Instructions to use Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1")
model = AutoModelForCausalLM.from_pretrained("Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1", 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 Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1

SGLang

How to use Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1 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 "Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1" \
    --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": "Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1",
		"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 "Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1" \
        --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": "Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1 with Docker Model Runner:
```
docker model run hf.co/Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1
```

GoldenNet-Qwen2.5-0.5B-LoRA-v1

Model Description

GoldenNet-Qwen2.5-0.5B-LoRA-v1 is a LoRA fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct specialized for Iraqi Government Correspondence Processing.

This is the standard LoRA variant (no quantization), offering potentially better quality than QLoRA at the cost of higher VRAM usage during training.

Tasks

Document Classification - 8 categories (طلب، شكوى، تقرير، إعلام، استفسار، دعوة، تعميم، إحالة)
Named Entity Recognition - Extracts persons, organizations, locations, dates, monetary values, laws

Model Comparison

Model	Method	Train Loss	Eval Loss	Training Time
QLoRA-v1	4-bit QLoRA	0.448	0.2998	49s
LoRA-v1	Standard LoRA	0.496	0.3665	70s

Training Details

Parameter	Value
Base Model	Qwen/Qwen2.5-0.5B-Instruct
Fine-tuning Method	LoRA (no quantization)
LoRA Rank	64
LoRA Alpha	128
LoRA Dropout	0.05
Learning Rate	1e-4
Epochs	3
Batch Size	1 (effective: 16)
Max Sequence Length	2048
Precision	BF16
Trainable Parameters	35.2M (6.6%)

Loss Progression

Epoch 1: 0.979
Epoch 2: 0.350
Epoch 3: 0.247

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1",
    device_map="auto",
    torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1")

# Classification example
correspondence = """جمهورية العراق
وزارة التربية
العدد: 1234/ت/2025

إلى/ السيد مدير عام التعليم المحترم

م/ طلب تعيين معلمين

نرجو الموافقة على تعيين 50 معلماً.

مع التقدير"""

instruction = "صنّف المراسلة الحكومية التالية إلى إحدى الفئات: طلب، شكوى، تقرير، إعلام، استفسار، دعوة، تعميم، إحالة. أجب بصيغة JSON."

messages = [{"role": "user", "content": f"{instruction}\n\n{correspondence}"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1)
print(tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))

Related Models

GoldenNet-Qwen2.5-0.5B-QLoRA-v1 - 4-bit quantized version

License

Apache 2.0

Developed by Golden Net AI
Empowering Iraqi Government Digital Transformation

Downloads last month: 11

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Adapter

(729)

this model

Evaluation results

Eval Loss
self-reported

0.366