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--- |
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license: mit |
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--- |
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This model is finetuned from HuggingFaceH4/zephyr-7b-gemma-v0.1 and is finetuned on 9 Indian languages (Hindi, Tamil, Punjabi, Bengali, Gujarati, Oriya, Telugu, Kannada, Malayalam) plus English. |
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To improve the resoning and maths skills, we first SFT tune the gemma on Microsoft's Orca datasets. |
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We utilize Orca maths Hindi dataset: GenVRadmin/Aryabhatta-Orca-Maths-Hindi \ |
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And original Orca maths dataset: microsoft/orca-math-word-problems-200k |
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This pushes the MATHS score from 24.3 in Gemma-7B to 25.5 in Zephyr-Gemma and 31.6 in GemmaOrca. |
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The model is then finetuned on GenVR's Samvaad datasets (GenVRadmin/Samvaad-Indic-Positive and GenVRadmin/Samvaad-Tamil-Mixtral and a subset of GenVRadmin/Samvaad-Mixed-Language-3). |
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This is then finetuned on various open sourced datasets like: |
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Telugu-LLM-Labs/yahma_alpaca_cleaned_telugu_filtered_and_romanized \ |
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Telugu-LLM-Labs/teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized \ |
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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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Tensoic/Alpaca-Gujarati \ |
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HydraIndicLM/bengali_alpaca_dolly_67k \ |
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Open-Orca/OpenOrca \ |
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pankajmathur/alpaca_orca \ |
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OdiaGenAI/Odia_Alpaca_instructions_52k \ |
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OdiaGenAI/gpt-teacher-roleplay-odia-3k \ |
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GenVRadmin/Samvaad-Punjabi-Mini \ |
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pankajmathur/WizardLM_Orca |
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The model achieves following scores on benchmarks: |
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Model AGIEval GPT4All TruthfulQA BigBench Average ⬇️ \ |
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AryaBhatta-GemmaOrca 35.9 72.26 53.85 40.35 50.59 \ |
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zephyr-7b-beta 37.52 71.77 55.26 39.77 51.08 \ |
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zephyr-7b-gemma-v0.1 34.22 66.37 52.19 37.10 47.47 \ |
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mlabonne/Gemmalpaca-7B 21.6 40.87 44.85 30.49 34.45 \ |
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google/gemma-7b-it 21.33 40.84 41.70 30.25 33.53 |
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How to use:- |
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``` |
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from peft import AutoPeftModelForCausalLM |
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from transformers import AutoTokenizer |
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model = AutoPeftModelForCausalLM.from_pretrained( |
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"GenVRadmin/AryaBhatta-GemmaOrca", |
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load_in_4bit = False, |
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token = hf_token |
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) |
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tokenizer = AutoTokenizer.from_pretrained("GenVRadmin/AryaBhatta-GemmaOrca") |
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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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"Answer this question about India.", # instruction |
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"Who is the Prime Minister of India", # 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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``` |
