Instructions to use temmam/mistral-7b-academic-metadata-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use temmam/mistral-7b-academic-metadata-extractor with Transformers:

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

pipe = pipeline("text-generation", model="temmam/mistral-7b-academic-metadata-extractor")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("temmam/mistral-7b-academic-metadata-extractor")
model = AutoModelForCausalLM.from_pretrained("temmam/mistral-7b-academic-metadata-extractor", 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 temmam/mistral-7b-academic-metadata-extractor with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/temmam/mistral-7b-academic-metadata-extractor

SGLang

How to use temmam/mistral-7b-academic-metadata-extractor 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 "temmam/mistral-7b-academic-metadata-extractor" \
    --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": "temmam/mistral-7b-academic-metadata-extractor",
		"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 "temmam/mistral-7b-academic-metadata-extractor" \
        --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": "temmam/mistral-7b-academic-metadata-extractor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use temmam/mistral-7b-academic-metadata-extractor with Docker Model Runner:
```
docker model run hf.co/temmam/mistral-7b-academic-metadata-extractor
```

Mistral-7B Academic Metadata Extractor

A fine-tuned Mistral-7B-Instruct-v0.3 model for structured metadata extraction from academic documents.

The model extracts metadata from academic text and produces structured JSON output.

Extracted Metadata

The model extracts the following fields:

Title
Keywords
Authors
Affiliations
Publisher
Journal
Publication Year

Performance

The fine-tuned model achieved an overall field-level accuracy of 91.8% on the test set.

Metadata Field	Accuracy
Title	98%
Keywords	85%
Authors	96%
Affiliations	93%
Publisher	90%
Journal	88%
Year	87%
Overall	91.8%

Training Dataset

A dataset of 10,000 academic PDF documents was prepared for the project.

The final dataset was divided into:

Training: 9,000 documents
Validation: 900 documents
Testing: 100 documents

Approximately 3,000 documents were manually reviewed to complete or correct missing metadata fields.

Training Configuration

Parameter	Value
Base Model	Mistral-7B-Instruct-v0.3
Fine-Tuning Method	LoRA
Epochs	3 (early stopping at 1.97)
Batch Size	2
Learning Rate	2e-5
Maximum Sequence Length	1024 tokens
GPU	NVIDIA A100 80 GB
CPUs per Task	6
Framework	Unsloth + Hugging Face

Output Format

The model is designed to return metadata in JSON format:

{
  "title": "",
  "keywords": [],
  "authors": [
    {
      "name": "",
      "affiliations": [""]
    }
  ],
  "publisher": "",
  "year": "",
  "journal": ""
}

Research

This model was developed as part of the research:

Developing AI Agents That Extract Metadata Using The RAG Method With Fine-Tuned Large Language Models

Authors: Tamam Alhasan and Savaş Takan
Affiliation: Artificial Intelligence and Data Engineering Department, Ankara University

The research compared RAG-based metadata extraction with supervised fine-tuning. Mistral-7B-Instruct-v0.3 achieved 74.43% overall accuracy in the RAG setup, while the fine-tuned model improved the overall accuracy to 91.8%.

Intended Use

This model is intended for extracting structured bibliographic metadata from academic and scientific documents.

Potential applications include:

Academic repositories
Digital libraries
Research databases
Automated document indexing
Scientific knowledge management

Limitations

The model was trained and evaluated primarily on English-language academic documents. Performance may vary for documents with unusual layouts, incomplete metadata, OCR errors, or domains significantly different from the training data.

Authors

Tamam Alhasan
Artificial Intelligence and Data Engineering
Ankara University, Türkiye

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Safetensors

Model size

7B params

Tensor type

F16

Model tree for temmam/mistral-7b-academic-metadata-extractor

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Adapter

(853)

this model