Quazim0t0/On-Fly-Jev
Text Classification • Updated • 3
Dean Byrne PRO
Quazim0t0
AI & ML interests
DaisyChainAI🌼 / SmallLM's / San Francisco / Open Source
Recent Activity
updated a model 1 day ago
Quazim0t0/On-Fly-Jev posted an update 1 day ago
On-Fly-Jev is up: https://huggingface.co/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. published a model 1 day ago
Quazim0t0/On-Fly-JevOrganizations
updated a
model 1 day ago
posted an update 1 day ago
Post
133
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.
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.
published a
model 1 day ago
updated a
Space 1 day ago
updated a
model 1 day ago
reacted to Enderchef's post with 🧠❤️🚀🔥 4 days ago
Post
2997
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
Check it out and like it!
AxiomicLabs/Tiny_Theory_of_Mind
liked a
dataset 4 days ago
reacted to kostakoff's post with 👍 4 days ago
Post
3146
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.
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 🔥❤️ 4 days ago
Post
2912
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!
ForgeWorks/ForgePlex-M1-6M just dropped from
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
updated 2
models 5 days ago
published a
model 5 days ago
updated a
Space 6 days ago
reacted to Yuki131's post with 🔥 6 days ago
Post
3620
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:
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
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
- 1 reply
reacted to FlameF0X's post with 🔥 7 days ago
Post
4549
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.
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.
- 24 replies
posted an update 7 days ago
Post
199
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.
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.
- 1 reply