Upload README.md with huggingface_hub

README.md

@@ -1,67 +1,3 @@
----
-license: cc-by-4.0
----
-
-Sample Code
-```
-import requests
-import torch
-import os
-from tqdm import tqdm
-from transformers import AutoProcessor, Gemma3ForConditionalGeneration, AutoTokenizer
-
-
-model_id = "ai4bharat/IndicTrans3-gemma-beta"
-language = "Hindi"
-# permitted languages = Assamese, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Odia, Punjabi, Sanskrit, Tamil, Telugu, Urdu
-
-model = Gemma3ForConditionalGeneration.from_pretrained(
-    model_id, device_map="auto", attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16
-).eval()
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-src = [
-    "When I was young, I used to go to the park every day.",
-    "We watched a new movie last week, which was very inspiring.",
-    "If you had met me at that time, we would have gone out to eat.",
-    "My friend has invited me to his birthday party, and I will give him a gift."
-]
-
-_PROMPT = (
-    "<bos><start_of_turn>user\n"
-    "Translate the following text to {tgt_lang}: {source_text}:"
-    "<end_of_turn>\n<start_of_turn>model\n"
-)
-
-batch_size = 100  # Adjust based on memory constraints
-outputs = []
-
-for i in tqdm(range(0, len(src), batch_size)):
-    batch = src[i:i + batch_size]
-
-    batch = [
-        _PROMPT.format(
-            tgt_lang=language,
-            source_text=s
-        )
-        for s in batch
-    ]
-    tokinp = tokenizer(batch, return_tensors='pt', padding="longest")
-    for k in tokinp:
-        tokinp[k] = tokinp[k].to("cuda")
-
-    out = model.generate(
-        **tokinp,
-        max_new_tokens=8192,
-        num_beams=1,
-        do_sample=False
-    )
-
-    for b, o in zip(batch, out):
-        input_length = len(tokenizer(b)['input_ids'])
-        finout = tokenizer.decode(o, skip_special_tokens=True)
-        outputs.append(finout.split('model')[-1].strip())
-
-print(outputs)
-```
+---
+license: cc-by-4.0
+---