yuj-v1 / README.md

Adding Evaluation Results (#1)

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	---
	license: apache-2.0
	tags:
	- merge
	- hindi
	- english
	- Llama2
	- ai4bharat/Airavata
	- BhabhaAI/Gajendra-v0.1
	model-index:
	- name: yuj-v1
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: AI2 Reasoning Challenge (25-Shot)
	type: ai2_arc
	config: ARC-Challenge
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: acc_norm
	value: 45.65
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shuvom/yuj-v1
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: HellaSwag (10-Shot)
	type: hellaswag
	split: validation
	args:
	num_few_shot: 10
	metrics:
	- type: acc_norm
	value: 70.1
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shuvom/yuj-v1
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU (5-Shot)
	type: cais/mmlu
	config: all
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 43.78
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shuvom/yuj-v1
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: TruthfulQA (0-shot)
	type: truthful_qa
	config: multiple_choice
	split: validation
	args:
	num_few_shot: 0
	metrics:
	- type: mc2
	value: 41.69
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shuvom/yuj-v1
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: Winogrande (5-shot)
	type: winogrande
	config: winogrande_xl
	split: validation
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 69.85
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shuvom/yuj-v1
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GSM8k (5-shot)
	type: gsm8k
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 4.78
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shuvom/yuj-v1
	name: Open LLM Leaderboard
	---

	# The Model yuj-v1:

	The yuj-v1 model is a blend of advanced models strategically crafted to enhance Hindi Language Models (LLMs) effectively and democratically. Its primary goals include catalyzing the development of Hindi and its communities, making significant contributions to linguistic knowledge. The term "yuj," from Sanskrit, signifies fundamental unity, highlighting the integration of sophisticated technologies to improve the language experience for users in the Hindi-speaking community.

	Official GGUF version: [shuvom/yuj-v1-GGUF](https://huggingface.co/shuvom/yuj-v1-GGUF)

	Below are the model which are leverage to build this yuj-v1:
	* [ai4bharat/Airavata](https://huggingface.co/ai4bharat/Airavata)
	* [BhabhaAI/Gajendra-v0.1](https://huggingface.co/BhabhaAI/Gajendra-v0.1)

	## ☄️Space to use it (yuj-v1 tryO):
	<a target="_blank" href="https://shuvom-yuj-v1-tryo.hf.space">
	<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
	</a>




	## 💻 Usage:

	First, you need to install some of below packages:

	1. Bits and bytes
	```python
	!pip install bitsandbytes
	```
	2. Accelerate (to install the latest version)
	```python
	!pip install git+https://github.com/huggingface/accelerate.git
	```
	3. Usage
	```python
	# Usage
	import torch

	# Load model directly
	from transformers import AutoTokenizer, AutoModelForCausalLM

	# load the model in 4-bit quantization
	tokenizer = AutoTokenizer.from_pretrained("shuvom/yuj-v1")
	model = AutoModelForCausalLM.from_pretrained("shuvom/yuj-v1",torch_dtype=torch.bfloat16,load_in_4bit=True)

	prompt = "युज शीर्ष द्विभाषी मॉडल में से एक है"
	inputs = tokenizer(prompt, return_tensors="pt")

	# Generate
	generate_ids = model.generate(inputs.input_ids, max_length=65)
	tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
	```
	4. Output
	```python
	युज शीर्ष द्विभाषी मॉडल में से एक है। यह एक उत्पादक मॉडल है जो एक साथ एक ट्रांसफॉर्मर और एक आत्म-ध्यान तंत्रिका नेटवर्क को जोड़ता है। यह एक ट्रांसफॉर्मर वास्तुकला का उपयोग करता है जो एक ट्रांसफॉर्मर मॉडल की तुलना में बहुत अधिक जटिल है।
	```

	## 🧩 Configuration

	```yaml
	models:
	- model: sarvamai/OpenHathi-7B-Hi-v0.1-Base
	# no parameters necessary for base model
	- model: ai4bharat/Airavata
	parameters:
	density: 0.5
	weight: 0.5
	- model: BhabhaAI/Gajendra-v0.1
	parameters:
	density: 0.5
	weight: 0.3
	merge_method: ties
	base_model: sarvamai/OpenHathi-7B-Hi-v0.1-Base
	parameters:
	normalize: true
	dtype: float16
	```



	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_shuvom__yuj-v1)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|45.97\|
	\|AI2 Reasoning Challenge (25-Shot)\|45.65\|
	\|HellaSwag (10-Shot) \|70.10\|
	\|MMLU (5-Shot) \|43.78\|
	\|TruthfulQA (0-shot) \|41.69\|
	\|Winogrande (5-shot) \|69.85\|
	\|GSM8k (5-shot) \| 4.78\|