Navya-1
Collection
India-first financial LMs from scratch (100M-1.31B), base + SFT chat. navya-1c-sft powers navyam.ai. • 6 items • Updated
Text Generation
Transformers
Safetensors
English
Hindi
llama
india
finance
from-scratch
navya
text-generation-inference
How to use from
SGLangUse 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-1c" \
--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-1c",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
navya-1c
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 | 1.31B (dim2048/26L, GQA16/4) |
| Training tokens | ~103B |
| Trained | Aug-Sep 2026 |
| Type | base |
| Author | Navyam AI (Bachatt) |
| License | Apache-2.0 |
| Code | https://github.com/bachatt-app/navyam-gpt |
1.31B from-scratch decoder trained ~103B tokens with a WSD cooldown, on 8xH100. Custom 64k tokenizer.
Intended use: India personal-finance Q&A. Research model — not investment advice.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("navyam-ai/navya-1c")
model = AutoModelForCausalLM.from_pretrained("navyam-ai/navya-1c")
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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-1c" \ --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-1c", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'