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", 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=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
```

Shinzmann commited on 20 days ago

Commit

dc72181

verified ·

1 Parent(s): aa87b19

Update model card (standardized)

Browse files

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -17,7 +17,7 @@ tags:
 # Naija-Petro 8B
-**Naija-Petro 8B** is a [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) model fine-tuned (QLoRA, [Unsloth](https://github.com/unslothai/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](https://github.com/Mystique1337/naija-petro), which grounds answers in verifiable Nigerian sources.
@@ -28,7 +28,7 @@ tags:
 - **Language:** English
 - **License:** Apache-2.0 (inherited from Qwen3-8B)
 - **Finetuned from:** [`Qwen/Qwen3-8B`](https://huggingface.co/Qwen/Qwen3-8B)
-- **Domain:** Petroleum engineering — drilling, reservoir, production, completions, EOR, well testing, petroleum geoscience
 ### Model sources
 - **Repository:** https://github.com/Mystique1337/naija-petro
@@ -38,7 +38,7 @@ tags:
 ## 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.
@@ -85,7 +85,7 @@ 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](https://github.com/Mystique1337/naija-petro/tree/main/notebooks).
 ### Procedure
 QLoRA (4-bit NF4) with Unsloth on a single A100 80GB.

 # Naija-Petro 8B
+**Naija-Petro 8B** is a [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) model fine-tuned (QLoRA, [Unsloth](https://github.com/unslothai/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](https://github.com/Mystique1337/naija-petro), which grounds answers in verifiable Nigerian sources.
 - **Language:** English
 - **License:** Apache-2.0 (inherited from Qwen3-8B)
 - **Finetuned from:** [`Qwen/Qwen3-8B`](https://huggingface.co/Qwen/Qwen3-8B)
+- **Domain:** Petroleum engineering covering drilling, reservoir, production, completions, EOR, well testing, petroleum geoscience
 ### Model sources
 - **Repository:** https://github.com/Mystique1337/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.
 ## 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](https://github.com/Mystique1337/naija-petro/tree/main/notebooks).
 ### Procedure
 QLoRA (4-bit NF4) with Unsloth on a single A100 80GB.