🔄 In a Training Loop

Dean Byrne PRO

Quazim0t0

25 8 111

https://huggingface.co/DaisyChainAI

AI & ML interests

DaisyChainAI🌼 / SmallLM's / San Francisco / Open Source

Recent Activity

updated a model 2 days ago

Quazim0t0/Qwenx86-4B-compact

liked a model 3 days ago

MinimaLabs/KLM-1.5

updated a model 5 days ago

Quazim0t0/Qwenx86-4B-GGUF

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posted an update 7 days ago

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Quazim0t0/Qwenx86-4B-compact

▶️ See it in action: Quazim0t0/Qwenx86-Debugger . Walk through actual recorded tool calls: the conversation, x86 registers and flags, code at the program counter, RAM and machine output, with all verified blocks on a timeline.

Qwen3.5-4B with a 64-bit x86 computer compiled into its weights: 13 transformer layers ahead of Qwen's own. There are no learned weights in them; all of their weights are computed by a compiler. Qwen invokes the computer through its own tool calls (<tool_call>), and the computer in the weights executes the tool. Calculator, Python, C compiler, notes store, physics/chemistry lookup and Python-docs lookup are all firmware in the checkpoint. This is the compact checkpoint: ~9 GB GPU memory, so it fits on a 16 GB GPU.

Edit: GGUF Release: Quazim0t0/Qwenx86-4B-GGUF

Credits: Qwenputer by Divyansh Agrawal (@divagr1925 , github.com/divagr18/qwenputer, MIT)

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reacted to DedeProGames's post with 🤗🤯🧠🚀🔥 25 days ago

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🚀 Introducing Kiyo-230M-Instruct — my compact English & Portuguese chat model!

Built on Kiyo-230M-Preview, a base model I pretrained from scratch, Kiyo now has an instruction-tuned release for everyday conversations, rewriting, and lightweight assistant experiments.

🧠 229.7M parameters
🌍 English + Portuguese
💬 Native chat template for multi-turn conversations
📏 2,048-token context window
📦 459 MB of BF16 safetensors weights
🔓 Apache-2.0 license

For instruction tuning, I used 165,735 conversations from SmolTalk2, smol-smoltalk, UltraChat, and Dolci-Instruct-SFT. Training ran for two complete epochs with assistant-only loss, using a roughly 70% English / 30% Portuguese token mix.

The updated model card includes benchmarks against SmolLM2-135M-Instruct, BananaMind, and Supra2, plus example conversations, CPU measurements, training details, and a Transformers quickstart.

The results are mixed: Kiyo is competitive with these small models across the reported evaluations, while SmolLM2 and BananaMind score higher on the instruction-following benchmarks. Portuguese was a highlight in the small conversational sanity test.

This is an experimental 230M model, with plenty of room to improve reasoning, factual accuracy, and strict instruction following. I’m excited to share it with people exploring how much we can get from small, accessible models!

🤗 Model & full README:
DedeProGames/Kiyo-230M-Instruct

💬 Try the chat demo:
DedeProGames/kiyo-230m-chat

Try it in English or Portuguese — I’d love to see your examples, feedback, and experiments!

9 replies

reacted to DedeProGames's post with 🔥 about 1 month ago

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🧱 SLM Tetris Arena: can a small language model play Tetris without ever being trained on it?

I built an arena where tiny decoder-only LMs (50K–250M params) play Tetris zero-shot. There is no fine-tuning and no game data. They only use what they picked up from pre-training on text.

How it works:
- For every piece, the engine simulates each legal placement and describes the result in plain English ("clears one line, creates no new holes, keeps the stack low…").
- The model never sees the grid. It reads each description, and the arena compares log P(" good move") with log P(" bad move"). The best-rated placement is played.
- Every player gets the same piece sequence, so it's a fair race.
- There are two protocols: Guided (the rules are in the prompt) and Blind (no rules, only pre-training knowledge).

Two ways to play:
- Match: pick any models (even your own, custom architectures welcome) and watch them play side by side on retro 8-bit boards.
- Ranked: press Play and the arena picks up to 4 models at random from a curated pool of 29. Nobody chooses their opponents, so Elo can't be farmed. Matches run on the server and count even if you close the tab.

First results (~225 ranked matches):
- gpt2 (124M) leads with 1283 Elo, but SupraNeo-4M (4M) is right behind at 1239. Next come LowOnMind-5M and BananaMind-2.1-Pico (1.5M!).
- Model size barely predicts Elo (r ≈ 0.06). Survival does (r ≈ 0.9): the models that avoid holes and keep the stack low are the ones that win.

Every ranked match (seed, model commit SHAs, scores, Elo before/after) is logged in a public dataset.

▶ Play: DedeProGames/SLM-Tetris-Arena
📊 Results: DedeProGames/lm-tetris-arena-results

Want your model in the Ranked pool? Drop it in the comments!

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replied to their post about 1 month ago

Good catch. Shipped decision_config.json was stale vs the weights.

DecisionAgent does not read that file. It reads temperatures from the checkpoint’s embedded decision_cfg. On-Fly-Jev.pt embeds [1.0, 1.0, 1.0] and has no per-bucket temps. So 0.12 never ran. The clamp never ran. Every number I reported is T = 1.0.

Accuracy does not care. Temp does not move argmax, and it does not move the noul 0.5 cut.

You were right about choice:2. That fit was junk. Hold-out was 12 items: 8 SST2, 2 IMDB, 2 other. It slammed into the 0.1 floor. SST2 got worse, not better:

• T = 1.0, choice-form ECE 0.254
• T = 0.5, 0.297
• T = 0.12, 0.310

One correction: about half of SST2 and IMDB are noul:2, not choice:2.

What I changed:

• Config now matches the weights.
• A bucket needs at least 100 items or it falls back to the question-type temperature.
• Fits clamp to [0.5, 5.0], same as the agent.

I refit on 4,093 held-out benchmark items, kept off the eval set. Ceiling is real. At T = 1, typed is fine (ECE 0.045) and the benchmarks are overconfident (0.197). Fitted temps pull benchmarks to 0.085 and wreck typed (0.245). One T per bucket cannot do both jobs.

Published model stays T = 1. Benchmark-calibrated copy is in calibrated/. README says both now.

reacted to ErenAta00's post with 🔥 about 1 month ago

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Maverick-4B-Unity-XR-Agent is now on Hugging Face.

It's a 4B model that turns spoken or typed English into actions in Unity scenes. Say "put the red mug on the table" or "turn on the lamp", and it returns the tool call your app executes. If a command could mean two objects, it asks which one. If it can't do something, it says so instead of guessing.

Everything runs on the user's machine through llama.cpp: no API key, no internet connection. The Q4_K_M GGUF is 2.5 GB and needs about 3 GB of GPU memory, so it fits on a 4 GB laptop GPU and usually answers in one to three seconds. It is fine-tuned from Qwen3-4B with QLoRA on about 20,000 English conversations.

Results:
- 83.8% on 499 human-written ALFRED instructions (right action on the right object). The base model, Qwen3-4B, scores 57.1%. The strongest of the five other models we tested, from 1.7B to 120B parameters, was Ministral 3 14B at 67.1%.
- 91.7% on object types it never saw in training.
- 97.3% on 440 commands run through a live Unity scene.

There is also a Unity package that starts the model, describes the scene to it and carries out its tool calls. You install it from the Package Manager with a Git URL.

Model: https://huggingface.co/ErenAta00/Maverick-4B-Unity-XR-Agent-GGUF
Unity package: ErenAta00/Maverick-Unity
Full write-up: https://huggingface.co/blog/ErenAta00/maverick-4b-unity-xr-agent

Built at the Extended Reality Laboratory (XRLab), Manisa Celal Bayar University:

ExtendedRealityLabMCBU
Released under Apache-2.0. Feedback and bug reports are welcome in the Community tab.

posted an update about 2 months ago

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On-Fly-Jev is up: Quazim0t0/On-Fly-Jev

Second Jev-style model. First was Byrne-Jev (70M SpikeWhale). This one is a 96M spiking trunk - every unit is a copy of one of 100 real MaleCNS fly neurons - plus a typed-decision head. One forward pass. No generated text.

I built it for speed.

Typed-decisions test split, 400 cases, 2,000 decisions, 5 questions per case, local GPU:

• 7.3 ms p50 per decision (~137 / s)
• 36.6 ms p50 / 53.5 ms p95 per case of 5
• ~27 cases / s

Same protocol vs the others:

• On-Fly-Jev: 36.6 ms / case, ~137 decisions / s
• Byrne-Jev: 110.7 ms / case, ~45 / s
• ModernBERT-base: 349 ms / case, ~14 / s
• TypeSafe Jev 1.13 (hosted, so network is in it): 710 ms / case, ~7 / s

About 3x Byrne-Jev, 9.5x ModernBERT, 19x TypeSafe Jev per case.

Live ViZDoom, 1 question per tick including game I/O: 23.4 ms (~43 / s).

• Accuracy 0.666 (Byrne-Jev 0.630, Jev 1.13 0.727)
• ECE 0.045, same as Byrne-Jev, about a third of Jev 1.13

50/50 merge of two checkpoints from one run. Research artifact, not a chatbot. More videos are on the card.

4 replies

reacted to Enderchef's post with 🧠❤️🚀🔥 about 2 months ago

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AxiomicLabs released new benchmark, Tiny Theory of Mind, to test your SLM models' Theory of Mind Intuition!
Check it out and like it!

AxiomicLabs/Tiny_Theory_of_Mind

reacted to kostakoff's post with 👍 about 2 months ago

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3,186

Canceling My Pro Subscription

I'm officially canceling my Hugging Face Pro subscription today.
I supported this platform because it stood for true openness and neutrality. This acquisition by NVIDIA fundamentally changes that.

Here’s why I’m against this deal:
- Neutrality is dead. NVIDIA is a US-based company. This means US regulations will inevitably dictate platform policies, creating direct pressure on Chinese developers and anyone building open-weight models outside the US.
- Community over bureaucracy. NVIDIA is a massive, slow-moving corporation. This acquisition will likely drown the community in corporate processes and commercial interests. Soon, uploading a simple finetune might become a bureaucratic nightmare.
- Open vs. Proprietary. Hugging Face was built on open-source ideals. NVIDIA? They are a fiercely proprietary hardware company with a minimal track record of meaningful open-source contributions. They sell chips, not freedom.
- And to add insult to injury, NVIDIA has practically abandoned consumer RTX GPUs in 2026 to chase data center profits. Why would I pay them for "openness" when they've turned their back on the very developers who built this ecosystem?

I paid for openness. Not for a corporate takeover.

🤗 was about community.

12 replies

reacted to Datdanboi25's post with 🔥❤️ about 2 months ago

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ForgePlex-M1-6M first model trained on AxiomicLabs TrainWork

ForgeWorks/ForgePlex-M1-6M just dropped from

ForgeWorks , and is the first model to ever be trained on our TrainWork training framework.

Achieving an Intelligence Index of 6.87 and taking #22 in the <10m category on the AxiomicLabs/Open_SLM_Leaderboard, very impressive work for a first model.

Give it some love!

7 replies

reacted to Yuki131's post with 🔥 about 2 months ago

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Meet KaLM-Jev — your local, Jev-style judgment engine, available in Nano, Small, and Large.

Building an agent or automation workflow? Sometimes all you need is a choice, a score, or a signal that a condition holds.

Built on KaLM-Reranker-R2, KaLM-Jev turns these decisions into structured outputs through three primitives:

🔀 Choice — select among candidates, with a probability distribution.
📊 Score — return a continuous score over your defined levels.
🔍 Noul — evaluate conditions independently, so multiple conditions can hold at once.

Think support-ticket routing, bug severity scoring, human-escalation detection, or candidate tool selection for agents.

🖥️ Run locally with downloaded weights
📦 Choose from Nano / Small / Large
🔌 Integrate through HTTP or Python
⚡ Reuse cached candidate/rule representations to reduce repeated encoding
🧪 Explore included examples, bilingual semantic smoke tests, and recorded GPU validation results

No answer-text generation: output_tokens = 0. Inference still runs to compute the judgments.

KaLM-Jev is an independent implementation based on KaLM-Reranker, not an official TypeSafe project or a guarantee of full Jev compatibility. Scores are uncalibrated; validate thresholds on your own tasks.

Code & quickstart:
https://github.com/KaLM-Embedding/KaLM-Jev
Yuki131/KaLM-Jev
We’d love to hear what you’d build with it. Try it out, share feedback, or open an issue! 🤗

#Jev #Reranker #Agents #LocalAI #OpenSource

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reacted to FlameF0X's post with 🔥 about 2 months ago

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Personally, I don't think bot accounts on Hugging Face are a good thing, as we don't know how many accounts are run by automated systems versus how many actual users there are. Dead Internet theory is already a thing.

To clarify I am taking about LLM powered bot accounts and NOT rule base once like @parquet-converter or others.

also I'd like to talk with HUMANS not a machine so I'm going to hide messages from bots.

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posted an update about 2 months ago

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I turned the fruit fly's connectome into a language model. It learned broken English.

MaleCNS v1.0 (Janelia / Google), as released. 167,565 neurons, 25.6M synapses. I made that the core of a spiking net and trained synapse strengths only. The wiring is still the fly's.

It learned language. ~16M tokens in, it produces stuff like Once upon a time, there was a girl smiled. The language is in the brain's activity, not just the readout.

It sees through its own eyes. Photoreceptors on both eyes into the optic lobes. A dopamine reward through the fly's own PAM / PPL1 cells is what actually got it to use the pictures.

It's still a fly. Put it back in a whole-brain fly sim and sugar still fires the proboscis.

It can live as a fly again. 30 simulated days with the language synapses frozen: the rest of the brain adapted around them. Language and reflex both still there.

Talk to it, show it pictures, sugar test, or let it live 1-30 days:

Space: Quazim0t0/MaleCNS-Fly

Model, code, write-up: Quazim0t0/MaleCNS-SpikeWhale-Anatomy-LM

Research experiment, not a chatbot. Simulation of the released wiring.

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Dean Byrne PRO

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