Instructions to use nvsngurram/cai-group123-assignment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvsngurram/cai-group123-assignment with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nvsngurram/cai-group123-assignment")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvsngurram/cai-group123-assignment")
model = AutoModelForCausalLM.from_pretrained("nvsngurram/cai-group123-assignment", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvsngurram/cai-group123-assignment with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvsngurram/cai-group123-assignment"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvsngurram/cai-group123-assignment",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nvsngurram/cai-group123-assignment

SGLang

How to use nvsngurram/cai-group123-assignment with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "nvsngurram/cai-group123-assignment" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvsngurram/cai-group123-assignment",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "nvsngurram/cai-group123-assignment" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvsngurram/cai-group123-assignment",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nvsngurram/cai-group123-assignment with Docker Model Runner:
```
docker model run hf.co/nvsngurram/cai-group123-assignment
```

Model Card for Model ID

Comparative Financial QA System: RAG vs Fine-Tuning

Model Details

Objective: Develop and compare two systems for answering questions based on company financial statements (last two years):

Retrieval-Augmented Generation (RAG) Chatbot: Combines document retrieval and generative response. Fine-Tuned Language Model (FT) Chatbot: Directly fine-tunes a small open-source language model on financial Q&A.

Model Description

This model is Desinged as part of Conversation AI Asignment 2, we used IRFC Annual reports of 2023-24 and 2024-25 and extracted the data then used for model training and evaluation.

Developed by: [Assignment Group 123]
Funded by [optional]: [Group 123]
Shared by [optional]: [Group 123]
Model type: [RAG, Fine-Tune]
Language(s) (NLP): [NLP]
License: [More Information Needed]
Finetuned from model [optional]: [gpt2]

Model Sources [optional]

Repository: [https://huggingface.co/nvsngurram/cai-group123-assignment]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Question and Answer based System for the learning of implementation of RAG and Fine-Tuning the model.

Direct Use

Used to Analyse the pdf documents, let's say financial reports of a company can be analysed and give summary of it based on the user query in a short and compact manner.

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

responsive text generation

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model. Step 1: clone the git repo into local Step 2: install the required libraries by running pip install -r requirement.txt Step 3: check if Annual reports are under data/annual_reports and QandA.txt in the data/raw/QandA.txt Step 4: run the data_extraction.py(python src/data_extraction.py) script to convert from .PDF to .txt Step 5: run the rag_ft_qa.py(python src/rag_ft_qa.py) script to pre-process, segmentation of data, tokenization, creation of model, pre-train the model then Implement of RAG techniques(cross encoder), Fine-tune the model with dataset prepared, re-ranking the results and extracting best of it, compared the results and represented in tabular format. Step 6: run the streamlit_cli.py(python streamlit_cli.py) script to run the application on streamlit

[More Information Needed]

Training Details

Training moto is to train the model with sample 10 questions and evaluate the performace after training the model.

Training Data

used Annual Report 2023-24.pdf and Annual Report 2024-25.pdf annual reports for raw data, then converted into .txt files and then segmented and trained with 400 chunck dataset.

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

Summary

Model Examination [optional]

[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 [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

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Safetensors

Model size

0.1B params

Tensor type

F32

Space using nvsngurram/cai-group123-assignment 1

Paper for nvsngurram/cai-group123-assignment

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 61