Instructions to use Mecharnia/Llama-KGQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mecharnia/Llama-KGQA with Transformers:

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

pipe = pipeline("text-generation", model="Mecharnia/Llama-KGQA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mecharnia/Llama-KGQA")
model = AutoModelForCausalLM.from_pretrained("Mecharnia/Llama-KGQA")
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 Mecharnia/Llama-KGQA with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Mecharnia/Llama-KGQA

SGLang

How to use Mecharnia/Llama-KGQA 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 "Mecharnia/Llama-KGQA" \
    --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": "Mecharnia/Llama-KGQA",
		"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 "Mecharnia/Llama-KGQA" \
        --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": "Mecharnia/Llama-KGQA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Mecharnia/Llama-KGQA with Docker Model Runner:
```
docker model run hf.co/Mecharnia/Llama-KGQA
```

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Llama-KGQA

Llama-KGQA is a fine-tuned model designed for question answering (QA) over knowledge graphs (KGs). This model translates natural language (NL) questions into SPARQL queries, enabling efficient querying of structured knowledge bases like DBpedia and Wikidata.

Model Overview

Base Model: The fine-tuning is performed on Meta-Llama-3-8B-Instruct model with 6 epochs.
Dataset: The model was fine-tuned using the QALD benchmark datasets, this version is trained on QALD-9-plus-DBpedia.
Objective: Enable natural language interfaces to query knowledge graphs.

Usage

You can use the translate.py script provided in the GitHub repository.

python translate.py "[NATURAL_LANGUAGE_QUESTION]"

Example:

python translate.py "What is the capital of France?"

Example Output

Input:

What is the capital of France?

Output:

PREFIX dbo: <http://dbpedia.org/ontology/>
PREFIX res: <http://dbpedia.org/resource/>
SELECT DISTINCT?uri WHERE {
res:France dbo:capital?uri
}

Fine-Tuning

If you would like to fine-tune the model on your own dataset, you can use the main_llama_kgqa.py script provided in the GitHub repository.

Evaluation

The model has been evaluated on QALD-9-plus-DBpedia and QALD-10-Wikidata datasets. Detailed results can be found in the GitHub repository.

License

This model is licensed under the MIT License. See the GitHub repository for more details.

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Tensor type

F16

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Llama-KGQA