Instructions to use navyam-ai/navya-1b-sft-v9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use navyam-ai/navya-1b-sft-v9 with Transformers:

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

pipe = pipeline("text-generation", model="navyam-ai/navya-1b-sft-v9")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("navyam-ai/navya-1b-sft-v9")
model = AutoModelForCausalLM.from_pretrained("navyam-ai/navya-1b-sft-v9", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use navyam-ai/navya-1b-sft-v9 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "navyam-ai/navya-1b-sft-v9"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "navyam-ai/navya-1b-sft-v9",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/navyam-ai/navya-1b-sft-v9

SGLang

How to use navyam-ai/navya-1b-sft-v9 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 "navyam-ai/navya-1b-sft-v9" \
    --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": "navyam-ai/navya-1b-sft-v9",
		"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 "navyam-ai/navya-1b-sft-v9" \
        --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": "navyam-ai/navya-1b-sft-v9",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use navyam-ai/navya-1b-sft-v9 with Docker Model Runner:
```
docker model run hf.co/navyam-ai/navya-1b-sft-v9
```

navya-1b-sft-v9

Navya is an India-first financial foundation model family, trained from scratch on an India-first corpus (finance + English + Hindi/Hinglish and other Indian languages), custom 64k tokenizer.


Parameters	337M
Training tokens	not disclosed
Trained	2026
Type	chat (SFT)
Author	Navyam AI (Bachatt)
License	Apache-2.0
Code	https://github.com/bachatt-app/navyam-gpt

337M base instruction-tuned (SFT) on the India-finance corpus. Prior model served on navyam.ai.

Intended use: India personal-finance Q&A. Research model — not investment advice.

Chat template

Role tokens <|reserved_0..4|> (system/user/assistant/tool/end_turn); generation stops on <|reserved_4|> and <|eos|>.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("navyam-ai/navya-1b-sft-v9")
model = AutoModelForCausalLM.from_pretrained("navyam-ai/navya-1b-sft-v9")

Downloads last month: -

Safetensors

Model size

0.3B params

Tensor type

F32

Collection including navyam-ai/navya-1b-sft-v9

Navya-1

Collection

India-first financial LMs from scratch (100M-1.31B), base + SFT chat. navya-1c-sft powers navyam.ai. • 5 items • Updated about 8 hours ago