Instructions to use tegana/qwen2.5-arabic-finance-news-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tegana/qwen2.5-arabic-finance-news-parser with Transformers:

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

pipe = pipeline("text-generation", model="tegana/qwen2.5-arabic-finance-news-parser")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tegana/qwen2.5-arabic-finance-news-parser")
model = AutoModelForCausalLM.from_pretrained("tegana/qwen2.5-arabic-finance-news-parser")
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 tegana/qwen2.5-arabic-finance-news-parser with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tegana/qwen2.5-arabic-finance-news-parser"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tegana/qwen2.5-arabic-finance-news-parser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tegana/qwen2.5-arabic-finance-news-parser

SGLang

How to use tegana/qwen2.5-arabic-finance-news-parser 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 "tegana/qwen2.5-arabic-finance-news-parser" \
    --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": "tegana/qwen2.5-arabic-finance-news-parser",
		"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 "tegana/qwen2.5-arabic-finance-news-parser" \
        --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": "tegana/qwen2.5-arabic-finance-news-parser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tegana/qwen2.5-arabic-finance-news-parser with Docker Model Runner:
```
docker model run hf.co/tegana/qwen2.5-arabic-finance-news-parser
```

qwen2.5-arabic-finance-news-parser / README.md

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	---
	language:
	- ar
	license: apache-2.0
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	library_name: transformers
	tags:
	- fine-tuned
	- arabic
	- financial-nlp
	- information-extraction
	- lora
	- llama-factory
	datasets:
	- custom
	pipeline_tag: text-generation
	---

	# qwen2.5-arabic-finance-news-parser

	A fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for structured information extraction from Arabic financial news articles.

	## Model Description

	This model was fine-tuned using LoRA via [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) on a dataset of ~2,792 Egyptian stock-market news articles. Given a news article and a JSON output schema, the model extracts structured data such as company name, event type, sentiment, financial figures, and a short summary.

	## Training Details

	\| Setting \| Value \|
	\|---\|---\|
	\| Base model \| Qwen/Qwen2.5-1.5B-Instruct \|
	\| Fine-tuning method \| LoRA (rank 64, all targets) \|
	\| Dataset size \| 2,792 samples (2,700 train / 92 val) \|
	\| Epochs \| 3 \|
	\| Learning rate \| 1e-4 (cosine scheduler) \|
	\| Max sequence length \| 3,500 tokens \|
	\| Hardware \| Kaggle T4 GPU \|

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch, json

	model_id = "tegana/qwen2.5-arabic-finance-news-parser"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id, device_map="auto", torch_dtype=torch.bfloat16
	)

	article = "القاهرة - واصل جهاز مستقبل مصر للتنمية المستدامة..."

	output_scheme = json.dumps({
	"company_name": "اسم الشركة",
	"event_type": "acquisition\|earnings\|dividends\|...",
	"sentiment": "positive\|negative\|neutral",
	"impact_level": "high\|medium\|low",
	"short_summary": "ملخص من 3 إلى 5 جمل"
	}, ensure_ascii=False)

	messages = [
	{"role": "system", "content": (
	"You are a professional Arabic financial news parser.\n"
	"Extract structured information and return ONLY a valid JSON object."
	)},
	{"role": "user", "content": f"## Article:\n{article}\n\n## Output Scheme:\n{output_scheme}\n\n## Output JSON:"}
	]

	text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer([text], return_tensors="pt").to(model.device)

	with torch.no_grad():
	out = model.generate(inputs.input_ids, max_new_tokens=512, do_sample=False)

	out_ids = [o[len(i):] for i, o in zip(inputs.input_ids, out)]
	print(tokenizer.batch_decode(out_ids, skip_special_tokens=True)[0])
	```

	## Supported Event Types

	`earnings` · `capital_increase` · `capital_decrease` · `dividends` · `acquisition` · `sale_of_stake` · `financing` · `project` · `board_decision` · `regulatory_approval` · `analysis_financial` · `stock_exchange_decision` · `other`

	## Limitations

	- Trained primarily on Egyptian stock-market news; may underperform on other Arabic financial dialects.
	- Numerical extraction quality depends on how clearly figures appear in the source text.

	## License

	Apache 2.0 — same as the base model.