Instructions to use nayeshdaggula/sutradhar-3b-moe-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nayeshdaggula/sutradhar-3b-moe-base with Transformers:

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

pipe = pipeline("text-generation", model="nayeshdaggula/sutradhar-3b-moe-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nayeshdaggula/sutradhar-3b-moe-base")
model = AutoModelForCausalLM.from_pretrained("nayeshdaggula/sutradhar-3b-moe-base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nayeshdaggula/sutradhar-3b-moe-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nayeshdaggula/sutradhar-3b-moe-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nayeshdaggula/sutradhar-3b-moe-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nayeshdaggula/sutradhar-3b-moe-base

SGLang

How to use nayeshdaggula/sutradhar-3b-moe-base 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 "nayeshdaggula/sutradhar-3b-moe-base" \
    --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": "nayeshdaggula/sutradhar-3b-moe-base",
		"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 "nayeshdaggula/sutradhar-3b-moe-base" \
        --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": "nayeshdaggula/sutradhar-3b-moe-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nayeshdaggula/sutradhar-3b-moe-base with Docker Model Runner:
```
docker model run hf.co/nayeshdaggula/sutradhar-3b-moe-base
```

Sutradhar 3B MoE Base

Sutradhar 3B MoE Base is an experimental Indian AI base model.

Current Main Version

This repository currently contains the sutradhar-3b-moe-base-pretrain-v0.2 checkpoint.

Training Type

Causal language modeling / continued base pretraining.

Focus

English text
Telugu text
Indian business and software vocabulary
Coding and application development vocabulary

Important

This is a base model, not an instruction/chat model.

For chat behavior, train or use a separate instruction-tuned checkpoint.

Downloads last month: 372

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for nayeshdaggula/sutradhar-3b-moe-base

Finetunes

1 model

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sutradhar-3b-moe-base