Instructions to use DivyaRani/TallyAssistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DivyaRani/TallyAssistant with Transformers:

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

pipe = pipeline("text-generation", model="DivyaRani/TallyAssistant")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DivyaRani/TallyAssistant")
model = AutoModelForCausalLM.from_pretrained("DivyaRani/TallyAssistant")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DivyaRani/TallyAssistant with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DivyaRani/TallyAssistant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DivyaRani/TallyAssistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DivyaRani/TallyAssistant

SGLang

How to use DivyaRani/TallyAssistant 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 "DivyaRani/TallyAssistant" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DivyaRani/TallyAssistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "DivyaRani/TallyAssistant" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DivyaRani/TallyAssistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DivyaRani/TallyAssistant with Docker Model Runner:
```
docker model run hf.co/DivyaRani/TallyAssistant
```

💼 TallyPrimeAssistant — Distilled GPT-2 Model

This is a distilled GPT-2-based conversational model fine-tuned on FAQs and navigation instructions from TallyPrime, a leading business accounting software used widely in India. The model is designed to help users get quick and accurate answers about using features in TallyPrime like GST, e-invoicing, payroll, and more.

🧠 Model Summary

Teacher Model: gpt2-large
Student Model: distilgpt2
Distillation Method: Knowledge Distillation using Hugging Face's Transformers and custom training pipeline
Training Dataset: Internal dataset of Q&A pairs and system navigation steps from TallyPrime documentation and usage
Format: safetensors (secure and fast)
Tokenizer: Byte-Pair Encoding (BPE), same as GPT-2

🚀 Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("Jayanthram/TallyPrimeAssistant")
tokenizer = AutoTokenizer.from_pretrained("Jayanthram/TallyPrimeAssistant")

prompt = "How to enable GST in Tally Prime?"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=60)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Downloads last month: 3

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for DivyaRani/TallyAssistant

Base model

openai-community/gpt2-large

Finetuned

(129)

this model