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:
+20% average improvement on Indian language benchmarks
+21.6% enhancement on math benchmarks
+17.6% boost on programming benchmarks
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.
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)
Note. For thinking mode, we recommend temperature=0.5; for no-think mode, temperature=0.2.
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.
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}
)
Files
19 files · 47.2 GBWeights 47.1 GB (safetensors). Sizes and sha256 from the signed manifest.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("sarvamai/sarvam-m", revision="01534a53c46f2788e392dbb3d994e0fa8f04d3fd")
model = AutoModelForCausalLM.from_pretrained("sarvamai/sarvam-m", revision="01534a53c46f2788e392dbb3d994e0fa8f04d3fd", device_map="auto")
