Instructions to use sujalrajpoot/Jarvis-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sujalrajpoot/Jarvis-3B with Transformers:

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

pipe = pipeline("text-generation", model="sujalrajpoot/Jarvis-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sujalrajpoot/Jarvis-3B")
model = AutoModelForCausalLM.from_pretrained("sujalrajpoot/Jarvis-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sujalrajpoot/Jarvis-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sujalrajpoot/Jarvis-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sujalrajpoot/Jarvis-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sujalrajpoot/Jarvis-3B

SGLang

How to use sujalrajpoot/Jarvis-3B 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 "sujalrajpoot/Jarvis-3B" \
    --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": "sujalrajpoot/Jarvis-3B",
		"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 "sujalrajpoot/Jarvis-3B" \
        --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": "sujalrajpoot/Jarvis-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sujalrajpoot/Jarvis-3B with Docker Model Runner:
```
docker model run hf.co/sujalrajpoot/Jarvis-3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GGUF-model here

Overview

Jarvis-3B is a text generation model developed by Sujal Rajpoot. Inspired by the fictional AI assistant Jarvis from the Iron Man series, this model aims to emulate Jarvis's conversational abilities. With a total of 3 billion parameters, Jarvis-3B is designed to handle various natural language understanding and generation tasks.

Model Details

Model Name: Jarvis-3B
Authors: Sujal Rajpoot
Parameters: 3 billion
Architecture: Transformers
Training Data: Not specified

Intended Use

Jarvis-3B is intended for tasks requiring text generation, conversational interfaces, and natural language understanding. It can be used in various applications such as chatbots, virtual assistants, and dialogue systems.

Evaluation Results

Evaluation results for Jarvis-3B are pending. Stay tuned for updates.

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Safetensors

Model size

3B params

Tensor type

F32

F16

Model tree for sujalrajpoot/Jarvis-3B

Quantizations

1 model

sujalrajpoot
/

Jarvis-3B

Overview

Model Details

Intended Use

Evaluation Results

Model tree for sujalrajpoot/Jarvis-3B

Dataset used to train sujalrajpoot/Jarvis-3B