Instructions to use sparrowaisolutions/aras-ember-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sparrowaisolutions/aras-ember-v1 with Transformers:

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

pipe = pipeline("text-generation", model="sparrowaisolutions/aras-ember-v1", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sparrowaisolutions/aras-ember-v1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("sparrowaisolutions/aras-ember-v1", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sparrowaisolutions/aras-ember-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sparrowaisolutions/aras-ember-v1

SGLang

How to use sparrowaisolutions/aras-ember-v1 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 "sparrowaisolutions/aras-ember-v1" \
    --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": "sparrowaisolutions/aras-ember-v1",
		"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 "sparrowaisolutions/aras-ember-v1" \
        --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": "sparrowaisolutions/aras-ember-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sparrowaisolutions/aras-ember-v1 with Docker Model Runner:
```
docker model run hf.co/sparrowaisolutions/aras-ember-v1
```

🔥 Aras-Ember v1

Aras-Ember v1 is a lightweight conversational AI model trained on the Ember instruction dataset and built on top of Falcon RW-1B.

This model is designed for chat, instruction following, and experimentation with compact open-source LLMs.

Model page: https://huggingface.co/sparrowaisolutions/aras-ember-v1

[] (https://colab.research.google.com/drive/18UVyG5RWn5YJlyZqyEyP_MzvMrYoplqX?usp=sharing)

🧠 Model Details

Model name: Aras-Ember v1 Base model: tiiuae/falcon-rw-1b Architecture: Falcon decoder transformer Parameters: ~1B

Training method:

LoRA fine-tuning
merged weights for standalone model

📚 Dataset

Training dataset:

→ https://huggingface.co/datasets/sparrowaisolutions/ember-dataset

Dataset format:

{
  "instruction": "...",
  "response": "..."
}

The dataset contains instruction-response pairs designed for conversational AI training.

⚙️ Training Details

Training setup:

Base model: Falcon RW-1B
Dataset size used: ~15K examples
Training epochs: 2
Method: LoRA fine-tuning
LoRA merged into final weights

Frameworks used:

Transformers
PEFT
PyTorch
Hugging Face Datasets

🚀 Usage

Install dependencies:

!pip install transformers accelerate safetensors gradio torch --upgrade

Example usage:

# Imports
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
import torch
from threading import Thread
import gradio as gr

# Model ID on Hugging Face
model_id = "sparrowaisolutions/aras-ember-v1"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token  # required for generation padding

model = AutoModelForCausalLM.from_pretrained(model_id)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Default AI prompt
DEFAULT_PROMPT = """You are Aras-Ember, a helpful AI assistant by Sparrow AI Solutions.
Respond clearly and concisely. Stay in chatbot mode and do NOT generate unrelated articles or dataset text.
"""

def generate_stream(user_input):
    """Generate streamed response from model"""
    prompt = DEFAULT_PROMPT + f"User: {user_input}\nAras-Ember:"

    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    
    # Streamer for live output
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

    generation_kwargs = dict(
        **inputs,
        streamer=streamer,
        max_new_tokens=150,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
        repetition_penalty=1.2,
        no_repeat_ngram_size=3,
        pad_token_id=tokenizer.eos_token_id
    )

    # Run generation in separate thread
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()

    # Stream output
    response = ""
    for new_text in streamer:
        print(new_text, end="", flush=True)
        response += new_text
    print()  # newline after completion

    return response

# Gradio chat interface
def chat_fn(user_input):
    return generate_stream(user_input)

iface = gr.Interface(
    fn=chat_fn,
    inputs=gr.Textbox(lines=3, placeholder="Say something to Aras-Ember..."),
    outputs="text",
    title="Aras-Ember Chat",
    description="A lightweight AI by Sparrow AI Solutions."
)

# Launch Gradio in Colab
iface.launch(share=True)  # `share=True` gives a public URL for testing

💬 Chat Format

Recommended prompt format:

You are Aras-Ember, a creative AI assistant.

User: <question>
Assistant:

Example:

User: Explain black holes simply.
Assistant:

🧪 Intended Use

Aras-Ember is intended for:

conversational AI
research experiments
educational projects
lightweight chatbot systems

Not intended for critical decision-making systems.

⚠️ Limitations

small parameter model (1B)
limited reasoning compared to larger LLMs
may hallucinate facts
trained on a limited dataset subset

🏗 Architecture

Base architecture:

→ Falcon RW-1B by the Technology Innovation Institute

Reference model:

https://huggingface.co/tiiuae/falcon-rw-1b

👨‍💻 Authors

Created by:

Sparrow AI Solutions

Hugging Face:

https://huggingface.co/sparrowaisolutions

❤️ Acknowledgements

Thanks to:

Hugging Face
Falcon model creators
open-source AI community

Research Paper

EMBER Dataset and ARAS-EMBER Models: Open Lightweight AI Systems for Creative and Conversational Language Generation

DOI: https://doi.org/10.6084/m9.figshare.31617994

📜 License

Apache 2.0 License

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Safetensors

Model size

1B params

Tensor type

F16

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