Instructions to use sarvamai/sarvam-m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sarvamai/sarvam-m with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sarvamai/sarvam-m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sarvamai/sarvam-m")
model = AutoModelForCausalLM.from_pretrained("sarvamai/sarvam-m", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sarvamai/sarvam-m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sarvamai/sarvam-m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sarvamai/sarvam-m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sarvamai/sarvam-m

SGLang

How to use sarvamai/sarvam-m with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sarvamai/sarvam-m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sarvamai/sarvam-m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sarvamai/sarvam-m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sarvamai/sarvam-m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sarvamai/sarvam-m with Docker Model Runner:
```
docker model run hf.co/sarvamai/sarvam-m
```

sarvam-m / README.md

mohit-sarvam

Update README.md

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metadata

library_name: transformers
license: apache-2.0
language:
  - en
  - bn
  - hi
  - kn
  - gu
  - mr
  - ml
  - or
  - pa
  - ta
  - te
base_model:
  - mistralai/Mistral-Small-3.1-24B-Instruct-2503

Model Information

Sarvam-M multilingual hybrid reasoning llm is an instruction tuned generative model in 24B (text in/text out) post trained over Mistral 3.1 24B. It significantly improves on the base Mistral model: +20% average improvement on Indian language benchmarks, +21.6% on math benchmarks, and +17.6% on programming benchmarks. The gains in tasks in the intersectionality of Indian languages and math are even higher, e.g., +86% improvement in a romanized Indian language GSM-8K benchmark.

Learn in detail about sarvam-M in our blog post

Key Features

Hybrid thinking mode A single model supports both "think" and "non-think" modes. Use the think mode for tasks requiring complex logical reasoning, math, and coding, and switch to the non-think mode for efficient, general-purpose conversation.
Indic Skills Specifically post-trained on Indian languages alongside English, the model also embodies a character that reflects and emphasizes Indian cultural values.
Reasoning capabilities Sarvam-M outperforms most models of similar size on coding and math benchmarks, demonstrating strong reasoning capabilities.
Chatting Experience With support for both Indic scripts and romanized versions of Indian languages, Sarvam-M offers a smooth and accessible multilingual chat experience.

Quickstart

The following contains a code snippet illustrating how to use the model generate content based on given inputs.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "sarvamai/sarvam-M"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, torch_dtype="auto", device_map="auto"
)

# prepare the model input
prompt = "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 completion
generated_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)

VLLM Deployment

For deployment, you can use vllm>=0.8.5 to create an OpenAI-compatible API endpoint:

vllm serve sarvamai/sarvam-M

For inference and switching between thinking and non-thinking mode, refer to the below python code:

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": "How many letter r in word strawberry?"}]

# By default, the model is in thinking mode.
# 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").rstrip("</s>")
else:
    reasoning_content = ""
    content = output_text.rstrip("</s>")

print("reasoning content:", reasoning_content)
print("content:", content)

# For the next round, add the assistant's response and reasoning to the messages.
messages.append(
    {"role": "assistant", "content": content, "reasoning_content": reasoning_content}
)

The above example also shows how to add assistant turns in the messages for multiturn conversation.