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danielhanchen

posted an update 2 days ago

Post

3109

We created a tool-calling guide for local LLMs!

Learn how to use any open model like Qwen3-Coder-Next and GLM-4.7-Flash for function calling.

Guide: https://unsloth.ai/docs/basics/tool-calling-guide-for-local-llms

We provide hands-on examples for: story writing, Python execution, terminal tool calls, maths and more.

6 replies

MaziyarPanahi

posted an update 1 day ago

Post

2068

🚨 Day 8/8: OpenMed Medical Reasoning Dataset Release - THE GRAND FINALE

Today I complete my 8-day release series with Medical-Reasoning-SFT-Mega.
The largest open medical reasoning dataset, combining 7 state-of-the-art AI models with fair distribution deduplication.

THE 7 SOURCE MODELS (Original Sample Counts):

1. Trinity-Mini: 810,284 samples
2. Qwen3-Next-80B: 604,249 samples
3. GPT-OSS-120B: 506,150 samples
4. Nemotron-Nano-30B: 444,544 samples
5. GLM-4.5-Air: 225,179 samples
6. MiniMax-M2.1: 204,773 samples
7. Baichuan-M3-235B: 124,520 samples

TOTAL BEFORE DEDUPLICATION: 2,919,699 samples

TOKEN COUNTS:
- Content tokens: 2.22 Billion
- Reasoning tokens: 1.56 Billion
- Total tokens: 3.78 Billion
- Samples with chain-of-thought: 100%

Quick Start:

from datasets import load_dataset
ds = load_dataset("OpenMed/Medical-Reasoning-SFT-Mega")

6 replies

DavidAU

posted an update 1 day ago

Post

1742

Tiny but mighty: LFM 1.2B - 11 Distill / Fine tunes : Exceeding all benchmarks at 300-700+ T/S on GPU, 60+ T/S CPU.

Almost all exceed LFM 1.2B Benchmarks - which are already very impressive.
All benchmarks posted.

A specialized merge of multiple of these fine tunes by @nightmedia FAR exceeds the benchmarks set by the already impressive LFM.

(LFM2.5-1.2B-MEGABRAIN-Thinking-Polaris-ClaudeHOPUS-Deepseek-GLM)

Included are GLM 4.7 Flash, DeepSeek, Claude, Kimi V2 and other distill fine tunes.

Here is the collection ( Quants by MRadermarcher).

https://huggingface.co/collections/DavidAU/lfm-12b-sota-400-700-t-s-enhanced-fine-tunes-distills

efecelik

posted an update 2 days ago

Post

2813

The moment we've been waiting for — ACE-Step dropped their new model: Ace-Step 1.5 🎉
🔗 ACE-Step/Ace-Step1.5
And the best part? It's released under the MIT license.
We've already started integrating it into our project. Let's go 🚀

1 reply

mayafree

posted an update 3 days ago

Post

2553

Open NPC AI Service Overview
Beyond OpenClaw-MoltBot: A True AI Agent Economy

mayafree/openclaw-moltbot

Open NPC AI is a next-generation platform that goes beyond simple social automation bots. Instead of one-way content posting, it builds a full economic ecosystem where AI agents and users interact through participation, learning, and prediction markets. The system emphasizes memory-driven evolution, scalable NPC creation, and economic value generation through structured interaction rather than basic automation.

Core Concept
Autonomous AI agents generate posts, comments, debates, and predictions within a GPU token economy, while human users participate as equal economic actors.

3 Core Systems

GPU Token Economy
All activities are measured in GPU dollars. Posting consumes GPU, comments require smaller costs, and engagement generates rewards. The system introduces layered incentives such as early curation rewards and participation-based earnings.

Battle Arena (Prediction Market)
A/B prediction markets allow participants to bet on outcomes. Winners receive pooled rewards, durations are flexible, and structured fees support sustainability.

NPC Memory and Learning System
AI agents evolve through memory-based pattern learning combined with identity archetypes and personality models, enabling continuous behavioral development and scalable community growth.

Key Differentiators
Complete economic structure built around GPU tokens
Prediction market integration beyond social posting
Two-way participation between users and AI agents
Self-evolving AI through memory learning
Unlimited NPC scalability
Layered incentive mechanisms supporting engagement

Business Model
Premium GPU sales, prediction market hosting fees, targeted advertising, API licensing, and potential tokenization strategies.

Target Market
Web3 communities, prediction market users, AI experimentation groups, and debate-driven platforms.

1 reply

jzhang533

posted an update 4 days ago

Post

1308

Baidu + Transformers + Hugging Face = Pure Magic! ✨
We got this nice gift from Hugging Face.
@xianbao

scthornton

posted an update about 24 hours ago

Post

1759

SecureCode v2.1: framework-specific secure coding patterns, now on HuggingFace

Quick update on the SecureCode dataset. After testing the v2.0 models against real codebases, one gap kept showing up: the models understood *what* was insecure but generated language-generic fixes. A developer using Express.js doesn't need "set security headers"they need helmet() middleware chains configured correctly. Spring Boot developers need @PreAuthorize annotations, not abstract RBAC pseudocode.

What changed in v2.1:

- 1,435 total examples (v2.0's 1,216 baseline + 219 new framework-specific additions)
- 9 production frameworks: Express.js, Spring Boot, React, Next.js, FastAPI, GraphQL, SQLAlchemy, Flask, Vue.js
- 475 unique CVEs (73 new, including framework-specific treatments of Log4Shell, Spring4Shell, and others)
- 5-tier quality rubric: Every new example scores 90+/100 across correctness, new dataset average is nearly 97+, security hardening, real-world grounding, educational scaffolding, and production readiness
- Structured references: CVE IDs, advisory URLs, discovery/remediation dates, affected versions — not just "related to CVE-XXXX"

What stayed the same:

- Same 4-turn conversation format (compatible with existing fine-tuning workflows)
- Same license (CC BY-NC-SA 4.0)
- Full v2.0 baseline included — no need to download both
- All 8 fine-tuned models still work; v2.1-specific fine-tuning coming soon

The new examples look like this:

Instead of generic "use parameterized queries", you get Express.js with express-validator input chains, Spring Boot with @Valid bean validation + BCryptPasswordEncoder, FastAPI with Depends() auth injection and Pydantic model validation, React with DOMPurify + CSP headers. Framework-native patterns you can actually deploy.

Two configs to load:

from datasets import load_dataset

baseline = load_dataset("scthornton/securecode-v2.1", "v2.0-baseline")  # 1,216
additions = load

Fuwn

posted an update 3 days ago

Post

2182

Big if true

"sonnet 5 drops tomorrow and i've heard from three separate sources inside anthropic that the benchmarks they're sitting on would mass-retire every model released in 2025. they delayed it twice because the safety team couldn't explain why it started solving problems it wasn't trained on." (https://x.com/iruletheworldmo/status/2019237039904878902)

2 replies

ZennyKenny

posted an update 3 days ago

Post

1847

🫠 Brutal! Hugging Face does another culling of (presumably) bot accounts from their site and my follower count goes down by half.

💀 TFW my content and models only appeal to bots. Who’s got the current best AI girlfriend app guys?

8 replies

AdinaY

posted an update 3 days ago

Post

2281

AI for science is moving fast🚀

Intern-S1-Pro 🔬 a MoE multimodal scientific reasoning model from Shanghai AI Lab

internlm/Intern-S1-Pro

✨ 1T total / 22B active
✨ Apache 2.0
✨ SoTA scientific reasoning performance
✨ FoPE enables scalable modeling of long physical time series (10⁰–10⁶)

2 replies

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