sarvamai
/

sarvam-m

@@ -17,197 +17,45 @@ base_model:
 - mistralai/Mistral-Small-3.1-24B-Instruct-2503
 ---
-# Model Card for Model ID
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-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 - 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.
+```
+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:
+    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("thinking content:", thinking_content)
+print("content:", content)
+```