deepreinforce-ai/Ornith-1.0-9B
Text Generation • 1.47M • Updated • 1.5k • • 165
Tyler Williams PRO
unmodeled-tyler
AI & ML interests
engineer & solo builder of VANTA Research/Quanta Intellect
Recent Activity
liked a model about 2 hours ago
deepreinforce-ai/Ornith-1.0-9B updated a collection 10 days ago
My Current Open Source Daily Drivers liked a model 11 days ago
zai-org/GLM-5.2Organizations
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reacted to wenhuach's post with 🔥 about 1 month ago
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4559
🚀 We provide **free** hardware to quantize models at the [Intel Low Bit Open LLM Leaderboard]( Intel/low_bit_open_llm_leaderboard), currently supporting
⭐ If you find it useful, please consider starring the AutoRound project on [GitHub](https://github.com/intel/auto-round)!
Pure RTN mode powered by AutoRound⭐ If you find it useful, please consider starring the AutoRound project on [GitHub](https://github.com/intel/auto-round)!
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reacted to kalyan-ks's post with 👀 about 1 month ago
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1628
LLM Guardrail Models are Less Robust Against Text Mutation Attacks
Blog post - https://huggingface.co/blog/kalyan-ks/llm-guardrail-models-less-robust
Evaluated the robustness of three LLM guardrail models (GLiGuard, LlamaGuard3 and MiniGuard).
Evaluation is done using 16 text mutation attacks over three datasets (AEGIS 2.0, WildGuard and ExpGuard).
Achieved average Unsafe ASR score of up to 33% and average Safe ASR score of up to 25% against GLiGuard model.
Achieved average Unsafe ASR score of up to 35% and average Safe ASR score of up to 17% against LlamaGuard3-8B model.
Achieved average Unsafe ASR score of up to 45% and average Safe ASR score of up to 15% against MiniGuard v0.1 model.
Blog post - https://huggingface.co/blog/kalyan-ks/llm-guardrail-models-less-robust
Evaluated the robustness of three LLM guardrail models (GLiGuard, LlamaGuard3 and MiniGuard).
Evaluation is done using 16 text mutation attacks over three datasets (AEGIS 2.0, WildGuard and ExpGuard).
Achieved average Unsafe ASR score of up to 33% and average Safe ASR score of up to 25% against GLiGuard model.
Achieved average Unsafe ASR score of up to 35% and average Safe ASR score of up to 17% against LlamaGuard3-8B model.
Achieved average Unsafe ASR score of up to 45% and average Safe ASR score of up to 15% against MiniGuard v0.1 model.
replied to PhysiQuanty's post about 1 month ago
This is such a cool project!
reacted to PhysiQuanty's post with 🔥 about 1 month ago
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4578
🧬 You can now find out whether your cognitive soulmate has already existed among 50k anonymized profiles ✨
SpiceeChat/Check-If-Your-Soulmate-Has-Already-Existed
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You seek them: 79.7% | They may seek you: 84.1% (coming soon)
🔥 Powered by open source and too much coffee 🔥
SpiceeChat/Check-If-Your-Soulmate-Has-Already-Existed
SpiceeChat/OkCupid-59k-Anonymized-Profiles
https://dating-fatigue.com/
You seek them: 79.7% | They may seek you: 84.1% (coming soon)
🔥 Powered by open source and too much coffee 🔥
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reacted to prithivMLmods's post with 🔥 about 1 month ago
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5597
I've made 8 Spaces in the Qwen-Image-Edit series, and out of them, 5 Spaces reached “Space of the Week”! A few Spaces are still topping the list even after many months.
Cumulatively, the series has crossed 8.2 million+ ZeroGPU runs and nearly 4 million visitors overall.
Thanks for all the community support! 🤗❤️
🔗 Spaces: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
Cumulatively, the series has crossed 8.2 million+ ZeroGPU runs and nearly 4 million visitors overall.
Thanks for all the community support! 🤗❤️
🔗 Spaces: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
- 4 replies
reacted to alvarobartt's post with 🚀 about 1 month ago
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Open agents on AWS SageMaker AI with open models from the Hugging Face Hub!
> Deploy an open model from the Hugging Face Hub on SageMaker AI
> Connect the deployed model to Strands Agents
> Add built-in and custom tools for tool calling
> Expose external capabilities through MCP integration
> Bonus: talk to your agent and visualize traces with Gradio
https://alvarobartt.com/agents-on-aws-sagemaker
> Deploy an open model from the Hugging Face Hub on SageMaker AI
> Connect the deployed model to Strands Agents
> Add built-in and custom tools for tool calling
> Expose external capabilities through MCP integration
> Bonus: talk to your agent and visualize traces with Gradio
https://alvarobartt.com/agents-on-aws-sagemaker
replied to their post about 1 month ago
Centauri is a new project and is rapidly improving!
posted an update about 1 month ago
Post
185
Centauri ADE: https://github.com/unmodeled-tyler/centauri
This is Centauri - It's a lightweight Agent Development Environment for Linux with git source control and codebase understanding.
I've tried out several different IDE/ADEs and nothing has really worked as well as I'd like for what I need. For the options that I did like, BYOK was second-class, or in the case of a full IDE, it sometimes felt like I was trying to get an airliner into low-earth-orbit.
Enter: Centauri - a simple local-first CLI harness wrapper with a focused git source-control workspace.
It gives you a clean side-by-side flow:
- run your preferred CLI coding agent in an embedded terminal
- watch repository changes appear in the Changes panel
- generate an industry-standard commit message
- commit and push without leaving the app
It's designed to complement tools like Claude Code, Codex, Pi, OpenCode, etc. It does not replace Git, your terminal, or your existing credentials - it wraps the tools you already use into a tighter cohesive workspace optimized for engineering with an agent.
I built Centauri for myself after trying way too many different options. It solves several pain points for me and I'm sharing it in case it solves some for you too!
This is Centauri - It's a lightweight Agent Development Environment for Linux with git source control and codebase understanding.
I've tried out several different IDE/ADEs and nothing has really worked as well as I'd like for what I need. For the options that I did like, BYOK was second-class, or in the case of a full IDE, it sometimes felt like I was trying to get an airliner into low-earth-orbit.
Enter: Centauri - a simple local-first CLI harness wrapper with a focused git source-control workspace.
It gives you a clean side-by-side flow:
- run your preferred CLI coding agent in an embedded terminal
- watch repository changes appear in the Changes panel
- generate an industry-standard commit message
- commit and push without leaving the app
It's designed to complement tools like Claude Code, Codex, Pi, OpenCode, etc. It does not replace Git, your terminal, or your existing credentials - it wraps the tools you already use into a tighter cohesive workspace optimized for engineering with an agent.
I built Centauri for myself after trying way too many different options. It solves several pain points for me and I'm sharing it in case it solves some for you too!
- 1 reply
liked a
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reacted to HannesVonEssen's post with 🔥 about 1 month ago
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4916
📣 Add architecture visualization to model card!
🌟 For all creators out there: add a model visualization to your model card to capture your audience's attention!
🖱️ When clicked, it opens an interactive view with multiple levels of granularity!
1️⃣ Paste url at https://hfviewer.com/model-card-embed
2️⃣ Paste generated code in your README.md!
3️⃣ ✨
🌟 For all creators out there: add a model visualization to your model card to capture your audience's attention!
🖱️ When clicked, it opens an interactive view with multiple levels of granularity!
1️⃣ Paste url at https://hfviewer.com/model-card-embed
2️⃣ Paste generated code in your README.md!
3️⃣ ✨
reacted to juiceb0xc0de's post with 🔥 about 1 month ago
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1508
Introducing the Gemma-4-E2B Brain Atlas, an interactive neural census of every layer, every head, 16 behavior categories in Google's flagship 2B model. We ran 184,320 probe prompts across 35 layers × 8 components and mapped what came back.
The Brain Atlas is an interactive tool that lets you explore the internal behavior of Google's Gemma-4-E2B model layer by layer, head by head. Pick a behavior category, pick a layer, and see exactly which components light up and which go quiet. The dataset is fully queryable if you want to go deeper.
The mapping combines multiple single-direction techniques run in parallel across every layer and component. Activation taxonomy (classifying each neuron by how broadly it fires across prompt categories), coactivation pair analysis (which neurons lock together and on what topics), F-stat behavioral separation (one-way ANOVA per feature across 16 behavior categories), per-head specificity scoring, and a full compliance probe pipeline using SVD, sparse decomposition, and variance analysis.
Here's what I found when I ran it.
The sharpest behavioral signal isn't at the output. It's Layer 0. Up projection hits F=22.7, nearly 2x anything in the final third of the network. The model does its behavioral sorting before it's barely started, then spends the next 34 layers… doing what exactly?
The gate has a lifecycle. 70% dormant at L1, highest in the model. Brutal sparsification at L23–26 (>58% silent). Then reopens. The final five layers are the most alive gates anywhere. The model's last act is a gate flare.
Layer 4 routes 5 projections to dim 448. One layer. One dimension. That's a topology highway.
Zero specialist neurons. Not one. 1.2M neurons analyzed. None fires exclusively on a single category. This model distributes everything.
🧠 Space: juiceb0xc0de/gemma-4-e2b-brain-atlas
📊 Dataset (1.3M rows, fully queryable): juiceb0xc0de/gemma-4-e2b-atlas
The Brain Atlas is an interactive tool that lets you explore the internal behavior of Google's Gemma-4-E2B model layer by layer, head by head. Pick a behavior category, pick a layer, and see exactly which components light up and which go quiet. The dataset is fully queryable if you want to go deeper.
The mapping combines multiple single-direction techniques run in parallel across every layer and component. Activation taxonomy (classifying each neuron by how broadly it fires across prompt categories), coactivation pair analysis (which neurons lock together and on what topics), F-stat behavioral separation (one-way ANOVA per feature across 16 behavior categories), per-head specificity scoring, and a full compliance probe pipeline using SVD, sparse decomposition, and variance analysis.
Here's what I found when I ran it.
The sharpest behavioral signal isn't at the output. It's Layer 0. Up projection hits F=22.7, nearly 2x anything in the final third of the network. The model does its behavioral sorting before it's barely started, then spends the next 34 layers… doing what exactly?
The gate has a lifecycle. 70% dormant at L1, highest in the model. Brutal sparsification at L23–26 (>58% silent). Then reopens. The final five layers are the most alive gates anywhere. The model's last act is a gate flare.
Layer 4 routes 5 projections to dim 448. One layer. One dimension. That's a topology highway.
Zero specialist neurons. Not one. 1.2M neurons analyzed. None fires exclusively on a single category. This model distributes everything.
🧠 Space: juiceb0xc0de/gemma-4-e2b-brain-atlas
📊 Dataset (1.3M rows, fully queryable): juiceb0xc0de/gemma-4-e2b-atlas
reacted to espejelomar's post with ❤️ about 1 month ago
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4732
Sharing WorldForge with @abdelstark
It's an open-source Python project for evaluating and replaying robotics and world-model workflows.
The useful part is not only calling a model. WorldForge records the run, validates action shapes, translates outputs into actions, and keeps replay artifacts you can inspect later.
The current demo uses LeRobot + LeWorldModel on PushT through the official loader:
The harness also has replay-only paths for Cosmos-Policy and GR00T-style outputs, so you can inspect the provider contract from saved artifacts without keeping a GPU server online.
Try it:
Repo: https://github.com/AbdelStark/worldforge
Docs: https://abdelstark.github.io/worldforge/
Pre-1.0, MIT, and actively looking for contributors. Good areas:
- robotics provider adapters
- replay artifacts
- eval flows
- docs & first-run demos
Good first issues: https://github.com/AbdelStark/worldforge/contribute
If you're building robot policy evals or model adapters, would love a PR — or an issue describing what's missing.
It's an open-source Python project for evaluating and replaying robotics and world-model workflows.
The useful part is not only calling a model. WorldForge records the run, validates action shapes, translates outputs into actions, and keeps replay artifacts you can inspect later.
The current demo uses LeRobot + LeWorldModel on PushT through the official loader:
stable_worldmodel.policy.AutoCostModel("pusht/lewm")The harness also has replay-only paths for Cosmos-Policy and GR00T-style outputs, so you can inspect the provider contract from saved artifacts without keeping a GPU server online.
Try it:
pip install worldforge-aiuv run --extra harness worldforge-harness --flow robotics-compareRepo: https://github.com/AbdelStark/worldforge
Docs: https://abdelstark.github.io/worldforge/
Pre-1.0, MIT, and actively looking for contributors. Good areas:
- robotics provider adapters
- replay artifacts
- eval flows
- docs & first-run demos
Good first issues: https://github.com/AbdelStark/worldforge/contribute
If you're building robot policy evals or model adapters, would love a PR — or an issue describing what's missing.
updated a
collection about 1 month ago
upvoted a paper about 1 month ago