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Open language models for South-East Asia

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gabrielchua authored a paper about 22 hours ago

RabakBench: Scaling Human Annotations to Construct Localized Multilingual Safety Benchmarks for Low-Resource Languages

SivilTaram authored a paper 5 days ago

ZeCO: Zero Communication Overhead Sequence Parallelism for Linear Attention

koalazf99 authored a paper 13 days ago

OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling

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gabrielchua

authored a paper about 22 hours ago

RabakBench: Scaling Human Annotations to Construct Localized Multilingual Safety Benchmarks for Low-Resource Languages

Paper • 2507.05980 • Published 1 day ago

SivilTaram

authored a paper 5 days ago

ZeCO: Zero Communication Overhead Sequence Parallelism for Linear Attention

Paper • 2507.01004 • Published 9 days ago • 10

Abhaykoul

posted an update 9 days ago

Post

2828

🎉 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

dreamerdeo

updated a Space 14 days ago

README

💻

Abhaykoul

posted an update 15 days ago

Post

4194

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)