Instructions to use Alamori/GoldenNet-Qwen2.5-0.5B-Full-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-Full-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-Full-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Alamori/GoldenNet-Qwen2.5-0.5B-Full-v1")
model = AutoModelForCausalLM.from_pretrained("Alamori/GoldenNet-Qwen2.5-0.5B-Full-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=256)
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-Full-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-Full-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-Full-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

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

Model Description

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

This is the full fine-tuning variant where all 494M parameters were trained, potentially offering the best task-specific performance.

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	Size
QLoRA-v1	4-bit QLoRA	0.448	0.2998	49s	943MB
LoRA-v1	Standard LoRA	0.496	0.3665	70s	943MB
Full-v1	Full Fine-tune	0.461	0.3636	121s	1.9GB

Training Details

Parameter	Value
Base Model	Qwen/Qwen2.5-0.5B-Instruct
Fine-tuning Method	Full (all parameters)
Learning Rate	5e-5
Optimizer	AdamW 8-bit
Epochs	3
Batch Size	1 (effective: 16)
Max Sequence Length	1024
Precision	BF16
Trainable Parameters	494M (100%)
Hardware	NVIDIA RTX 5070 (8GB VRAM)

Loss Progression

Epoch 1: 0.983
Epoch 2: 0.328
Epoch 3: 0.171 (lowest among all variants!)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

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

When to Use This Model

Use Full-v1 when you need maximum task-specific performance and have sufficient storage/memory
Use QLoRA-v1 for best balance of quality and efficiency (recommended for most cases)
Use LoRA-v1 for comparison or when you need standard LoRA compatibility