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Banaxi-TechΒ 
posted an update 2 days ago
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7490
BananaMind 2 Pro is training!
The current checkpoint (ONLY 20% DONE) GETS #6 On the entire Open SLM Leaderboard.
We are going to release the first public preview on August 2-4 (estimated from speed)

Give us a follow to know when it releases!
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FredyRivera-devΒ 
posted an update 1 day ago
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4538
We wrote a full technical guide on how to train a bilingual (ES/EN) LLM from scratch: TinyQwen.

Covers:
- Hybrid architecture based on Qwen3.5
- Pre-training with 15B tokens
- Cost benchmark between H200 and B200
- Post-training with SFT + LoRA
- Full code and data, open source

With ~$11 of compute on an H200 we ran an initial training run, enough to validate the full architecture and pipeline.

Blog post: https://aquiles-ai.vercel.app/blog/tinyqwen-from-scratch

Technical feedback welcome, especially from anyone looking to replicate the pipeline with more compute.
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julien-cΒ 
posted an update about 20 hours ago
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1232
who's working on an NVFP4 version of Kimi-K3?
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sergiopaniegoΒ 
posted an update 2 days ago
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quick reminder! 🚨

tomorrow (Tuesday, July 28), we're back with Class 3 of the Training Agents live series

🧠 what: reinforcement learning for training agents (GRPO): how it works, how to implement it in TRL, and end-to-end examples
πŸ—“οΈ when: Tuesday, July 28 - πŸ•” 5:00 PM CEST / 8:30 PM IST
πŸ“ where: Live on @huggingface 's X, YouTube, and LinkedIn

live: https://www.youtube.com/watch?v=ztdTed5egrM

class 1: https://x.com/SergioPaniego/status/2069382207618379813
class 2: https://x.com/SergioPaniego/status/2075180665184686187
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NymboΒ 
posted an update 4 days ago
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5255
Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.

CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.

See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2

Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
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owensongΒ 
posted an update 3 days ago
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I just released Inflect v2, pushing complete TTS below 4M parameters.

Inflect-Nano-v2 fits every learned inference component, including waveform generation, into 3.96M parameters (15.97 MB FP32), making it one of the smallest publicly available complete TTS models. Inflect-Micro-v2 stays under 10M at 9.36M parameters (37.53 MB), using the extra capacity for clearer, more natural speech.

This was not about building a toy that technically produces audio. I wanted to see how much useful TTS could actually fit at this scale.

Nano stayed surprisingly close to Micro across the reported release metrics, but if you want to hear Inflect at its best, start with Micro:
owensong/Inflect-Micro-v2

Text goes in and 24 kHz speech comes out without a second learned model. Both models have PyTorch and ONNX releases: PyTorch runs on CPU or CUDA, while the browser demo runs ONNX locally through WebGPU with a WASM fallback. The Inflect code and weights are released under Apache 2.0.

Try Nano and Micro side by side:
owensong/Inflect-v2

Some people may remember the much rougher Inflect-Nano-v1 experiment. V2 was rebuilt and retrained to address its biggest weaknesses: metallic audio, unstable timing, weak prosody, and difficulty with unseen text. Nano-v2 is smaller while sounding substantially cleaner and more stable.

In an anonymous community blind test across nine voices, Micro and Nano placed second and third by normalized preference rate. Full results, samples, limitations, and inference instructions are on the model pages.

Inflect-Nano-v2:
owensong/Inflect-Nano-v2

I built Inflect independently with a limited training budget. If you test it, give it something difficult: unusual names, numbers, abbreviations, symbols, or awkward punctuation. Send me the exact text when something breaks. If you build something with it, I would genuinely love to see it.
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EnderchefΒ 
posted an update about 12 hours ago
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Introducing GCI-Bench, Glint Research's first benchmark!

This 5000-question bench benchmarks attention and gradients; it gives a prompt and unrelated filler parts. The goal is to benchmark if the model understands and attends to the important parts more than the unimportant parts.

I've already benchmarked on the top 5 models on our Leaderboard(Glint2, Supra 50M, Glint1.3, GPT S 5M, GPT X2 125M)
Pending benchmark status at OpenEvals/README#2


Glint-Research/GCI_Bench
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SeaWolf-AIΒ 
posted an update 3 days ago
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πŸ–ΌοΈ POCKET-Image β€” the POCKET series goes visual: character-perfect text in any language, on-device

A new model in VIDRAFT's POCKET family. POCKET put 35B-class models on phones and no-GPU PCs. POCKET-Image carries the same "big capability, small hardware" idea into image generation β€” and fixes the one thing nearly every image model gets wrong: text.

Type "μ•ˆλ…•ν•˜μ„Έμš”" into a typical model and you get "μ•ˆγ…κΈ°." Hangul alone composes 11,172 syllable blocks; Arabic connects its letters; Thai stacks marks. Diffusion models draw scripts as shapes, so they smear. POCKET-Image renders every glyph exactly β€” ν•œκ΅­μ–΄ Β· δΈ­ζ–‡ Β· ζ—₯本θͺž Β· Ψ§Ω„ΨΉΨ±Ψ¨ΩŠΨ© (RTL) Β· ΰΉ„ΰΈ—ΰΈ’ Β· Latin and more β€” onto any scene you describe.

What it is:
β€’ 100% accurate text, any language β€” where global models produce gibberish
β€’ Any background from a prompt β€” text is optional (empty β†’ a pure image)
β€’ No GPU, no NPU β€” runs on plain CPU + RAM via the POCKET-Core engine
β€’ Measured footprint: 8.6 GB (RTX 3050/4060) Β· 4.5 GB (offloaded, 6 GB cards) Β· 13.4 GB (MacBook, 16 GB+)
β€’ Windows Β· macOS Β· Linux Β· fully local, no cloud

Built on the open, commercial-friendly Z-Image (Apache-2.0) foundation.

Honest note: the text is the guaranteed-correct part β€” the surrounding scene is ordinary generation, so a busy foreground can crowd the letters. We say so; clean backgrounds stay razor-sharp.

🎨 Studio β€” generate right here, any language:
FINAL-Bench/POCKET-Image-Studio

🧩 Model card:
FINAL-Bench/POCKET-Image-Zimage

πŸ“š The POCKET collection:
https://huggingface.co/collections/FINAL-Bench/pocket-models
danielhanchenΒ 
posted an update 9 days ago
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Introducing Unsloth for AMD πŸš€
You can now train & run LLMs on your AMD hardware

β€’ We collaborated with AMD to enable you to train & run 500+ models on AMD GPUs
β€’ Works on Windows, WSL, Linux
β€’ Train Qwen, Gemma on just 3GB VRAM

GitHub: https://github.com/unslothai/unsloth
Blog + Guide: https://unsloth.ai/docs/basics/amd
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appvoidΒ 
posted an update 1 day ago
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A Small Model is All You Need. Meet palmer-006 (90M)

After 3 years of experiments, we are finally releasing our flagship tiny model: **palmer-006**.

If you are building for edge hardware, SBCs (Raspberry Pi, etc.), or low-power devices, this is for you. Inspired by Andrej Karpathy's idea of a self-contained "cognitive core," we wanted to see how much power we could pack into a sub-100M parameter footprint.

🧠 **How we "Palmerized" it:**
We believe in starting our experiments with the absolute strongest baseline possible.
1. Light fine-tuning on highly curated data
2. Model merging
3. Another light fine-tuning round
4. Adjusted Mamba for maximum token speed ⚑️

⚠️ *Note: This is a foundational language model. It has not been instruction-tuned yet!*

Also, since this needs instruction tuning next to become a chat assistantβ€”**what dataset would you recommend we use for the instruct tune?**

---
πŸ”— **Quick Links & Info:**

* **License:** Open for research, education, hobby, and modification! (For commercial use/hosted APIs, shoot an email to nosoyhackercodigo@gmail.com. *PS: Donators can claim a free commercial license!*)

* **Attribution:** Built using AI tech from the Technology Innovation Institute (TII).

Can't wait to see what you build at the edge. Let me know your prompt completions below! πŸ‘‡

appvoid/palmer-006
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