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sarvamai
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Model Details
Model Provider
sarvamai
Model Tree
mistralai/Mistral-Small-3.1-24B-Base-2503
Input Modalities
Output Modalities
Supported Functionality
sarvam-m is a multilingual, hybrid-reasoning, text-only language model built on Mistral-Small. This post-trained version delivers exceptional improvements over the base model:
Performance gains are even more impressive at the intersection of Indian languages and mathematics, with an outstanding +86% improvement in romanized Indian language GSM-8K benchmarks.
Learn more about sarvam-m in our detailed blog post.
Hybrid Thinking Mode: A single versatile model supporting both "think" and "non-think" modes. Use the think mode for complex logical reasoning, mathematical problems, and coding tasks, or switch to non-think mode for efficient, general-purpose conversation.
Advanced Indic Skills: Specifically post-trained on Indian languages alongside English, embodying a character that authentically reflects and emphasizes Indian cultural values.
Superior Reasoning Capabilities: Outperforms most similarly-sized models on coding and math benchmarks, demonstrating exceptional reasoning abilities.
Seamless Chatting Experience: Full support for both Indic scripts and romanized versions of Indian languages, providing a smooth and accessible multilingual conversation experience.
The following code snippet demonstrates how to use sarvam-m using Transformers.
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "sarvamai/sarvam-m" # load the tokenizer and the modeltokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto") # prepare the model inputprompt = "Who are you and what is your purpose on this planet?" messages = [{"role": "user", "content": prompt}]text = tokenizer.apply_chat_template( messages, tokenize=False, enable_thinking=True, # Switches between thinking and non-thinking modes. Default is True.) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completiongenerated_ids = model.generate(**model_inputs, max_new_tokens=8192)output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :].tolist()output_text = tokenizer.decode(output_ids) if "</think>" in output_text: reasoning_content = output_text.split("</think>")[0].rstrip("\n") content = output_text.split("</think>")[-1].lstrip("\n").rstrip("</s>")else: reasoning_content = "" content = output_text.rstrip("</s>") print("reasoning content:", reasoning_content)print("content:", content)[!NOTE] For thinking mode, we recommend
temperature=0.5; for no-think mode,temperature=0.2.
python
from openai import OpenAI base_url = "https://api.sarvam.ai/v1"model_name = "sarvam-m"api_key = "Your-API-Key" # get it from https://dashboard.sarvam.ai/ client = OpenAI( base_url=base_url, api_key=api_key,).with_options(max_retries=1) messages = [ {"role": "system", "content": "You're a helpful AI assistant"}, {"role": "user", "content": "Explain quantum computing in simple terms"},] response1 = client.chat.completions.create( model=model_name, messages=messages, reasoning_effort="medium", # Enable thinking mode. `None` for disable. max_completion_tokens=4096,)print("First response:", response1.choices[0].message.content) # Building messages for the second turn (using previous response as context)messages.extend( [ { "role": "assistant", "content": response1.choices[0].message.content, }, {"role": "user", "content": "Can you give an analogy for superposition?"}, ]) response2 = client.chat.completions.create( model=model_name, messages=messages, reasoning_effort="medium", max_completion_tokens=8192,)print("Follow-up response:", response2.choices[0].message.content)Refer to API docs here: sarvam Chat Completions API docs
reasoning_effort can take three possible values: low, medium, and high to be consistent with the OpenAI API spec. Setting any of the three values just enables the thinking mode of sarvam-m.
For easy deployment, we can use vllm>=0.8.5 and create an OpenAI-compatible API endpoint with vllm serve sarvamai/sarvam-m.
If you want to use vLLM with python, you can do the following.
python
from openai import OpenAI # Modify OpenAI's API key and API base to use vLLM's API server.openai_api_key = "EMPTY"openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base,) models = client.models.list()model = models.data[0].id messages = [{"role": "user", "content": "Why is 42 the best number?"}] # By default, thinking mode is enabled.# If you want to disable thinking, add:# extra_body={"chat_template_kwargs": {"enable_thinking": False}}response = client.chat.completions.create(model=model, messages=messages)output_text = response.choices[0].message.content if "</think>" in output_text: reasoning_content = output_text.split("</think>")[0].rstrip("\n") content = output_text.split("</think>")[-1].lstrip("\n")else: reasoning_content = "" content = output_text print("reasoning content:", reasoning_content)print("content:", content) # For the next round, add the model's response directly as assistant turn.messages.append( {"role": "assistant", "content": output_text})from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "sarvamai/sarvam-m" # load the tokenizer and the modeltokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto") # prepare the model inputprompt = "Who are you and what is your purpose on this planet?" messages = [{"role": "user", "content": prompt}]text = tokenizer.apply_chat_template( messages, tokenize=False, enable_thinking=True, # Switches between thinking and non-thinking modes. Default is True.) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completiongenerated_ids = model.generate(**model_inputs, max_new_tokens=8192)output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :].tolist()output_text = tokenizer.decode(output_ids) if "</think>" in output_text: reasoning_content = output_text.split("</think>")[0].rstrip("\n") content = output_text.split("</think>")[-1].lstrip("\n").rstrip("</s>")else: reasoning_content = "" content = output_text.rstrip("</s>") print("reasoning content:", reasoning_content)print("content:", content)from openai import OpenAI base_url = "https://api.sarvam.ai/v1"model_name = "sarvam-m"api_key = "Your-API-Key" # get it from https://dashboard.sarvam.ai/ client = OpenAI( base_url=base_url, api_key=api_key,).with_options(max_retries=1) messages = [ {"role": "system", "content": "You're a helpful AI assistant"}, {"role": "user", "content": "Explain quantum computing in simple terms"},] response1 = client.chat.completions.create( model=model_name, messages=messages, reasoning_effort="medium", # Enable thinking mode. `None` for disable. max_completion_tokens=4096,)print("First response:", response1.choices[0].message.content) # Building messages for the second turn (using previous response as context)messages.extend( [ { "role": "assistant", "content": response1.choices[0].message.content, }, {"role": "user", "content": "Can you give an analogy for superposition?"}, ]) response2 = client.chat.completions.create( model=model_name, messages=messages, reasoning_effort="medium", max_completion_tokens=8192,)print("Follow-up response:", response2.choices[0].message.content)from openai import OpenAI # Modify OpenAI's API key and API base to use vLLM's API server.openai_api_key = "EMPTY"openai_api_base = "http://localhost:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base,) models = client.models.list()model = models.data[0].id messages = [{"role": "user", "content": "Why is 42 the best number?"}] # By default, thinking mode is enabled.# If you want to disable thinking, add:# extra_body={"chat_template_kwargs": {"enable_thinking": False}}response = client.chat.completions.create(model=model, messages=messages)output_text = response.choices[0].message.content if "</think>" in output_text: reasoning_content = output_text.split("</think>")[0].rstrip("\n") content = output_text.split("</think>")[-1].lstrip("\n")else: reasoning_content = "" content = output_text print("reasoning content:", reasoning_content)print("content:", content) # For the next round, add the model's response directly as assistant turn.messages.append( {"role": "assistant", "content": output_text})