Instructions to use BRR1154/SLM-Simulator-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BRR1154/SLM-Simulator-v1 with Transformers:

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

pipe = pipeline("text-generation", model="BRR1154/SLM-Simulator-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("BRR1154/SLM-Simulator-v1")
model = AutoModelForCausalLM.from_pretrained("BRR1154/SLM-Simulator-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 BRR1154/SLM-Simulator-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BRR1154/SLM-Simulator-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": "BRR1154/SLM-Simulator-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BRR1154/SLM-Simulator-v1

SGLang

How to use BRR1154/SLM-Simulator-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 "BRR1154/SLM-Simulator-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": "BRR1154/SLM-Simulator-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 "BRR1154/SLM-Simulator-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": "BRR1154/SLM-Simulator-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BRR1154/SLM-Simulator-v1 with Docker Model Runner:
```
docker model run hf.co/BRR1154/SLM-Simulator-v1
```

SLM-Simulator-0.5B-v1: Multi-Physics Simulation & State-Space Engine

Lead Architect: Bhavanam Rajendra Reddy (Runtime-Slayers | ORCID: 0009-0005-3349-1162)
Organization: Runtime-Slayers
Parameter Footprint: 492 Million
Base Architecture: Qwen/Qwen2.5-0.5B-Instruct
Weight Surgery Protocol: Gram-Schmidt Subspace Null-Space Model Editing (Zero-Forgetting $\Delta W \cdot V_{\text{base}} = 0$)

1. Overview & Specialization

Specialized in synthesizing numerical ODE/PDE state-space integrations, aerodynamic lift/drag solvers, and thermodynamic Brayton cycle simulations.

Part of the Runtime-Slayers Autonomous SLM Swarm Engine, operating in coordination with frontier orchestrators (NVIDIA Nemotron-3-Ultra 550B, DeepSeek V4.1, GLM-5.3, MiniMax M3.1, and Kimi K3.1 Pro) and the Google Research Harness² recursive optimization framework.

2. Quickstart Inference Example

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "BRR1154/SLM-Simulator-v1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float32,
    device_map="auto"
)

messages = [{"role": "user", "content": "Execute domain task."}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)

outputs = model.generate(inputs, max_new_tokens=256, temperature=0.2)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

📊 Empirical Academic Benchmarks & Evaluation Realities

1. Official Foundation Baselines (`Qwen2.5-0.5B-Instruct` Technical Report, 2024):

Benchmark	Domain / Language	Evaluation Setup	Verified Baseline Score	Empirical Assessment
HumanEval	Python Code Generation	0-Shot, Pass@1	41.5%	Reliable for short, single-function Python code.
MBPP	Python Math & Basics	0-Shot, Pass@1	44.2%	Handles basic algorithmic programming tasks.
MultiPL-E (Average)	Multi-Language Code	0-Shot, Pass@1	38.4%	Cross-language code translation baseline.
MultiPL-E (MATLAB)	MATLAB Engineering	0-Shot, Pass@1	~24.8%	High Failure Area: Struggles with 1-based indexing, dimension matching, and matrix syntax.
GSM8K	Grade-School Math	8-Shot	43.1%	Basic arithmetic and single-step word problems.
MATH	Complex Mathematics	4-Shot	18.2%	Constrained on multi-step symbolic derivation.
MMLU	General Knowledge & Science	5-Shot	46.8%	Broad factual knowledge baseline for 0.5B scale.
ARC-Challenge	Scientific Reasoning	25-Shot	43.8%	Moderate scientific question-answering.

2. Surgical Weight Editing Protocol:

Null-Space Projection: Parameter updates $\Delta W$ were projected onto the orthogonal null space of base activation covariances ($V_{\text{base}}$) such that $\Delta W \cdot V_{\text{base}} = 0$.
Zero Catastrophic Forgetting: Base conversational and instruction-following capability is mathematically preserved while domain representations are calibrated.

3. Empirical Capacity Boundaries:

At 492M parameters, autonomous multi-step reasoning and deep error self-correction are constrained.
In production, this model is designed to operate within a closed-loop execution harness (interfacing with headless MATLAB R2024b, Blender 5.1, or Python) where execution errors are intercepted and rectified iteratively.

3. Citation

@misc{reddy2026slm_simulator_v1,
  author = {Reddy, Bhavanam Rajendra},
  title = {SLM-Simulator-0.5B-v1: Multi-Physics Simulation & State-Space Engine},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/BRR1154/SLM-Simulator-v1}}
}

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Safetensors

Model size

0.5B params

Tensor type

F32

Model tree for BRR1154/SLM-Simulator-v1

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct