Instructions to use GainEnergy/OGAI-STEM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GainEnergy/OGAI-STEM-7B with Transformers:

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

pipe = pipeline("text-generation", model="GainEnergy/OGAI-STEM-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GainEnergy/OGAI-STEM-7B")
model = AutoModelForCausalLM.from_pretrained("GainEnergy/OGAI-STEM-7B")
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 GainEnergy/OGAI-STEM-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GainEnergy/OGAI-STEM-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GainEnergy/OGAI-STEM-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GainEnergy/OGAI-STEM-7B

SGLang

How to use GainEnergy/OGAI-STEM-7B 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 "GainEnergy/OGAI-STEM-7B" \
    --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": "GainEnergy/OGAI-STEM-7B",
		"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 "GainEnergy/OGAI-STEM-7B" \
        --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": "GainEnergy/OGAI-STEM-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GainEnergy/OGAI-STEM-7B with Docker Model Runner:
```
docker model run hf.co/GainEnergy/OGAI-STEM-7B
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

OGAI-STEM-7B: AI-Powered Engineering Model for Oil & Gas Calculations

Model Description

OGAI-STEM-7B is a LoRA fine-tuned Mathstral-7B model, designed specifically for oil and gas engineering, scientific computing, and technical problem-solving. It is optimized for numerical accuracy, complex engineering calculations, and technical document understanding.

The model is an integral part of GainEnergy's Upstrima AI Platform, enhancing workflows with pragmatic AI agents, scientific computing tools, and retrieval-augmented generation (RAG)-based document analysis.

Technical Architecture

Base Model Specifications

Architecture: Mathstral-7B (Mistral fine-tuned for advanced math reasoning)
Parameters: 7B
Context Length: 32,768 tokens for long-form scientific queries
Mathematical Precision: Enhanced for oil & gas engineering computations

Fine-tuning Approach

Method: Low-Rank Adaptation (LoRA) with rank 64
Training Dataset: 3.2M datapoints from specialized oil & gas engineering sources
Hardware: Trained on 8x NVIDIA A100 80GB GPUs
Training Time: 2,200 GPU hours
Special Features: Improved accuracy in fluid mechanics, pressure drop, and geomechanics calculations

Performance Optimizations

Quantization: 4-bit and 8-bit versions optimized for low-memory inference
Inference Speed: Tuned KV cache management for real-time engineering computations
Memory Footprint: Runs efficiently on 12GB VRAM with 4-bit quantization
Reduced Hallucinations: Domain-specific fine-tuning minimizes incorrect scientific results

Deployment-Optimized Versions

Version	Memory Requirement	Performance
OGAI-STEM-7B-GGUF	CPU optimized	Suitable for edge computing

Local Deployment with vLLM

python -m vllm.entrypoints.openai.api_server \
  --model GainEnergy/ogai-stem-7b \
  --tensor-parallel-size 2

How to Use

Run Inference in Python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "GainEnergy/ogai-stem-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Calculate the pressure drop in a 500m pipeline with a 10,000 BPD flow rate."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citing OGAI-STEM-7B

@article{ogai_stem_7b_2025,
  title={OGAI-STEM-7B: AI Model for Oil & Gas Scientific Computing},
  author={GainEnergy AI Team},
  year={2025},
  publisher={Hugging Face Models}
}

Downloads last month: 9

Safetensors

Model size

7B params

Tensor type

F32

Model tree for GainEnergy/OGAI-STEM-7B

Base model

mistralai/Mathstral-7B-v0.1

Adapter

(4)

this model

Datasets used to train GainEnergy/OGAI-STEM-7B

Collection including GainEnergy/OGAI-STEM-7B

SMoE Datasets

Collection

Sparse Mixture of Experts datasets for mathematical reasoning and complex calculations. • 13 items • Updated Mar 8, 2025 • 1

Evaluation results

Engineering Calculations Accuracy on GainEnergy Oil & Gas Corpus
self-reported

94.500
Scientific Computation Precision on GainEnergy Oil & Gas Corpus
self-reported

92.300
Context Retention on GainEnergy Oil & Gas Corpus
self-reported

High