GenVRadmin/AryaBhatta-GemmaOrca

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. To improve the resoning and maths skills, we first SFT tune the gemma on Microsoft's Orca datasets.

We utilize Orca maths Hindi dataset: GenVRadmin/Aryabhatta-Orca-Maths-Hindi
And original Orca maths dataset: microsoft/orca-math-word-problems-200k

This pushes the MATHS score from 24.3 in Gemma-7B to 25.5 in Zephyr-Gemma and 31.6 in GemmaOrca.

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).

This is then finetuned on various open sourced datasets like:

Telugu-LLM-Labs/yahma_alpaca_cleaned_telugu_filtered_and_romanized
Telugu-LLM-Labs/teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized
abhinand/tamil-alpaca
Tensoic/airoboros-3.2_kn
Tensoic/gpt-teacher_kn
Tensoic/Alpaca-Gujarati
HydraIndicLM/bengali_alpaca_dolly_67k
Open-Orca/OpenOrca
pankajmathur/alpaca_orca
OdiaGenAI/Odia_Alpaca_instructions_52k
OdiaGenAI/gpt-teacher-roleplay-odia-3k
GenVRadmin/Samvaad-Punjabi-Mini
pankajmathur/WizardLM_Orca

The model achieves following scores on benchmarks:

Model AGIEval GPT4All TruthfulQA BigBench Average ⬇️
AryaBhatta-GemmaOrca 35.9 72.26 53.85 40.35 50.59
zephyr-7b-beta 37.52 71.77 55.26 39.77 51.08
zephyr-7b-gemma-v0.1 34.22 66.37 52.19 37.10 47.47
mlabonne/Gemmalpaca-7B 21.6 40.87 44.85 30.49 34.45
google/gemma-7b-it 21.33 40.84 41.70 30.25 33.53

How to use:-

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

model = AutoPeftModelForCausalLM.from_pretrained(
    "GenVRadmin/AryaBhatta-GemmaOrca",
    load_in_4bit = False,
    token = hf_token
)
tokenizer = AutoTokenizer.from_pretrained("GenVRadmin/AryaBhatta-GemmaOrca")

input_prompt = """
### Instruction:
{}

### Input:
{}

### Response:
{}"""

input_text = input_prompt.format(
        "Answer this question about India.", # instruction
        "Who is the Prime Minister of India", # 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]