Gaja-v2.00 / README.md
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---
language:
- en
- hi
license: llama2
library_name: transformers
tags:
- hindi
- 'english '
- Bilingual
datasets:
- sarvamai/samvaad-hi-v1
pipeline_tag: text-generation
model-index:
- name: Gaja-v2.00
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: 51.79
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=damerajee/Gaja-v2.00
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: 75.79
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=damerajee/Gaja-v2.00
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: 40.69
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=damerajee/Gaja-v2.00
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: 41.5
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=damerajee/Gaja-v2.00
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: 71.9
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=damerajee/Gaja-v2.00
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: 0.23
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=damerajee/Gaja-v2.00
name: Open LLM Leaderboard
---
# Model
# ๐Ÿ˜ Gaja
Gaja is a Hindi/Hinglish chat model, initially trained on SarvamAI's OpenHathi model and further fine-tuned for conversational interactions.
<img src="https://cdn-uploads.huggingface.co/production/uploads/6487239cca30096ea9f52115/sLgskU1h2Tih_bJo0ktYR.jpeg" width="600" alt="Image">
# 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](https://www.indic.chat/)
# Additional Information
- The code for this can be found in The github code - [Github](https://github.com/dame-cell/Gaja)
# ๐Ÿ’ฌ Prompt template
```
<|im_start|>user
{}<|im_end|>
<|im_start|>assistant
{}<|im_end|>
```
# ๐Ÿ˜Ž 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.
# [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_damerajee__Gaja-v2.00)
| 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|