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 about 6 hours ago
Quazim0t0/On-Fly-Jev liked a model about 7 hours ago
SlayerLab/gollem-v5-ckptsOrganizations
updated a
model about 6 hours ago
liked a
model about 7 hours ago
reacted to ErenAta00's post with 🔥 about 7 hours ago
Post
971
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: 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.
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: 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:
Released under Apache-2.0. Feedback and bug reports are welcome in the Community tab.
posted an update 2 days ago
Post
158
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 2 days ago
updated a
Space 2 days ago
updated a
model 2 days ago
reacted to Enderchef's post with 🧠❤️🚀🔥 4 days ago
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3000
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 👍 5 days ago
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3147
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 🔥❤️ 5 days ago
Post
2915
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
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updated a
Space 6 days ago
reacted to Yuki131's post with 🔥 7 days ago
Post
3622
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