Instructions to use Abhayn01/ARKA-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abhayn01/ARKA-v3 with Transformers:

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

pipe = pipeline("text-generation", model="Abhayn01/ARKA-v3", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Abhayn01/ARKA-v3", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Abhayn01/ARKA-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abhayn01/ARKA-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abhayn01/ARKA-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Abhayn01/ARKA-v3

SGLang

How to use Abhayn01/ARKA-v3 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 "Abhayn01/ARKA-v3" \
    --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": "Abhayn01/ARKA-v3",
		"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 "Abhayn01/ARKA-v3" \
        --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": "Abhayn01/ARKA-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Abhayn01/ARKA-v3 with Docker Model Runner:
```
docker model run hf.co/Abhayn01/ARKA-v3
```

⚡ ARKA-v3

A Custom 505M-Parameter Language Model

Created and developed by Abhay Kumar Rudrapaul

ARKA-v3 is a decoder-only Transformer language-model architecture using Grouped-Query Attention (GQA), RoPE, RMSNorm, SwiGLU and tied token/output embeddings. This recreated package contains fresh randomly initialized weights; it must be pretrained before meaningful text generation.

Architecture

Specification	Value
Parameters	505,661,184
Vocabulary	151,665
Hidden size	1,152
Layers	22
Attention heads	18
KV heads	6
Head dimension	64
FFN dimension	3,328
Maximum context	4,096
Weight tying	Yes
Attention	Grouped-Query Attention
Position encoding	RoPE
Activation	SwiGLU
Normalization	RMSNorm

The maintained Qwen2 implementation primitives in Hugging Face Transformers are used as the execution backend because their tensor layout matches this architecture. ARKA-v3 has its own model type, configuration and AutoModel registration.

Installation

pip install -U "torch>=2.4" "transformers>=4.53" \
  "huggingface_hub>=0.27" "safetensors>=0.4.5" "accelerate>=1.2"

Load

Review the repository's custom Python files before enabling remote code. For reproducible use, pin revision to a trusted commit hash.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "Abhayn01/ARKA-v3"
revision = "PASTE_TRUSTED_COMMIT_HASH"

tokenizer = AutoTokenizer.from_pretrained(
    repo_id, revision=revision, trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    revision=revision,
    trust_remote_code=True,
    dtype=torch.float16,
)

Important status

The recreated checkpoint is randomly initialized. It preserves the exact architecture and parameter count, but it does not reconstruct previously deleted learned weights or knowledge. Pretraining is required.

Creator

ARKA is an independent language-model development project created and developed by Abhay Kumar Rudrapaul.

Downloads last month: 294

Safetensors

Model size

0.5B params

Tensor type

F16