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Lokesh

Lokesh-CODER

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new activity about 5 hours ago

Intel/gemma-4-31B-it-int4-AutoRound:Whats the min ram and cpu to run this?

repliedto Abhaykoul's post 10 months ago

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) ```

reacted to Abhaykoul's post with 🔥 10 months ago

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) ```

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