LLMs (LLMs)

Nymbo

posted an update about 16 hours ago

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122

I have a few updates to my MCP server I wanna share: New Memory tool, improvements to web search & speech generation.

# Memory_Manager Tool

We now have a Memory_Manager tool. Ask ChatGPT to write all its memories verbatim, then tell gpt-oss-20b to save each one using the tool, then take them anywhere! It stores memories in a memories.json file in the repo, no external database required.

The Memory_Manager tool is currently hidden from the HF space because it's intended for local use. It's enabled by providing a HF_READ_TOKEN in the env secrets, although it doesn't actually use the key for anything. There's probably a cleaner way of ensuring memory is only used locally, I'll come back to this.

# Fetch & Websearch

The Fetch_Webpage tool has been simplified a lot. It now converts the page to Markdown and returns the page with three length settings (Brief, Standard, Full). This is a lot more reliable than the old custom extraction method.

The Search_DuckDuckGo tool has a few small improvements. The input is easier for small models to get right, and the output is more readable.

# Speech Generation

I've added the remaining voices for Kokoro-82M, it now supports all 54 voices with all accents/languages.

I also removed the 30 second cap by making sure it computes all chunks in sequence, not just the first. I've tested it on outputs that are ~10 minutes long. Do note that when used as an MCP server, the tool will timeout after 1 minute, nothing I can do about that for right now.

# Other Thoughts

Lots of MCP use cases involve manipulating media (image editing, ASR, etc.). I've avoided adding tools like this so far for two reasons:

1. Most of these solutions would require assigning it a ZeroGPU slot.
2. The current process of uploading files like images to a Gradio space is still a bit rough. It's doable but requires additional tools.

Both of these points make it a bit painful for local usage. I'm open to suggestions for other tools that rely on text.

KnutJaegersberg

posted an update 4 days ago

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921

What's missing for AGI

Current transformer-based, self-supervised systems have driven massive gains, but important gaps remain on the path to AGI. Key missing pieces are continual, curiosity-driven learning; grounded multimodal perception; reliable, contextual long-term memory with forgetting; motivated (hot) executive control and dynamic attention; metacognition and coherent causal world-models; and robust fluid reasoning, planning and decision-making. Progress will require hybrid architectures (neuromorphic/Hebbian + gradients + symbolic modules), active-inference and intrinsic-motivation objectives, and new lifelong, embodied benchmarks to evaluate safety and competence.

https://huggingface.co/blog/KnutJaegersberg/whats-missing-for-agi-in-todays-tech-trajectories

yanghaojin

posted an update 5 days ago

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212

I’m excited to share this article https://huggingface.co/blog/yanghaojin/greenbitais-4-bit-models-redefine-ai-efficiency, showcasing the power of low-bit small models in programming tasks. 🚀 Stay tuned—the next post will dive into test cases for small model–driven agentic applications!

yanghaojin

posted an update 6 days ago

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138

Our new blog post Smaller Models, Smarter Agents 🚀 https://huggingface.co/blog/yanghaojin/greenbit-3-bit-stronger-reasoning
DeepSeek’s R1-0528 proved that 8B can reason like 235B. Anthropic showed that multi-agent systems boost performance by 90%. The challenge? Both approaches burn massive compute and tokens.
💡 GreenBitAI cracked the code:
We launched the first 3-bit deployable reasoning model — DeepSeek-R1-0528-Qwen3-8B (3.2-bit).
✅ Runs complex multi-agent research tasks (e.g. Pop Mart market analysis)
✅ Executes flawlessly on an Apple M3 laptop in under 5 minutes
✅ 1351 tokens/s prefill, 105 tokens/s decode
✅ Near-FP16 reasoning quality with just 30–40% token usage
This is how extreme compression meets collaborative intelligence — making advanced reasoning practical on edge devices.

Nymbo

posted an update 13 days ago

Post

718

I built a general use MCP space ~ Fetch webpages, DuckDuckGo search, Python code execution, Kokoro TTS, Image Gen, Video Gen.

# Tools

1. Fetch webpage
2. Web search via DuckDuckGo (very concise, low excess context)
3. Python code executor
4. Kokoro-82M speech generation
5. Image Generation (use any model from HF Inference Providers)
6. Video Generation (use any model from HF Inference Providers)

The first four tools can be used without any API keys whatsoever. DDG search is free and the code execution and speech gen is done on CPU. Having a HF_READ_TOKEN in the env variables will show all tools. If there isn't a key present, The Image/Video Gen tools are hidden.

Nymbo/Tools

Nymbo

posted an update 22 days ago

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961

Anyone using Jan-v1-4B for local MCP-based web search, I highly recommend you try out Intelligent-Internet/II-Search-4B

Very impressed with this lil guy and it deserves more downloads. It's based on the original version of Qwen3-4B but find that it questions reality way less often. Jan-v1 seems to think that everything it sees is synthetic data and constantly gaslights me

Parveshiiii

posted an update about 1 month ago

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1003

🚀 Just Dropped: MathX-5M — Your Gateway to Math-Savvy GPTs

👨‍🔬 Wanna fine-tune your own GPT for math?
🧠 Building a reasoning agent that actually *thinks*?
📊 Benchmarking multi-step logic across domains?

Say hello to [**MathX-5M**]( XenArcAI/MathX-5M) — a **5 million+ sample** dataset crafted for training and evaluating math reasoning models at scale.

Built by **XenArcAI**, it’s optimized for:
- 🔍 Step-by-step reasoning with , , and formats
- 🧮 Coverage from arithmetic to advanced algebra and geometry
- 🧰 Plug-and-play with Gemma, Qwen, Mistral, and other open LLMs
- 🧵 Compatible with Harmony, Alpaca, and OpenChat-style instruction formats

Whether you're prototyping a math tutor, testing agentic workflows, or just want your GPT to solve equations like a pro—**MathX-5M is your launchpad**.

🔗 Dive in: ( XenArcAI/MathX-5M)

Let’s make open-source models *actually* smart at math.
#FineTuneYourGPT #MathX5M #OpenSourceAI #LLM #XenArcAI #Reasoning #Gemma #Qwen #Mistral

Abhaykoul

posted an update about 1 month ago

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3959

🚀 Dhanishtha-2.0-preview-0825 Is Here

The Intermediate Thinking Model just leveled up again.

With sharper reasoning, better tool use, and expanded capabilities, Dhanishtha-2.0-preview-0825 is now live and ready to impress.

🧠 What Makes Dhanishtha Special?
Unlike typical CoT models that only thinks one time, Dhanishtha thinks iteratively:

> Think → Answer → Rethink → Improve → Rethink again if needed.

🔗 Try it now: HelpingAI/Dhanishtha-2.0-preview-0825

🔞 Dhanishtha NSFW Preview

For those exploring more expressive and immersive roleplay scenarios, we’re also releasing:

HelpingAI/Dhanishtha-nsfw
A specialized version tuned for adult-themed interactions and character-driven roleplay.

🔗 Explore it here: HelpingAI/Dhanishtha-nsfw

💬 You can also try all of these live at chat.helpingai.co

4 replies

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Parveshiiii

posted an update about 1 month ago

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🚀 Launch Alert: Dev-Stack-Agents
Meet your 50-agent senior AI team — principal-level experts in engineering, AI, DevOps, security, product, and more — all bundled into one modular repo.

+ Code. Optimize. Scale. Secure.
- Full-stack execution, Claude-powered. No human bottlenecks.

🔧 Built for Claude Code
Seamlessly plug into Claude’s dev environment:

* 🧠 Each .md file = a fully defined expert persona
* ⚙️ Claude indexes them as agents with roles, skills & strategy
* 🤖 You chat → Claude auto-routes to the right agent(s)
* ✍️ Want precision? Just call @agent-name directly
* 👥 Complex task? Mention multiple agents for team execution

Examples:

"@security-auditor please review auth flow for risks"
"@cloud-architect + @devops-troubleshooter → design a resilient multi-region setup"
"@ai-engineer + @legal-advisor → build a privacy-safe RAG pipeline"

🔗 https://github.com/Parveshiiii/Dev-Stack-Agents
MIT License | Claude-Ready | PRs Welcome

1 reply

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Abhaykoul

posted an update about 2 months ago

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3079

🎉 Dhanishtha-2.0-preview-0725 is Now Live

The Intermediate Thinking Model just got even better.
With the new update, Dhanishtha is now sharper, smarter, and trained further on tool use

🧠 What Makes Dhanishtha Different?
Unlike standard COT models that give one-shot responses, Dhanishtha thinks in layers:

> Think → Answer → Rethink → Improve → Rethink again if needed.

HelpingAI/Dhanishtha-2.0-preview-0725

chansung

posted an update 2 months ago

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3999

YAML engineering becomes more and more important than ever from infra provisioning to model training (recipes).

Here, I built a simple editor first for @dstackai , and I will share the live endpoint this week. Let me know what you think about this approach.

Based on this approach, if people think this is useful, I am going to do the same thing for the LLM training recipes for popular frameworks such as Hugging Face open-r1, Axolotl, and so on. Let me hear.

Parveshiiii

posted an update 2 months ago

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2686

🧠 Glimpses of AGI — A Vision for All Humanity
What if AGI wasn’t just a distant dream—but a blueprint already unfolding?

I’ve just published a deep dive called Glimpses of AGI, exploring how scalable intelligence, synthetic reasoning, and alignment strategies are paving a new path forward. This isn’t your average tech commentary—it’s a bold vision for conscious AI systems that reason, align, and adapt beyond narrow tasks.

🔍 Read it, upvote it if it sparks something, and let’s ignite a collective conversation about the future of AGI.

https://huggingface.co/blog/Parveshiiii/glimpses-of-agi

Parveshiiii

posted an update 2 months ago

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2837

🧠 MathX-5M by XenArcAI — Scalable Math Reasoning for Smarter LLMs

Introducing MathX-5M, a high-quality, instruction-tuned dataset built to supercharge mathematical reasoning in large language models. With 5 million rigorously filtered examples, it spans everything from basic arithmetic to advanced calculus—curated from public sources and enhanced with synthetic data.

🔍 Key Highlights:
- Step-by-step reasoning with verified answers
- Covers algebra, geometry, calculus, logic, and more
- RL-validated correctness and multi-stage filtering
- Ideal for fine-tuning, benchmarking, and educational AI

📂 - XenArcAI/MathX-5M

1 reply

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Abhaykoul

posted an update 2 months ago

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3039

🎉 Dhanishtha 2.0 Preview is Now Open Source!

The world's first Intermediate Thinking Model is now available to everyone!

Dhanishtha 2.0 Preview brings revolutionary intermediate thinking capabilities to the open-source community. Unlike traditional reasoning models that think once, Dhanishtha can think, answer, rethink, answer again, and continue rethinking as needed using multiple blocks between responses.

🚀 Key Features
- Intermediate thinking: Think → Answer → Rethink → Answer → Rethink if needed...
- Token efficient: Uses up to 79% fewer tokens than DeepSeek R1 on similar queries
- Transparent thinking: See the model's reasoning process in real-time
- Open source: Freely available for research and development

HelpingAI/Dhanishtha-2.0-preview
https://helpingai.co/chat

1 reply

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Nymbo

posted an update 2 months ago

Post

2830

Anyone know how to reset Claude web's MCP config? I connected mine when the HF MCP first released with just the default example spaces added. I added lots of other MCP spaces but Claude.ai doesn't update the available tools... "Disconnecting" the HF integration does nothing, deleting it and adding it again does nothing.

Refreshing tools works fine in VS Code because I can manually restart it in mcp.json, but claude.ai has no such option. Anyone got any ideas?

4 replies

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Abhaykoul

posted an update 2 months ago

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4518

Introducing Dhanishtha 2.0: World's first Intermediate Thinking Model

Dhanishtha 2.0 is the world's first LLM designed to think between the responses. Unlike other Reasoning LLMs, which think just once.

Dhanishtha can think, rethink, self-evaluate, and refine in between responses using multiple <think> blocks.
This technique makes it Hinghlt Token efficient it Uses up to 79% fewer tokens than DeepSeek R1
---

You can try our model from: https://helpingai.co/chat
Also, we're gonna Open-Source Dhanistha on July 1st.

---
For Devs:
🔑 Get your API key at https://helpingai.co/dashboard

from HelpingAI import HAI  # pip install HelpingAI==1.1.1
from rich import print

hai = HAI(api_key="hl-***********************")

response = hai.chat.completions.create(
    model="Dhanishtha-2.0-preview",
    messages=[{"role": "user", "content": "What is the value of ∫0∞𝑥3/𝑥−1𝑑𝑥 ?"}],
    stream=True,
    hide_think=False # Hide or show models thinking
)

for chunk in response:
    print(chunk.choices[0].delta.content, end="", flush=True)

2 replies

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Nymbo

posted an update 4 months ago

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4097

Haven't seen this posted anywhere - Llama-3.3-8B-Instruct is available on the new Llama API. Is this a new model or did someone mislabel Llama-3.1-8B?

1 reply

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Nymbo

posted an update 4 months ago

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2770

PSA for anyone using Nymbo/Nymbo_Theme or Nymbo/Nymbo_Theme_5 in a Gradio space ~

Both of these themes have been updated to fix some of the long-standing inconsistencies ever since the transition to Gradio v5. Textboxes are no longer bright green and in-line code is readable now! Both themes are now visually identical across versions.

If your space is already using one of these themes, you just need to restart your space to get the latest version. No code changes needed.

KnutJaegersberg

posted an update 4 months ago

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1147

Mining LLM Pretraining Data: Topics, Skills, and Cognitive Patterns

Summary
The technical blog post details an analysis of pretraining data from various Large Language Models (LLMs) like GPT-2, Falcon, and Gemma2. Using text mining techniques including embeddings, clustering, and LLM-based annotation on datasets like OpenWebText, The Pile, and C4, the study identified key patterns.

Findings show the data is dominated by topics like Technology, Politics, Health, Business, and Culture, originating from diverse sources including web scrapes, academic papers, code repositories, and news media. The data reflects the work of professionals primarily in Journalism/Media, Content Creation, Analysis/Research, Academia, and Tech/Engineering. Consequently, LLMs learn corresponding skills (e.g., Research, Critical Thinking, Communication, Domain Expertise) and task representations (e.g., Analysis, Content Creation, Compliance).

The analysis also uncovered distinct writing styles, underlying cognitive frameworks (beliefs, frames, schemas, memes), and common cognitive biases (like Confirmation Bias) embedded in the data. LLM capability progression appears linked to data scale and task frequency, following a power law. The study concludes that LLMs are powerful data-driven simulators whose capabilities and limitations are shaped by the composition and inherent biases of their pretraining corpora, highlighting the importance of data understanding and curation.

https://huggingface.co/blog/KnutJaegersberg/mining-llm-pretraining-data

KnutJaegersberg

posted an update 4 months ago

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2752

The Intelligence Curse

The document warns of the "intelligence curse," a potential consequence of advanced AI (AGI) where powerful entities lose their incentive to invest in people as AI automates work[cite: 13, 297]. This could lead to job displacement, reduced social mobility, and a concentration of power and wealth based on AI ownership, similar to the "resource curse" in resource-rich states[cite: 17, 18, 31, 329, 353]. To counter this, the authors propose averting AI catastrophes to prevent centralization, diffusing AI widely to keep humans economically relevant, and democratizing institutions to remain anchored to human needs[cite: 22, 23, 25, 35, 36, 37, 566].

https://intelligence-curse.ai/intelligence-curse.pdf

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