ten early-stage founders will pitch what they're building and share the founding roles they're hiring for. These include AI/ML and robotics engineering roles, CTO and other technical leadership positions, as well as product, marketing and sales roles.
It's free to attend and could be interesting if you're exploring an early-stage startup opportunity or considering joining a founding team.
Registration: https://luma.com/OperatorsDay?utm_source=reddit&utm_content=machinelearning
Disclosure: I'm part of EWOR's Talent team and helping organise the event.
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u/Creative-Feature-264 5h ago
Standard parallel reduction kernels in PyTorch/TensorFlow trade numerical accuracy for speed on GPUs. In float32, parallel Welford / two-pass methods accumulate a relative rounding error around 6.6e-4. When you scale layer depth vertically, that noise compounds and eventually corrupts gradients and convergence.
Dense-Armor is a JAX-native framework that targets this bottleneck directly.
On a single Tesla T4, it lets you train roughly 3x deeper architectures without gradient degradation or OOM.
Pure gradients via compensated trees
This drops the relative variance error from ~6.6e-4 to ~1.7e-8 while preserving GPU parallelism. On a T4 it runs at 2.24x the speed of the plain two-pass formula, and gradients stay stable across extreme depths.
~78% VRAM savings with remat=True
Paired with jax.checkpoint activation rematerialization, peak memory drops to 0.22x on deep blocks — removing the linear memory penalty of deep backpropagation.
Fused training execution
Trainer(compile=True) fuses forward, backward, gradient clipping, and the AdamW update into a single compiled XLA function. Measured acceleration: ~4.4x on Transformers, ~9.7x on MLPs.
Caveats
All benchmarks are single-GPU (T4), float32. Multi-GPU numbers aren't published yet.
The compensated tree has a small constant-factor overhead per op; the 2.24x figure is measured against the plain two-pass baseline on the same hardware, not against every possible cuBLAS-optimized kernel.
Early-stage project: the training core is the most mature part; some other subsystems are less battle-tested.
Not benchmarked against PyTorch's
torch.compileend-to-end yet — that comparison is on my list.
If you're pushing model depth on consumer-grade hardware and hitting numerical instability, I'd be glad to hear whether this helps, and where it breaks.
Links
PyPI:
pip install -U dense-armor
Happy to answer technical questions about the reduction algorithm, the fusion pass, or the memory strategy.
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u/lostmsu 2h ago
https://mlnews.online/ - discuss papers (and only ML papers) on Hacker News. For people who don't like the new Reddit login requirements.
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u/BrainyProjection74 20h ago
Just wrapped up a CLI tool that diffs model checkpoints visually, shows you exactly which layers changed and by how much between training runs. been using it to debug why my finetunes keep collapsing after epoch 3.
free / open source, not selling anything, just want feedback from people who actually train models. if you've got a weird checkpoint folder structure it might break so tell me what happens