Instructions to use dkumar15/aria-1b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dkumar15/aria-1b-chat with Transformers:

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

pipe = pipeline("text-generation", model="dkumar15/aria-1b-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dkumar15/aria-1b-chat")
model = AutoModelForCausalLM.from_pretrained("dkumar15/aria-1b-chat")
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 dkumar15/aria-1b-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dkumar15/aria-1b-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dkumar15/aria-1b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dkumar15/aria-1b-chat

SGLang

How to use dkumar15/aria-1b-chat 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 "dkumar15/aria-1b-chat" \
    --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": "dkumar15/aria-1b-chat",
		"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 "dkumar15/aria-1b-chat" \
        --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": "dkumar15/aria-1b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dkumar15/aria-1b-chat with Docker Model Runner:
```
docker model run hf.co/dkumar15/aria-1b-chat
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Transformer-1B-Chat

A 1.1 billion parameter decoder-only language model trained entirely from scratch -- pretraining, supervised fine-tuning, and preference alignment -- on 8x NVIDIA H100 GPUs.

Model Details

Property	Value
Parameters	1,105,827,840 (1.1B)
Architecture	LLaMA-style Decoder-only Transformer
Hidden Size	2048
Intermediate Size	5504 (SwiGLU)
Layers	22
Attention Heads	32 (Grouped Query Attention)
KV Heads	8
Head Dim	64
Max Sequence Length	2048
Vocab Size	32,003
Precision	BFloat16

Architecture Highlights

RoPE (Rotary Position Embeddings) with theta=10,000
Grouped Query Attention (GQA) -- 4:1 query-to-KV head ratio for efficient inference
SwiGLU Feed-Forward Network
RMSNorm in a pre-norm configuration
Flash Attention 2 via PyTorch SDPA

Training Pipeline

This model was built through a complete 3-stage training pipeline:

Stage 1: Pretraining

Detail	Value
Dataset	HuggingFaceFW/fineweb-edu (sample-10BT)
Tokens Trained	~20B tokens
Steps	19,070
Duration	~12.3 hours
Optimizer	AdamW (lr=3e-4, betas=0.9/0.95, wd=0.1)
Schedule	WSD (Warmup-Stable-Decay), warmup=1000 steps
Batch Size	512 sequences (8 GPUs x 8 micro x 8 grad accum)
Final Loss	2.43
Throughput	~338K tokens/sec

Stage 2: Supervised Fine-Tuning (SFT)

Detail	Value
Dataset	HuggingFaceH4/ultrachat_200k (207,865 conversations)
Steps	3,240 (2 epochs)
Duration	~52 minutes
Optimizer	AdamW (lr=2e-5, cosine decay)
Batch Size	256 sequences
Final Loss	1.20

Stage 3: Direct Preference Optimization (DPO)

Detail	Value
Dataset	argilla/ultrafeedback-binarized-preferences-cleaned (60,917 pairs)
Steps	952 (1 epoch)
Duration	~14 minutes
Optimizer	AdamW (lr=5e-7, cosine decay)
Beta	0.1
Batch Size	64 pairs
Final Loss	0.49
Final Accuracy	72.5% (chosen preferred over rejected)
Final Reward Margin	0.84

Hardware

8x NVIDIA H100 80GB HBM3
Distributed Strategy: PyTorch DDP (DistributedDataParallel)
Communication: NCCL
Mixed Precision: BF16 autocast
Total Training Time: ~13.5 hours (all 3 stages)

Chat Template

The model uses a simple chat template with special tokens:

<|user|>
Your message here
<|end|>
<|assistant|>
Model response here
<|end|>

Special Tokens

Token	ID	Purpose
`<	user	>`
`<	assistant	>`
`<	end	>`

Limitations

1.1B parameters -- smaller models have inherent limitations in reasoning depth and factual accuracy
Trained on English data only
May generate plausible-sounding but incorrect information
The DPO alignment is single-epoch; additional iterations could improve quality
Not safety-tuned beyond what the UltraFeedback dataset provides

Training Code

The full training code is open-sourced alongside this model.

model/
  config.py          # Model and training hyperparameters
  transformer.py     # Full transformer implementation from scratch
  data.py            # Pretraining data pipeline (FineWeb-Edu)
  sft_data.py        # SFT data pipeline (UltraChat)
  dpo_data.py        # DPO data pipeline (UltraFeedback)
train.py             # Pretraining script (DDP, 8-GPU)
train_sft.py         # SFT script
train_dpo.py         # DPO script
chat.py              # Interactive chat interface
export_to_hf.py      # Export to HuggingFace format

License

Apache 2.0

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