Instructions to use Shinzmann/naija-petro-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shinzmann/naija-petro-8b with Transformers:

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

pipe = pipeline("text-generation", model="Shinzmann/naija-petro-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Shinzmann/naija-petro-8b")
model = AutoModelForCausalLM.from_pretrained("Shinzmann/naija-petro-8b")
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 Settings

vLLM

How to use Shinzmann/naija-petro-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shinzmann/naija-petro-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shinzmann/naija-petro-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Shinzmann/naija-petro-8b

SGLang

How to use Shinzmann/naija-petro-8b 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 "Shinzmann/naija-petro-8b" \
    --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": "Shinzmann/naija-petro-8b",
		"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 "Shinzmann/naija-petro-8b" \
        --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": "Shinzmann/naija-petro-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Shinzmann/naija-petro-8b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Shinzmann/naija-petro-8b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Shinzmann/naija-petro-8b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Shinzmann/naija-petro-8b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Shinzmann/naija-petro-8b",
    max_seq_length=2048,
)

Docker Model Runner
How to use Shinzmann/naija-petro-8b with Docker Model Runner:
```
docker model run hf.co/Shinzmann/naija-petro-8b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Naija-Petro 8B

Naija-Petro 8B is a Qwen3-8B model fine-tuned (QLoRA, Unsloth) on ~20,000 synthetic petroleum-engineering instruction–response pairs. It is the lightweight, fast-inference variant of the Naija-Petro family and the model served behind the project's retrieval-augmented assistant.

⚠️ The base training data is general/global petroleum knowledge. For Nigeria-specific facts (regulation, the PIA 2021, NUPRC/NMDPRA/NNPC), pair this model with the Naija-Petro RAG system, which grounds answers in verifiable Nigerian sources.

Model details

Developed by: Naija-Petro project (Hugging Face: Shinzmann)
Model type: Decoder-only causal LM, instruction-tuned
Language: English
License: Apache-2.0 (inherited from Qwen3-8B)
Finetuned from: Qwen/Qwen3-8B
Domain: Petroleum engineering — drilling, reservoir, production, completions, EOR, well testing, petroleum geoscience

Model sources

Repository: https://github.com/Mystique1337/naija-petro
GGUF (llama.cpp / Ollama): Shinzmann/naija-petro-8b-GGUF
Larger variant (32B): Shinzmann/naija-petro

Uses

Direct use

Technical question answering and explanation across petroleum-engineering subdomains: concepts, equations and derivations, workflow guidance, and terminology — as a study aid and engineering decision-support tool.

Downstream use

Backbone for retrieval-augmented assistants (see the project repo), further domain fine-tuning, or distillation.

Out-of-scope use

Not for autonomous operational, safety-critical, or financial decisions; not a substitute for licensed engineering judgment, official regulations, or field data. General-purpose chat is not its focus.

Bias, risks, and limitations

Trained largely on synthetic data generated from a scraped corpus; it can be confidently wrong ("hallucinate"), especially on numerical specifics and Nigeria-specific regulation/economics.
English only. May reflect biases of its base model and source literature.
Knowledge is static as of training; use the RAG layer for current/local facts.

Recommendation: Always validate outputs with qualified engineers and primary sources before any operational use.

How to get started

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Shinzmann/naija-petro-8b"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

SYSTEM = (
    "You are Naija-Petro, an expert petroleum-engineering AI assistant. Provide "
    "precise, technically accurate answers; include equations, units, and "
    "practical considerations."
)
messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": "Explain the material balance equation for an undersaturated reservoir."},
]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True,
                                 enable_thinking=False, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=512, temperature=0.4, top_p=0.9)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

Ollama (GGUF):

ollama run hf.co/Shinzmann/naija-petro-8b-GGUF:Q4_K_M

Training details

Data

~20,000 instruction–response pairs generated with NVIDIA NeMo Data Designer from a scraped, de-duplicated petroleum corpus (arXiv, Semantic Scholar, OpenAlex, Crossref, DOE/OSTI, PetroWiki, the SLB glossary, EIA, and more), with an LLM-as-judge quality-scoring pass. Pipeline and EDA are in the project notebooks.

Procedure

QLoRA (4-bit NF4) with Unsloth on a single A100 80GB.

Hyperparameter	Value
Base model	Qwen3-8B
Method	QLoRA, 4-bit NF4
LoRA rank / alpha / dropout	32 / 64 / 0.0
Epochs	3
Effective batch size	64 (32 × 2 grad-accum)
Learning rate / scheduler	2e-4 / cosine, 5% warmup
Weight decay	0.01
Max sequence length	2048
Seed	42

Training results (Weights & Biases)

Trained for 3 epochs (669 optimiser steps) on a single A100 80GB.

Metric	Value
Final training loss	≈ 0.83
Final validation loss	≈ 0.86
Epochs / steps	3 / 669

Evaluation

Training converged to a low validation loss (≈ 0.86; see Training results). A downstream 30-question internal benchmark across six subdomains (drilling, reservoir, production, completions, EOR, well testing), scored by an LLM-as-judge on technical accuracy, completeness, and terminology, was also run; the automated judge did not differentiate reliably in that pass, so a corrected evaluation is in progress and task-level scores are not yet reported. Qualitatively, the fine-tuned model produces well-structured, equation- and unit-aware answers in the target domain. Treat all outputs as expert-validated decision support.

Technical specifications

Architecture: Qwen3 (decoder-only transformer), 8B parameters
Adapter: LoRA merged into 16-bit weights; also distributed as GGUF quantizations
Software: Unsloth, 🤗 Transformers, PEFT, TRL, bitsandbytes
Hardware: 1× NVIDIA A100 80GB

Citation

@misc{naijapetro8b2025,
  title  = {Naija-Petro 8B: A Petroleum-Engineering Language Model},
  author = {Naija-Petro project},
  year   = {2025},
  url    = {https://huggingface.co/Shinzmann/naija-petro-8b}
}

Model card contact

Open an issue at https://github.com/Mystique1337/naija-petro.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Shinzmann/naija-petro-8b

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