Update README.md

README.md

@@ -50,7 +50,7 @@ max_seq_length = 2048
 dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
 load_in_4bit = False 
 model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "Telugu-LLM-Labs/gemma_7b_10_lang_finetuned",
+    model_name = "Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa",
     max_seq_length = max_seq_length,
     dtype = dtype,
     load_in_4bit = load_in_4bit,
@@ -87,11 +87,11 @@ from peft import AutoPeftModelForCausalLM
 from transformers import AutoTokenizer
 
 model = AutoPeftModelForCausalLM.from_pretrained(
-    "Telugu-LLM-Labs/Telugu-gemma-7b-finetuned-sft",
+    "Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa",
     load_in_4bit = False,
     token = hf_token
 )
-tokenizer = AutoTokenizer.from_pretrained("Telugu-LLM-Labs/Telugu-gemma-7b-finetuned-sft")
+tokenizer = AutoTokenizer.from_pretrained("Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa")
 
 input_prompt = """
 ### Instruction:
@@ -115,7 +115,7 @@ outputs = model.generate(**inputs, max_new_tokens = 300, use_cache = True)
 response = tokenizer.batch_decode(outputs)[0]
 ```
 
-Refer to the [blog post]() for sample examples.
+Refer to the [blog post](https://ravidesetty.medium.com/introducing-indic-gemma-7b-2b-instruction-tuned-model-on-9-indian-languages-navarasa-86bc81b4a282) for sample examples.
 
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