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appvoid

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https://ko-fi.com/appvoid

AI & ML interests

Working on small sota models

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updated a model 34 minutes ago

CEAMFA/palmer-007-preview

liked a model about 3 hours ago

harrrshall/BarunLM-35M

updated a collection about 17 hours ago

cool models out there

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reacted to FredyRivera-dev's post with 🚀 3 days ago

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6248

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.

3 replies

posted an update 3 days ago

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i love reinforcement learning

1 reply

reacted to danielhanchen's post with 🔥 4 days ago

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Introducing Unsloth for AMD 🚀
You can now train & run LLMs on your AMD hardware

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

GitHub: https://github.com/unslothai/unsloth
Blog + Guide: https://unsloth.ai/docs/basics/amd

3 replies

replied to their post 4 days ago

Yes, I got the script for ArithMark-3 and I can re-run it with no issues.

replied to their post 4 days ago

Mostly across harness details rather than a completed five-seed sweep. We have checked the checkpoint under repeated runs and small evaluation-pipeline variations, but I do not want to represent that as a formal variance estimate.

Your distinction is valid though, prompt formatting and answer parsing measure harness sensitivity, while multiple seeds under one frozen configuration might establish the actual noise band. I don't know if it will be published at all. Since is a little bit chaotic keeping base skills while improving them so our iteration method is barely reproducible.

replied to their post 4 days ago

That’s a fair point. At this scale, instruction tuning is definitely a capacity tradeoff rather than a free capability layer, so preserving the base model’s strengths will be one of the main acceptance criteria.

Regarding the post-merge result, we have done additional validation runs and the performance appears directionally consistent, although I would prefer to publish the full repeated-evaluation results once the methodology and comparison conditions are finalized. At 90M, even small evaluation details can meaningfully affect the reported ranking.

The instruct version will be treated as a separate checkpoint rather than a replacement for the base model.

replied to their post 5 days ago

Can't wait to see what the community 🪐do with this! 🚀🚀🚀

posted an update 5 days ago

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1373

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 assistant—**what dataset would you recommend we use for the instruct tune?**

---
🔗 **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! 👇

appvoid/palmer-006

10 replies

reacted to AxionLab-official's post with ❤️❤️ 5 days ago

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⚠️ Community Notice

We would like to clarify that SupraLabs has no affiliation, partnership, or connection whatsoever with "SupraLarps" or its members.

Please avoid interacting with their organization, repositories, or Spaces under the assumption that they are associated with us.

We are currently aware of the situation and have already contacted the appropriate channels to address it.

Thank you to everyone who continues to support SupraLabs. ❤️

12 replies

reacted to Banaxi-Tech's post with 🔥 5 days ago

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7882

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!

4 replies

posted an update 6 days ago

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...for tomorrow.

reacted to ucr-max's post with 👍 7 days ago

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Introducing Limen0.2B

We are releasing Limen0.2B, a 222.5M-parameter base language model developed as a research platform for efficient pretraining and superword tokenization at smaller scales.

Limen0.2B was trained from scratch on 50B tokens and uses a compact 16K BoundlessBPE vocabulary. The project explores whether SuperBPE-style tokenization can remain effective in a substantially smaller model and vocabulary regime than those examined in earlier large-scale experiments.

The model also combines a deep-and-narrow transformer design with Exclusive Self-Attention, grouped-query attention, and tied embeddings. Its compact vocabulary reduces the embedding footprint and leaves a larger share of the parameter budget available to the transformer layers.

Despite its relatively modest training budget, Limen0.2B achieves competitive results for its scale across the reported language understanding, commonsense reasoning, and grammatical evaluation tasks. Comparisons with other compact models are provided as context rather than strict rankings, as their training data, token budgets, architectures, and evaluation settings differ.

The release includes the model weights, implementation, training configuration, checkpoint progression, and evaluation results, all under Apache 2.0.

UniversalComputingResearch/Limen0.2B

Technical feedback, independent evaluations, and further experiments with the model and tokenizer are welcome.

replied to Banaxi-Tech's post 7 days ago

Cool stuff right there! Keep it up

reacted to Banaxi-Tech's post with 🔥 7 days ago

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3574

We're excited to release BananaMindBench Leaderboard, our leaderboard for BananaMind Base Bench 1.1.
It measures model performance on a variety of different tasks:
Language Completion
Common sense too
World Knowledge
Context Tracking
Quantitative
Logical Reasoning
Code Completion
Each has a different score and 1 overall score.
Submit your own model:
BananaMind/BananaMindBench-Leaderboard
Check it out:
BananaMind/BananaMindBench-Leaderboard

15 replies

reacted to Nymbo's post with 🔥 7 days ago

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5416

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)