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ProjectIndus

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metadata
license: osl-3.0
model-index:
  - name: indus_1.175B
    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: 22.7
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=nickmalhotra/ProjectIndus
          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: 25.04
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=nickmalhotra/indus_1.175B
          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: 23.12
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=nickmalhotra/indus_1.175B
          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: 0
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=nickmalhotra/indus_1.175B
          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: 49.57
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=nickmalhotra/indus_1.175B
          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
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=nickmalhotra/indus_1.175B
          name: Open LLM Leaderboard

Model Card for Project Indus

The model is a pretrained model in Hindi and dialects which is instruct tuned .

Table of Contents

Model Details

Model Description

TThe model is a pretrained model in Hindi and dialects which is instruct tuned.

  • Developed by: Nikhil Malhotra, Nilesh Brahme, Satish Mishra, Vinay Sharma (Makers Lab, TechMahindra)
  • Model type: Foundational Language model
  • Language(s) (NLP): hin, bho, mai, doi
  • License: other
  • Parent Model: It is a grounds up model built on GPT-2 architecture starting from tokenizer to decoder
  • Resources for more information: https://www.techmahindra.com/en-in/innovation/the-indus-project/

Uses

Uses include question and answeting and conversation in Hindi and Dialects. The model would be reward tuned to be used across various industries

  1. Call center
  2. Healthcare
  3. Automotive
  4. Telecom

Direct Use

Direct use is as a foundationla model on Hindi and dialects

Downstream Use [Optional]

Uses include question and answeting and conversation in Hindi and Dialects. The model would be reward tuned to be used across various industries

  1. Call center
  2. Healthcare
  3. Automotive
  4. Telecom

Out-of-Scope Use

Cannot be used for fill in the blanks, Multiple Q&A etc. at the moment

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. We have taken care across various biases by trying to remove them from training data. However since the model is a generative model, it would tend to produce hallucinations. Any disturbing or harmful sterotype produced by the model is purely un-intentional and coincidental.

Recommendations

Recommendation is to not use biases and negative connotation for the model

Training Details

Training Data

More information on training data needed

Training Procedure

Preprocessing

More information needed

Speeds, Sizes, Times

More information needed

Evaluation

Testing Data, Factors & Metrics

Testing Data

More information needed

Factors

More information needed

Metrics

More information needed

Results

More information needed

Model Examination

More information needed

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: More information needed
  • Hours used: More information needed
  • Cloud Provider: More information needed
  • Compute Region: More information needed
  • Carbon Emitted: More information needed

Technical Specifications [optional]

Model Architecture and Objective

More information needed

Compute Infrastructure

More information needed

Hardware

More information needed

Software

More information needed

Citation

BibTeX:

More information needed

APA:

More information needed

Glossary [optional]

More information needed

More Information [optional]

More information needed

Model Card Authors [optional]

Nikhil Malhotra, Nilesh Brahme, Vinay Sharma, Satish Mishra

Model Card Contact

More information needed

How to Get Started with the Model

Use the code below to get started with the model.

Use a pipeline as a high-level helper

from transformers import pipeline pipe = pipeline("text-generation", model="nickmalhotra/Indus_1.175B")

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nickmalhotra/Indus_1.175B")

model = AutoModelForCausalLM.from_pretrained("nickmalhotra/Indus_1.175B")

Click to expand

More information needed

# [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_nickmalhotra__indus_1.175B)
Metric Value
Avg. 20.07
AI2 Reasoning Challenge (25-Shot) 22.70
HellaSwag (10-Shot) 25.04
MMLU (5-Shot) 23.12
TruthfulQA (0-shot) 0.00
Winogrande (5-shot) 49.57
GSM8k (5-shot) 0.00