81 lines
2.8 KiB
Markdown
81 lines
2.8 KiB
Markdown
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---
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license: mit
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---
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This model is a part of two model series, AryaBhatta-1 and AryaBhatta-2 and is finetuned from HuggingFaceH4/zephyr-7b-gemma-v0.1 or Google/gemma and is finetuned on 9 Indian languages (Hindi, Tamil, Punjabi, Bengali, Gujarati, Oriya, Telugu, Kannada, Malayalam) plus English.
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There are two models. One finetuned on Google's Gemma and one fine-tuned on Zephyr's Gemma base. Repo for other one (Zephyr one): GenVRadmin/AryaBhatta-GemmaOrca-2-Merged
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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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```
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