GenVRadmin
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AryaBhatta-GemmaOrca-Merged

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 ---
+license: mit
+---
+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.
+To improve the resoning and maths skills, we first SFT tune the gemma on Microsoft's Orca datasets.
+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
+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]
+```