Instructions to use psyflowz/psyxd-nepali-jarvis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use psyflowz/psyxd-nepali-jarvis with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("psyflowz/psyxd-nepali-jarvis", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use psyflowz/psyxd-nepali-jarvis with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/psyflowz/psyxd-nepali-jarvis

SGLang

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

Docker Model Runner
How to use psyflowz/psyxd-nepali-jarvis with Docker Model Runner:
```
docker model run hf.co/psyflowz/psyxd-nepali-jarvis
```

PsyxD Nepali Jarvis

A multilingual Nepali AI assistant powered by fine-tuned Qwen3-8B.

Features

Multilingual: Speaks Nepali, English, and Hindi naturally
Coding Expert: Explains programming concepts in Nepali + English
Tool Calling: File management, browser automation, Python execution, GitHub, smart-home, charts
Persistent Memory: Remembers conversations using embeddings + vector store
Voice I/O: Speech-to-text and text-to-speech support
Local & Cloud: Deployable locally or on Hugging Face

Project Structure

├── app/agent/          Agent planner, router, executor
├── app/tools/          Tool integrations
├── app/memory/         Persistent memory with vector store
├── app/voice/          Speech-to-text & text-to-speech
├── app/personality/    System prompt & cultural traits
├── app/ui/             Interface
├── datasets/           Training datasets (JSONL)
├── training/           Training, merging, evaluation scripts
├── configs/            Configuration files

Setup

python -m venv .venv
# Windows
.venv\Scripts\activate
# Linux/Mac
source .venv/bin/activate

pip install -r requirements.txt

Copy .env.example to .env and fill in your tokens.

Training

python training/train.py

Run

uvicorn app.main:app --reload

Push to Hugging Face

from transformers import AutoModelForCausalLM, AutoTokenizer

model.push_to_hub("psyflowz/psyxd-nepali-jarvis")
tokenizer.push_to_hub("psyflowz/psyxd-nepali-jarvis")

License

MIT

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Model tree for psyflowz/psyxd-nepali-jarvis

Base model

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