language, license, library_name, tags, datasets, pipeline_tag, model-index
language
license
library_name
tags
datasets
pipeline_tag
model-index
llama2
transformers
text-generation
name
results
Gaja-v2.00
task
dataset
metrics
source
type
name
text-generation
Text Generation
name
type
config
split
args
AI2 Reasoning Challenge (25-Shot)
ai2_arc
ARC-Challenge
test
type
value
name
acc_norm
51.79
normalized accuracy
task
dataset
metrics
source
type
name
text-generation
Text Generation
name
type
split
args
HellaSwag (10-Shot)
hellaswag
validation
type
value
name
acc_norm
75.79
normalized accuracy
task
dataset
metrics
source
type
name
text-generation
Text Generation
name
type
config
split
args
MMLU (5-Shot)
cais/mmlu
all
test
type
value
name
acc
40.69
accuracy
task
dataset
metrics
source
type
name
text-generation
Text Generation
name
type
config
split
args
TruthfulQA (0-shot)
truthful_qa
multiple_choice
validation
task
dataset
metrics
source
type
name
text-generation
Text Generation
name
type
config
split
args
Winogrande (5-shot)
winogrande
winogrande_xl
validation
type
value
name
acc
71.9
accuracy
task
dataset
metrics
source
type
name
text-generation
Text Generation
name
type
config
split
args
GSM8k (5-shot)
gsm8k
main
test
type
value
name
acc
0.23
accuracy
Model
🐘 Gaja
Gaja is a Hindi/Hinglish chat model, initially trained on SarvamAI's OpenHathi model and further fine-tuned for conversational interactions.
Additional Information
It outperforms Airavata, AI4Bharat's chat version, on Huggingface OpenLLM benchmark suite.
It was fine-tuned on only 5k samples
Inference
hey guys thanks to Bhabha AI, you guys can finally try my model
Additional Information
The code for this can be found in The github code - Github
💬 Prompt template
😎 Features:
Language Support: Gaja is designed to understand and generate responses in both Hindi and Hinglish, catering to a diverse range of users.
Base Model: Built upon SarvamAI's OpenHathi model, Gaja inherits its foundational capabilities while being optimized for conversational tasks.
Fine-tuning: Gaja has undergone fine-tuning specifically for chat-based interactions, enhancing its ability to engage in meaningful conversations with users.
Experimental Platform: With its flexibility and adaptability, Gaja serves as a valuable platform for conducting experiments and exploring innovative approaches to chatbot development.
Detailed results can be found here
Metric
Value
Avg.
46.98
AI2 Reasoning Challenge (25-Shot)
51.79
HellaSwag (10-Shot)
75.79
MMLU (5-Shot)
40.69
TruthfulQA (0-shot)
41.50
Winogrande (5-shot)
71.90
GSM8k (5-shot)
0.23