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
Below are the model which are leverage to build this yuj-v1:
☄️Space to use it (yuj-v1 tryO):
💻 Usage:
First, you need to install some of below packages:
- Bits and bytes
!pip install bitsandbytes
- Accelerate (to install the latest version)
!pip install git+https://github.com/huggingface/accelerate.git
- Usage
# 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]
- Output
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
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
Detailed results can be found here
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 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard45.650
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard70.100
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard43.780
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard41.690
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard69.850
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard4.780