Navya-1
Collection
India-first financial LMs from scratch (100M-1.31B), base + SFT chat. navya-1c-sft powers navyam.ai. • 5 items • Updated
How to use navyam-ai/navya-1b with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="navyam-ai/navya-1b") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("navyam-ai/navya-1b")
model = AutoModelForCausalLM.from_pretrained("navyam-ai/navya-1b", device_map="auto")How to use navyam-ai/navya-1b with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "navyam-ai/navya-1b"
# 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",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/navyam-ai/navya-1b
How to use navyam-ai/navya-1b with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "navyam-ai/navya-1b" \
--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",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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" \
--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",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use navyam-ai/navya-1b with Docker Model Runner:
docker model run hf.co/navyam-ai/navya-1b
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 | base |
| Author | Navyam AI (Bachatt) |
| License | Apache-2.0 |
| Code | https://github.com/bachatt-app/navyam-gpt |
337M from-scratch base — the largest before the 1.31B jump.
Intended use: India personal-finance Q&A. Research model — not investment advice.
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("navyam-ai/navya-1b")
model = AutoModelForCausalLM.from_pretrained("navyam-ai/navya-1b")