Prashantmdgl9
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Indic_small_5_lang

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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.


1	+ license: other