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Model: Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 Source: Original Platform
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README.md
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---
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license: other
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license_name: gemma-terms-of-use
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license_link: https://ai.google.dev/gemma/terms
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base_model: google/gemma-2b
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datasets:
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- ravithejads/samvaad-hi-filtered
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- Telugu-LLM-Labs/telugu_teknium_GPTeacher_general_instruct_filtered_romanized
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- Telugu-LLM-Labs/telugu_alpaca_yahma_cleaned_filtered_romanized
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- Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered
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- Telugu-LLM-Labs/urdu_alpaca_yahma_cleaned_filtered
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- Telugu-LLM-Labs/marathi_alpaca_yahma_cleaned_filtered
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- Telugu-LLM-Labs/assamese_alpaca_yahma_cleaned_filtered
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- Telugu-LLM-Labs/konkani_alpaca_yahma_cleaned_filtered
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- Telugu-LLM-Labs/nepali_alpaca_yahma_cleaned_filtered
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- abhinand/tamil-alpaca
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- Tensoic/airoboros-3.2_kn
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- Tensoic/gpt-teacher_kn
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- VishnuPJ/Alpaca_Instruct_Malayalam
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- Tensoic/Alpaca-Gujarati
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- HydraIndicLM/punjabi_alpaca_52K
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- HydraIndicLM/bengali_alpaca_dolly_67k
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- OdiaGenAI/Odia_Alpaca_instructions_52k
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- yahma/alpaca-cleaned
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language:
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- te
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- en
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- ta
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- ml
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- mr
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- hi
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- kn
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- sd
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- ne
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- ur
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- as
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- gu
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- bn
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- pa
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- or
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Indic-gemma-2b-finetuned-sft-Navarasa-2.0
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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:
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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)
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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)
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3. #### Marathi - [Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered](https://huggingface.co/datasets/Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered)
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4. #### Urdu - [Telugu-LLM-Labs/urdu_alpaca_yahma_cleaned_filtered](https://huggingface.co/datasets/Telugu-LLM-Labs/urdu_alpaca_yahma_cleaned_filtered)
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5. #### Assamese - [Telugu-LLM-Labs/assamese_alpaca_yahma_cleaned_filtered](https://huggingface.co/datasets/Telugu-LLM-Labs/assamese_alpaca_yahma_cleaned_filtered)
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6. #### Konkani - [Telugu-LLM-Labs/konkani_alpaca_yahma_cleaned_filtered](https://huggingface.co/datasets/Telugu-LLM-Labs/konkani_alpaca_yahma_cleaned_filtered)
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7. #### Nepali - [Telugu-LLM-Labs/nepali_alpaca_yahma_cleaned_filtered](https://huggingface.co/datasets/Telugu-LLM-Labs/nepali_alpaca_yahma_cleaned_filtered)
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8. #### Sindhi - [Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered](https://huggingface.co/datasets/Telugu-LLM-Labs/sindhi_alpaca_yahma_cleaned_filtered)
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9. #### Tamil - [abhinand/tamil-alpaca](https://huggingface.co/datasets/abhinand/tamil-alpaca)
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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)
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11. #### Malayalam - [VishnuPJ/Alpaca_Instruct_Malayalam](https://huggingface.co/datasets/VishnuPJ/Alpaca_Instruct_Malayalam)
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12. #### Gujarati - [Tensoic/Alpaca-Gujarati](https://huggingface.co/datasets/Tensoic/Alpaca-Gujarati)
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13. #### Punjabi - [HydraIndicLM/punjabi_alpaca_52K](https://huggingface.co/datasets/HydraIndicLM/punjabi_alpaca_52K)
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14. #### Bengali - [HydraIndicLM/bengali_alpaca_dolly_67k](https://huggingface.co/datasets/HydraIndicLM/bengali_alpaca_dolly_67k)(alpaca filtered)
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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)
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16. #### English - [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned)
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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.
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# Training Details:
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The model is trained on approx 650K instruction samples.
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1. GPU: 1 A100, 80GB
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2. Time: 45 Hours
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3. Platform: [E2E Networks](https://www.e2enetworks.com/)
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# Installation
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`!pip install -U xformers --index-url https://download.pytorch.org/whl/cu121`
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`!pip install "unsloth[kaggle-new] @git+https://github.com/unslothai/unsloth.git@nightly"`
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# Input Text Format
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```
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### Instruction: {instruction}
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### Input: {input}
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## Response: {response}
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```
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# Inference With Unsloth
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```python3
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from unsloth import FastLanguageModel
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import torch
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max_seq_length = 2048
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dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
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load_in_4bit = False
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0",
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max_seq_length = max_seq_length,
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dtype = dtype,
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load_in_4bit = load_in_4bit,
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device_map="auto"
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)
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference
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input_prompt = """
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### Instruction:
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{}
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### Input:
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{}
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### Response:
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{}"""
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input_text = input_prompt.format(
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"Tranlsate following sentence to Hindi.", # instruction
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"India is a great country.", # input
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"", # output - leave this blank for generation!
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)
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inputs = tokenizer([input_text], return_tensors = "pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens = 300, use_cache = True)
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response = tokenizer.batch_decode(outputs)
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```
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# Inference with HuggingFace
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```python3
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0",
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load_in_4bit = False,
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token = hf_token
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)
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model.to("cuda")
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tokenizer = AutoTokenizer.from_pretrained("Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0")
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input_prompt = """
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### Instruction:
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{}
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### Input:
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{}
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### Response:
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{}"""
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input_text = input_prompt.format(
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"Tranlsate following sentence to Hindi.", # instruction
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"India is a great country.", # input
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"", # output - leave this blank for generation!
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)
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inputs = tokenizer([input_text], return_tensors = "pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens = 300, use_cache = True)
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response = tokenizer.batch_decode(outputs)[0]
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```
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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.
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Please check our [Code Repository](https://github.com/TeluguLLMLabs/Indic-gemma-7b-Navarasa) for training and inference scripts.
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# Developers:
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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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