Instructions to use Abhayn01/ARKA-SLM-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abhayn01/ARKA-SLM-V1 with Transformers:

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

pipe = pipeline("text-generation", model="Abhayn01/ARKA-SLM-V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Abhayn01/ARKA-SLM-V1")
model = AutoModelForCausalLM.from_pretrained("Abhayn01/ARKA-SLM-V1", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Abhayn01/ARKA-SLM-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abhayn01/ARKA-SLM-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": "Abhayn01/ARKA-SLM-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Abhayn01/ARKA-SLM-V1

SGLang

How to use Abhayn01/ARKA-SLM-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 "Abhayn01/ARKA-SLM-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": "Abhayn01/ARKA-SLM-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 "Abhayn01/ARKA-SLM-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": "Abhayn01/ARKA-SLM-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Abhayn01/ARKA-SLM-V1 with Docker Model Runner:
```
docker model run hf.co/Abhayn01/ARKA-SLM-V1
```

ARKA SLM V1

ARKA SLM V1 is the first official release of the ARKA Small Language Model family.

ARKA stands for Academic Research Knowledge Assistant.

Creator and Developer: Abhay Kumar Rudrapaul

ARKA SLM V1 is a compact causal language model designed as a foundation for conversational AI, educational applications, RAG systems, mobile AI experiments and domain-specific assistants.

Fine-tuning is recommended before specialized or production deployment.

Retrieval-Augmented Generation (RAG) is strongly recommended for factual, private, domain-specific or frequently changing information.

Model Information

Property	Value
Official Name	ARKA SLM V1
Full Form	Academic Research Knowledge Assistant
Creator	Abhay Kumar Rudrapaul
Version	V1
Release Status	Official Release
Model Type	Small Language Model
Architecture	GPT2LMHeadModel
Parameters	129,944,832
Hidden Size	768
Transformer Layers	12
Attention Heads	12
Head Dimension	64
Vocabulary Size	50,257
Maximum Context Positions	8,192
Framework	PyTorch / Hugging Face Transformers

About ARKA

ARKA is a Small Language Model project created and developed by Abhay Kumar Rudrapaul.

The project explores compact language models combined with supervised fine-tuning, instruction tuning, retrieval, external tools and application-specific AI.

ARKA SLM V1 is intended to provide a compact foundation that developers can further adapt for their own use cases.

Release Checkpoint

Source repository: Abhayn01/arka-v5-8k-mixed-sft-54m-v1
Source revision: a4a4f1566c57f5bb2ce3417c184fc7ffc94a6ca5
Source stage: balanced_recovery_2p5m_v1
Source status: complete
Source step: unknown
Recorded cumulative valid-token exposure: 76,539,609
Recorded cumulative supervised-token exposure: unknown

The official release does not contain optimizer, gradient-scaler or intermediate training checkpoint state.

Weight Integrity

NaN values: 0
Infinite values: 0
Fully-zero parameter tensors: 0
Tied LM head / token embeddings: True
Weight integrity: Verified

All model parameters were set to requires_grad=False during the official release-build verification.

Important: requires_grad=False is a runtime PyTorch property and is not permanently stored in SafeTensors. Users can intentionally fine-tune their own loaded copy.

Fine-Tuning Recommendation

ARKA SLM V1 should generally be treated as a foundation checkpoint.

Fine-tuning is recommended before using the model for a specialized domain or production application.

Examples include:

educational assistants
electrical engineering assistants
campus assistants
customer-support systems
mobile personal assistants
domain-specific question answering
document assistants
application intent routing

Retrieval-Augmented Generation (RAG)

RAG is strongly recommended when ARKA is expected to answer factual or knowledge-intensive questions.

Typical pipeline:

User Question
     |
     v
Intent Router
     |
     +---- Direct task ----> ARKA
     |
     +---- Knowledge task -> Retriever
                              |
                              v
                       Retrieved Evidence
                              |
                              v
                         ARKA SLM V1
                              |
                              v
                       Grounded Response

Possible retrieval sources include PDFs, local documents, vector databases, verified websites, institutional data, company documentation and search engines.

RAG can improve grounding, but it does not guarantee perfect factual accuracy.

Mobile and Edge Applications

ARKA SLM V1 contains approximately 129.9 million parameters.

It may be integrated into mobile or local applications using an appropriate runtime and, where necessary, quantization or model conversion.

Potential applications include:

mobile AI assistants
educational applications
campus assistants
voice assistants
offline or partially offline assistants
document Q&A systems
domain-specific RAG applications
embedded conversational features

Actual performance depends on model precision, RAM, CPU/GPU/NPU capability, runtime and context length.

Prompt Format

User: <user message>
Assistant:

Python Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_ID = "Abhayn01/ARKA-SLM-V1"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID)

prompt = "User: Explain renewable energy simply.\nAssistant:"

inputs = tokenizer(prompt, return_tensors="pt")

output = model.generate(
    **inputs,
    max_new_tokens=120,
    do_sample=False,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.eos_token_id,
)

new_tokens = output[0, inputs['input_ids'].shape[1]:]

print(tokenizer.decode(new_tokens, skip_special_tokens=True))

Current Strengths

Development testing has shown useful behavior in:

ARKA identity handling
short instruction following
exact short responses
supplied-context question answering
explicit missing-context abstention
some follow-up corrections
short-context recall
RAG-oriented workflows

These observations are developmental and are not standardized benchmark claims.

Known Limitations

ARKA SLM V1 is a compact model and has important limitations.

factual recall can be unreliable
unsupported questions may produce hallucinations
arithmetic reasoning is weak
programming ability is limited
electrical knowledge is inconsistent
multi-entity context binding can fail
long-form text may become repetitive
structured email/story/dialogue generation is inconsistent
exact formatting or units may occasionally be lost
reasoning capability is below significantly larger models

For factual tasks, developers should prefer RAG and validate important outputs.

Memory

ARKA SLM V1 should not be assumed to remember information from previous conversations unless the application explicitly provides that information in the active context.

Persistent memory should be implemented at the application layer.

Recommended Production Architecture

                     +--> Direct ARKA
                     |
User -> Intent Router+--> RAG -> ARKA
                     |
                     +--> Calculator / Tools
                     |
                     +--> Application Actions

High-Stakes Use

ARKA SLM V1 should not be used as the sole authority for medical, legal, financial, emergency, safety-critical or similarly high-stakes decisions.

Creator

Abhay Kumar Rudrapaul

Creator and developer of the ARKA model family.

Version

ARKA SLM V1 — Version 1.0

This checkpoint represents the first officially designated release of the ARKA Small Language Model family.

Future ARKA releases may improve factual knowledge, reasoning, long-form generation, RAG, tool use, coding, domain specialization and efficient mobile inference.

License

No specific open-source license is automatically declared by this release script. The repository owner should select an appropriate license separately before defining redistribution or commercial-use rights.

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Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for Abhayn01/ARKA-SLM-V1

Adapters

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