⚔️ Rostam-S1 (رستم)

Frontier Reasoning & Agentic Model by Mob AI (in collaboration with Spark)

Rostam-S1 is an elite intelligent reasoning and agentic model developed by Mob AI in strategic technological collaboration with the Spark project. Named after the legendary champion of Persian epic literature whose name personifies valor, strategic intellect, and unwavering perseverance, Rostam is engineered to deliver frontier capabilities across Mathematics, Theoretical Physics, Scientific Coding, Persian (Farsi) Cognition, and Agentic Systems.

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📌 Development Philosophy: Architectural Excellence

Rostam is built on the conviction that architectural precision, high-order reasoning traces, and disciplined alignment triumph over brute-force scaling. Rather than defining capability by parameter volume, Rostam focuses on:

• Verifiable Symbolic Reasoning: Step-by-step mathematical proofs and physical derivations.
• Deep Cognitive Duality: Native bilingual mastery across English and Persian technical thought.
• Zero Catastrophic Forgetting: Subspace-isolated parameter adaptation protected by persistent replay regularization.

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🎯 Target Benchmarks & Alignment Focus

Note on Evaluation Transparency: The numbers in the Target column represent the mathematical optimization goals designed for the full post-training curriculum. Official audited benchmark scores will be released upon completion of the full RLVR evaluation pipeline.

*Baseline and competitor numbers are from officially released model cards and papers.

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🚀 Key Features

• Dual-Channel Deep Reasoning (<think>): Native controllable reasoning traces providing step-by-step analytical derivations before delivering definitive conclusions.
• Deep Persian (فارسی) Cognitive Integration: Full bilingual fluency across technical terminology, complex physics derivations, software engineering, and formal Persian prose.
• Agentic Tool Integration: Native function calling and verifiable sandbox execution.

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💻 Quickstart Inference

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "officialmobai/Rostam-S1-4B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "نام شما چیست و سازنده شما کیست؟"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.9
)

print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

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🏢 Attribution & Lineage

Developed by Mob AI (https://huggingface.co/officialmobai) in strategic collaboration with Spark (XHToken).