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

1eb7a52

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1 Parent(s): 16e4c05

Update README.md

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README.md CHANGED Viewed

@@ -104,13 +104,13 @@ response = client.chat.completions.create(model=model, messages=messages)
 output_text = response.choices[0].message.content
 if "</think>" in output_text:
-    thinking_content = output_text.split("</think>")[0].rstrip("\n")
     content = output_text.split("</think>")[-1].lstrip("\n").rstrip("</s>")
 else:
-    thinking_content = ""
     content = output_text.rstrip("</s>")
-print("reasoning_content:", thinking_content)
 print("content:", content)
 # For the next round, add the assistant's response and reasoning to the 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.