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
```

mohit-sarvam commited on May 20, 2025

Commit

0b12a31

verified ·

1 Parent(s): b9fae8f

Update README.md

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README.md +10 -1

README.md CHANGED Viewed

@@ -17,6 +17,15 @@ base_model:
 - mistralai/Mistral-Small-3.1-24B-Instruct-2503
 ---
 ## Quickstart
 The following contains a code snippet illustrating how to use the model generate content based on given inputs.
@@ -105,4 +114,4 @@ print("content:", content)
 messages.append(
     {"role": "assistant", "content": content, "reasoning_content": reasoning_content}
 )
-```

 - mistralai/Mistral-Small-3.1-24B-Instruct-2503
 ---
+## Model Information
+Sarvam-M multilingual 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.
+## Key Features
+Learn more about sarvam-M in our [blog post](link)
 ## Quickstart
 The following contains a code snippet illustrating how to use the model generate content based on given inputs.
 messages.append(
     {"role": "assistant", "content": content, "reasoning_content": reasoning_content}
 )
+```