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README.md

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+---
+license: other
+license_name: gemma-terms-of-use
+license_link: https://ai.google.dev/gemma/terms
+base_model: google/gemma-2b
+datasets:
+- ravithejads/samvaad-hi-filtered
+- Telugu-LLM-Labs/telugu_teknium_GPTeacher_general_instruct_filtered_romanized
+- Telugu-LLM-Labs/telugu_alpaca_yahma_cleaned_filtered_romanized
+- Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered
+- Telugu-LLM-Labs/urdu_alpaca_yahma_cleaned_filtered
+- Telugu-LLM-Labs/marathi_alpaca_yahma_cleaned_filtered
+- Telugu-LLM-Labs/assamese_alpaca_yahma_cleaned_filtered
+- Telugu-LLM-Labs/konkani_alpaca_yahma_cleaned_filtered
+- Telugu-LLM-Labs/nepali_alpaca_yahma_cleaned_filtered
+- abhinand/tamil-alpaca
+- Tensoic/airoboros-3.2_kn
+- Tensoic/gpt-teacher_kn
+- VishnuPJ/Alpaca_Instruct_Malayalam
+- Tensoic/Alpaca-Gujarati
+- HydraIndicLM/punjabi_alpaca_52K
+- HydraIndicLM/bengali_alpaca_dolly_67k
+- OdiaGenAI/Odia_Alpaca_instructions_52k
+- yahma/alpaca-cleaned
+language:
+- te
+- en
+- ta
+- ml
+- mr
+- hi
+- kn
+- sd
+- ne
+- ur
+- as
+- gu
+- bn
+- pa
+- or
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Indic-gemma-2b-finetuned-sft-Navarasa-2.0
+
+This model is based on [google/gemma-2b](https://huggingface.co/google/gemma-2b) and hase been LoRA finetuned on 15 Indian languages and English language instruction datasets:
+
+1. #### Hindi - [ravithejads/samvaad-hi-filtered](https://huggingface.co/datasets/ravithejads/samvaad-hi-filtered), [HydraIndicLM/hindi_alpaca_dolly_67k](https://huggingface.co/datasets/HydraIndicLM/hindi_alpaca_dolly_67k)(sampled)
+2. #### Telugu - [Telugu-LLM-Labs/telugu_alpaca_yahma_cleaned_filtered_romanized](https://huggingface.co/datasets/Telugu-LLM-Labs/telugu_alpaca_yahma_cleaned_filtered_romanized), [Telugu-LLM-Labs/telugu_teknium_GPTeacher_general_instruct_filtered_romanized](https://huggingface.co/datasets/Telugu-LLM-Labs/telugu_teknium_GPTeacher_general_instruct_filtered_romanized)
+3. #### Marathi - [Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered](https://huggingface.co/datasets/Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered)
+4. #### Urdu - [Telugu-LLM-Labs/urdu_alpaca_yahma_cleaned_filtered](https://huggingface.co/datasets/Telugu-LLM-Labs/urdu_alpaca_yahma_cleaned_filtered)
+5. #### Assamese - [Telugu-LLM-Labs/assamese_alpaca_yahma_cleaned_filtered](https://huggingface.co/datasets/Telugu-LLM-Labs/assamese_alpaca_yahma_cleaned_filtered)
+6. #### Konkani - [Telugu-LLM-Labs/konkani_alpaca_yahma_cleaned_filtered](https://huggingface.co/datasets/Telugu-LLM-Labs/konkani_alpaca_yahma_cleaned_filtered)
+7. #### Nepali - [Telugu-LLM-Labs/nepali_alpaca_yahma_cleaned_filtered](https://huggingface.co/datasets/Telugu-LLM-Labs/nepali_alpaca_yahma_cleaned_filtered)
+8. #### Sindhi - [Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered](https://huggingface.co/datasets/Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered)
+9. #### Tamil - [abhinand/tamil-alpaca](https://huggingface.co/datasets/abhinand/tamil-alpaca)
+10. #### Kannada - [Tensoic/airoboros-3.2_kn](https://huggingface.co/datasets/Tensoic/airoboros-3.2_kn), [Tensoic/gpt-teacher_kn](https://huggingface.co/datasets/Tensoic/gpt-teacher_kn)
+11. #### Malayalam - [VishnuPJ/Alpaca_Instruct_Malayalam](https://huggingface.co/datasets/VishnuPJ/Alpaca_Instruct_Malayalam)
+12. #### Gujarati - [Tensoic/Alpaca-Gujarati](https://huggingface.co/datasets/Tensoic/Alpaca-Gujarati)
+13. #### Punjabi - [HydraIndicLM/punjabi_alpaca_52K](https://huggingface.co/datasets/HydraIndicLM/punjabi_alpaca_52K)
+14. #### Bengali - [HydraIndicLM/bengali_alpaca_dolly_67k](https://huggingface.co/datasets/HydraIndicLM/bengali_alpaca_dolly_67k)(alpaca filtered)
+15. #### Odia - [OdiaGenAI/Odia_Alpaca_instructions_52k](https://huggingface.co/datasets/OdiaGenAI/Odia_Alpaca_instructions_52k), [OdiaGenAI/gpt-teacher-roleplay-odia-3k](https://huggingface.co/datasets/OdiaGenAI/gpt-teacher-roleplay-odia-3k)
+16. #### English - [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned)
+
+The model is finetuned using [unsloth](https://github.com/unslothai/unsloth) library and we provide inference code using the same for faster inference. Alternatively you can use HuggingFace Library for inference.
+
+# Training Details:
+
+The model is trained on approx 650K instruction samples.
+1. GPU: 1 A100, 80GB
+2. Time: 45 Hours
+3. Platform: [E2E Networks](https://www.e2enetworks.com/)
+# Installation
+
+`!pip install -U xformers --index-url https://download.pytorch.org/whl/cu121`
+`!pip install "unsloth[kaggle-new] @git+https://github.com/unslothai/unsloth.git@nightly"`
+
+# Input Text Format
+
+```
+### Instruction: {instruction}
+
+### Input: {input}
+
+## Response: {response}
+```
+
+# Inference With Unsloth
+
+```python3
+from unsloth import FastLanguageModel
+import torch
+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/Indic-gemma-2b-finetuned-sft-Navarasa-2.0",
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+    device_map="auto"
+)
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+
+input_prompt = """
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}"""
+
+input_text = input_prompt.format(
+        "Tranlsate following sentence to Hindi.", # instruction
+        "India is a great country.", # input
+        "", # output - leave this blank for generation!
+    )
+
+inputs = tokenizer([input_text], return_tensors = "pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens = 300, use_cache = True)
+response = tokenizer.batch_decode(outputs)
+```
+
+# Inference with HuggingFace
+
+```python3
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0",
+    load_in_4bit = False,
+    token = hf_token
+)
+model.to("cuda")
+
+tokenizer = AutoTokenizer.from_pretrained("Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0")
+
+input_prompt = """
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}"""
+
+input_text = input_prompt.format(
+        "Tranlsate following sentence to Hindi.", # instruction
+        "India is a great country.", # input
+        "", # output - leave this blank for generation!
+    )
+
+inputs = tokenizer([input_text], return_tensors = "pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens = 300, use_cache = True)
+response = tokenizer.batch_decode(outputs)[0]
+```
+
+Refer to the [blog post](https://ravidesetty.medium.com/introducing-navarasa-2-0-indic-gemma-7b-2b-instruction-tuned-model-on-15-indian-languages-31f6565b2750) for sample examples.
+
+Please check our [Code Repository](https://github.com/TeluguLLMLabs/Indic-gemma-7b-Navarasa) for training and inference scripts.
+
+
+# Developers:
+
+The model is a collaborative effort by [Ravi Theja](https://twitter.com/ravithejads) and [Ramsri Goutham](https://twitter.com/ramsri_goutham). Feel free to DM either of us if you have any questions.

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