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

Libraries

How to use kshitijthakkar/shlokartha with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kshitijthakkar/shlokartha")
model = AutoModelForCausalLM.from_pretrained("kshitijthakkar/shlokartha", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kshitijthakkar/shlokartha with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/kshitijthakkar/shlokartha

SGLang

How to use kshitijthakkar/shlokartha 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 "kshitijthakkar/shlokartha" \
    --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": "kshitijthakkar/shlokartha",
		"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 "kshitijthakkar/shlokartha" \
        --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": "kshitijthakkar/shlokartha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Shlokartha Vāgartha SFT

Shlokartha is a compact causal language model fine-tuned for layered Sanskrit verse interpretation. It is designed to separate Pada Artha (word-by-word meaning), Anvaya (semantic translation), and Bhavartha (expanded context) while preserving Devanagari and transliteration where useful.

Model details

Base checkpoint: kshitijthakkar/loggenix-moe-0.4B-0.2A-sft-s4
Training type: full-parameter supervised fine-tuning
Training dataset: kshitijthakkar/shlokartha-sft
Upstream corpus: sarvamai/vagartha
Demo: kshitijthakkar/shlokartha-playground

Training configuration

The final run used three epochs and 3,372 optimizer steps:

Setting	Value
Learning rate	`2e-4`
Scheduler	cosine
Warmup	`10%` of training steps
Per-device batch size	`4`
Gradient accumulation	`8`
Effective batch size	`32` sequences/update
Maximum sequence length	`8,192` tokens
Packing	enabled
Precision	bfloat16
Optimizer	`adamw_torch_fused`
Gradient checkpointing	enabled
Hardware	1 × 96 GB RTX 6000-class GPU

The held-out validation split contained 4,404 examples. The final logged validation loss was 1.469 and mean token accuracy was 0.6935. These are training diagnostics, not a claim of expert-level Sanskrit correctness or a substitute for human evaluation.

Intended use

Use this checkpoint for experimentation with Sanskrit verse explanation, Indic-language interfaces, educational prototypes, and research on structured literary interpretation. The companion playground exposes decoding controls and displays the three intended commentary layers separately.

Limitations and responsible use

The model can produce fluent but incorrect translations, grammatical analyses, source attributions, or philosophical context. It should not be treated as an authoritative Sanskrit scholar, a substitute for a critical edition, or a sole source for religious, historical, or educational claims. Review outputs against the original text and qualified references. The training corpus contains source explanations from the upstream Vāgartha dataset; retain its CC-BY-4.0 attribution when redistributing derived material.

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "kshitijthakkar/shlokartha"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, trust_remote_code=True, dtype="auto"
)

messages = [{
    "role": "user",
    "content": (
        "Explain this Sanskrit verse with exactly these headings: "
        "**Pada Artha**, **Anvaya**, and **Bhavartha**.\n\n"
        "कर्मण्येवाधिकारस्ते मा फलेषु कदाचन।"
    ),
}]
inputs = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

License and attribution

The dataset used for fine-tuning is derived from sarvamai/vagartha and is distributed under CC-BY-4.0 with upstream attribution requirements. Consult the base checkpoint's terms for model redistribution and derivative-use conditions.