Sarvam's New Open Source Models Match GPT-OSS-120B and One Only Uses 2.4B Active Parameters Sarvam built two models for two very different jobs. The 30B is a deployment model. It was designed to run fast, stay cheap, and handle real-time interactions without breaking a sweat. If you need an ...

Most #opensource model releases follow a predictable pattern. A lab drops weights, publishes benchmark numbers, and the community spends the next week figuring out if any of it holds up in real use. Sarvam’s 30B and 105B both are already in production. #sarvam #ai

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Introducing Indus by Sarvam: India's own AI chat app revolutionizing digital conversations. Experience AI tailored for you. #IndusAI #Sarvam #AIIndia #TechInnovation Link: thedailytechfeed.com/sarvam-unvei...

Sarvam Launches Indus: India's New AI Chat App to Rival ChatGPT – Archyde %

Sarvam Launches Indus: India’s New AI Chat App to Rival ChatGPT Archyde Bengaluru-based startup Sarvam AI has entered the rapidly expanding generative AI market with the launch of Indus, its new ...

#Technology #chat #apps #Indus #app #Sarvam #Sarvam #105B #Sarvam #AI

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Sarvam Goes Big: New Models, Edge Devices, and Open Source Plans Indian AI lab Sarvam launched new large language models, speech systems, and edge AI plans at the India AI Impact Summit.

Sarvam unveiled new AI models at the India AI Impact Summit, including 30B and 105B systems, speech tools, and edge AI plans. Here's everything you need to know:

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How to Install and Run Sarvam – M Locally? Sarvam-M is a multilingual, hybrid-reasoning, text-only language model built on Mistral-Small. This advanced model offers exceptional improvements over its base counterpart, showcasing a +20% enhancement in Indian language benchmarks, a +21.6% improvement on math tasks, and a +17.6% boost in programming capabilities. Sarvam-M excels particularly in scenarios that involve Indian languages and mathematics, achieving impressive performance, including an +86% improvement on romanized Indian language GSM-8K benchmarks. With a hybrid thinking mode, Sarvam-M supports both complex reasoning tasks such as coding and mathematical problem-solving, as well as general-purpose conversation in a seamless manner. It is specifically designed to handle both Indic scripts and romanized versions, making it a versatile solution for multilingual environments, particularly for Indian users. The model’s superior reasoning abilities make it ideal for tasks requiring high accuracy in logical thinking, math, and coding. It can easily switch between “think” mode for complex tasks and “non-think” mode for efficient conversation. ### Step-by-Step Process to Install and Run Sarvam – M Locally For the purpose of this tutorial, we will use a GPU-powered Virtual Machine offered by NodeShift; however, you can replicate the same steps with any other cloud provider of your choice. NodeShift provides the most affordable Virtual Machines at a scale that meets GDPR, SOC2, and ISO27001 requirements. ### Step 1: Sign Up and Set Up a NodeShift Cloud Account Visit the NodeShift Platform and create an account. Once you’ve signed up, log into your account. Follow the account setup process and provide the necessary details and information. ### Step 2: Create a GPU Node (Virtual Machine) GPU Nodes are NodeShift’s GPU Virtual Machines, on-demand resources equipped with diverse GPUs ranging from H100s to A100s. These GPU-powered VMs provide enhanced environmental control, allowing configuration adjustments for GPUs, CPUs, RAM, and Storage based on specific requirements. Navigate to the menu on the left side. Select the GPU Nodes option, create a GPU Node in the Dashboard, click the Create GPU Node button, and create your first Virtual Machine deploy ### Step 3: Select a Model, Region, and Storage In the “GPU Nodes” tab, select a GPU Model and Storage according to your needs and the geographical region where you want to launch your model. We will use 1 x RTX A6000 GPU for this tutorial to achieve the fastest performance. However, you can choose a more affordable GPU with less VRAM if that better suits your requirements. ### Step 4: Select Authentication Method There are two authentication methods available: Password and SSH Key. SSH keys are a more secure option. To create them, please refer to our official documentation. ### Step 5: Choose an Image Next, you will need to choose an image for your Virtual Machine. We will deploy Sarvam – M on an NVIDIA Cuda Virtual Machine. This proprietary, closed-source parallel computing platform will allow you to install Sarvam – M on your GPU Node. After choosing the image, click the ‘Create’ button, and your Virtual Machine will be deployed. ### Step 6: Virtual Machine Successfully Deployed You will get visual confirmation that your node is up and running. ### Step 7: Connect to GPUs using SSH NodeShift GPUs can be connected to and controlled through a terminal using the SSH key provided during GPU creation. Once your GPU Node deployment is successfully created and has reached the ‘RUNNING’ status, you can navigate to the page of your GPU Deployment Instance. Then, click the ‘Connect’ button in the top right corner. Now open your terminal and paste the proxy SSH IP or direct SSH IP. Next, if you want to check the GPU details, run the command below: `nvidia-smi ` ### Step 8: Check the Available Python version and Install the new version Run the following commands to check the available Python version. If you check the version of the python, system has Python 3.8.1 available by default. To install a higher version of Python, you’ll need to use the deadsnakes PPA. Run the following commands to add the deadsnakes PPA: sudo apt update sudo apt install -y software-properties-common sudo add-apt-repository -y ppa:deadsnakes/ppa sudo apt update ### Step 9: Install Python 3.11 Now, run the following command to install Python 3.11 or another desired version: sudo apt install -y python3.11 python3.11-venv python3.11-dev ### Step 10: Update the Default Python3 Version and Activate the Environment Now, run the following command to link the new Python version as the default python3: sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1 sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 2 sudo update-alternatives --config python3 Then, run the following command to verify that the new Python version is active: `python3 --version ` Next, run the following command to activate the python environment: python3.10 -m venv sarvam-env source sarvam-env/bin/activate ### Step 11: Install and Update Pip Run the following command to install and update the pip: `curl -O https://bootstrap.pypa.io/get-pip.py python3.11 get-pip.py ` Then, run the following command to check the version of pip: `pip --version ` ### Step 12: Install Required Libraries Run the following command to install required libraries: `pip install torch transformers accelerate ` ### Step 13: Run directly inside the terminal (REPL) and Load the Model In your SSH terminal, just run Python interactively: `python ` Then, paste the following code in terminal to load the model: from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "sarvamai/sarvam-m" # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) ### Step 14: Run the Prompt Run the prompt on terminal and generate output: prompt = "भारत के पहले उपग्रह का नाम क्या है?" # Format chat messages = [{"role": "user", "content": prompt}] text = tokenizer.apply_chat_template( messages, tokenize=False, enable_thinking=True ) # Tokenize and move to GPU model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # Generate output generated_ids = model.generate(**model_inputs, max_new_tokens=1024) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() output_text = tokenizer.decode(output_ids) # Separate reasoning if present if "</think>" in output_text: reasoning_content = output_text.split("</think>")[0].strip() content = output_text.split("</think>")[-1].strip("</s>").strip() else: reasoning_content = "" content = output_text.strip("</s>") print("🧠 Reasoning:", reasoning_content) print("🗣️ Content:", content) ### Step-by-Step Process to Install and Run Sarvam – M in Browser using Gradio ### Step 1: Install Dependencies Run the following command to install dependencies: apt update && apt install nano -y pip install gradio ### Step 2: Create the Python3 File Run the following command to create the Python3 file: `nano sarvam_transformers_gradio.py ` After this, paste the following code in the nano editor: from transformers import AutoTokenizer, AutoModelForCausalLM import torch import gradio as gr # Load model and tokenizer model_name = "sarvamai/sarvam-m" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # Inference function def chat_with_sarvam(user_input): messages = [{"role": "user", "content": user_input}] prompt_text = tokenizer.apply_chat_template( messages, tokenize=False, enable_thinking=True ) inputs = tokenizer(prompt_text, return_tensors="pt").to(model.device) generated = model.generate(**inputs, max_new_tokens=1024) output_ids = generated[0][inputs["input_ids"].shape[1]:] output = tokenizer.decode(output_ids, skip_special_tokens=True) if "</think>" in output: reasoning = output.split("</think>")[0].strip() answer = output.split("</think>")[-1].strip("</s>").strip() else: reasoning = "" answer = output.strip("</s>").strip() return f"🧠 Reasoning:\n{reasoning}\n\n🗣️ Answer:\n{answer}" # Gradio UI gr.Interface( fn=chat_with_sarvam, inputs=gr.Textbox(lines=2, placeholder="Ask something..."), outputs="text", title="Sarvam-M Chat (Transformers)", description="Hybrid reasoning — math, code, culture — via Transformers." ).launch(server_name="0.0.0.0", server_port=7860) ### After Pasting: * Press Ctrl + O → Enter to save * Then Ctrl + X to exit ### Step 3: Run Gradio Web App Execute the following command to run the gradio web app: `python3 sarvam_transformers_gradio.py ` ### Step 4: Run SSH Port Forwarding Command to access the Gradio Web App Run the following command to access the Gradio web app (or any other port from your VM) on your local machine: `ssh -L 7860:localhost:7860 root@116.127.115.18 -p 26052 ` ### Step 5: Access the Gradio Web App Access the Gradio Web App on: Running on local URL: http://localhost:7860 ### Conclusion In this guide, we have successfully walked through the process of setting up and deploying the Sarvam-M model locally on a GPU-powered Virtual Machine. Sarvam-M stands out for its ability to seamlessly handle both multilingual tasks and advanced reasoning capabilities, making it ideal for a wide range of applications from complex mathematical problem-solving to general conversation. By following the steps outlined in this blog, you can easily integrate this powerful model into your own environment and start utilizing its hybrid reasoning modes for a range of tasks. Whether you’re running it for development, research, or practical applications, Sarvam-M’s capabilities will help enhance your productivity and problem-solving efficiency.

How to Install and Run Sarvam – M Locally? Sarvam-M is a multilingual, hybrid-reasoning, text-o...


#sarvam #ai #llm
Origin | Interest | Match

What is Sarvam-M and Why is it Facing Flak? Sarvam, IndiaAI Mission’s flagship AI startup, releases Sarvam-M LLM but faces criticism over low adoption and high expectations.

Sarvam, India’s chosen startup for building a sovereign LLM, released Sarvam-M. While it sets benchmarks in Indian languages, the reception has been polarising. Is India’s AI mission on track or missing the mark?

itmatterss.in/india/what-i...

#Sarvam #IndicLanguages #AIinIndia #IndiaAI

GitHub - CodeWithSushil/sarvam.ai: Sarvam.AI for India AI ecosystem. Sarvam.AI for India AI ecosystem. Contribute to CodeWithSushil/sarvam.ai development by creating an account on GitHub.

Sarvam AI for Indian AI ecosystem.
Sarvam AI have:
- LLM
- Text To Speech
- Speech To Text
- Speech Translate
- Transliterate
- Translate
- Indic Language support

model: sarvam-m

API support in 3 language
- Python
- JavaScript
- cURL

github.com/CodeWithSush...

#sarvam
#sarvamai
#javascript
#llm

Sarvam AI’s Backlash Exposes the Sad State of Indian AI Much of Sarvam’s criticism comes from...

analyticsindiamag.com/ai-features/sarvam-ais-b...

#AI #Features #Sarvam

Result Details

Sarvam AI Launches Sarvam-M, a 24B Open-Weights Model on Top of Mistral The model is focused on 1...

analyticsindiamag.com/ai-news-updates/sarvam-l...

#AI #News #Sarvam

Result Details

Sarvam AI Launches Bulbul, New Voice Model for Indian Market The model promises lightning-fast pe...

analyticsindiamag.com/ai-news-updates/sarvam-a...

#AI #News #sarvam #ai

Result Details

Sarvam AI Launches Bulbul, New Voice Model for Indian Market The model promises lightning-fast pe...

analyticsindiamag.com/ai-news-updates/sarvam-a...

#AI #News #sarvam #ai

Result Details

Sarvam AI Launches Bulbul, New Voice Model for Indian Market The model promises lightning-fast pe...

analyticsindiamag.com/ai-news-updates/sarvam-a...

#AI #News #sarvam #ai

Result Details

Bengaluru-Based Sarvam AI Selected Under IndiaAI Mission to Build India’s Sovereign LLM The com...

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#AI #Startups #sarvam #ai

Result Details