Instructions to use Alamori/GoldenNet-Qwen2.5-3B-QLoRA-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alamori/GoldenNet-Qwen2.5-3B-QLoRA-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Alamori/GoldenNet-Qwen2.5-3B-QLoRA-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Alamori/GoldenNet-Qwen2.5-3B-QLoRA-v1")
model = AutoModelForCausalLM.from_pretrained("Alamori/GoldenNet-Qwen2.5-3B-QLoRA-v1")
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-Qwen2.5-3B-QLoRA-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alamori/GoldenNet-Qwen2.5-3B-QLoRA-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-Qwen2.5-3B-QLoRA-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Alamori/GoldenNet-Qwen2.5-3B-QLoRA-v1

SGLang

How to use Alamori/GoldenNet-Qwen2.5-3B-QLoRA-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-Qwen2.5-3B-QLoRA-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-Qwen2.5-3B-QLoRA-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-Qwen2.5-3B-QLoRA-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-Qwen2.5-3B-QLoRA-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Alamori/GoldenNet-Qwen2.5-3B-QLoRA-v1 with Docker Model Runner:
```
docker model run hf.co/Alamori/GoldenNet-Qwen2.5-3B-QLoRA-v1
```

GoldenNet-Qwen2.5-3B-QLoRA-v1

Model Description

GoldenNet-Qwen2.5-3B-QLoRA-v1 is a QLoRA fine-tuned version of Qwen/Qwen2.5-3B-Instruct specialized for Iraqi Government Correspondence Processing.

This is the larger 3B model variant, offering improved performance over the 0.5B models while remaining efficient for edge deployment.

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	Size	Method	Train Loss	Eval Loss	Training Time
0.5B-QLoRA-v1	0.5B	QLoRA	0.448	0.2998	49s
0.5B-LoRA-v1	0.5B	LoRA	0.496	0.3665	70s
0.5B-Full-v1	0.5B	Full	0.461	0.3636	121s
3B-QLoRA-v1	3B	QLoRA	0.396	0.2521	14min

Training Details

Parameter	Value
Base Model	Qwen/Qwen2.5-3B-Instruct
Fine-tuning Method	QLoRA (4-bit quantization + LoRA)
Quantization	4-bit (bitsandbytes)
LoRA Rank	64
LoRA Alpha	128
LoRA Dropout	0.05
Learning Rate	1e-4
Epochs	3
Batch Size	1 (effective: 16)
Max Sequence Length	1024
Precision	BF16
Total Parameters	3.2B
Trainable Parameters	119.7M (3.7%)
Hardware	NVIDIA RTX 5070 (8GB VRAM)

Loss Progression

Epoch 0.5: 1.143
Epoch 1.0: 0.462
Epoch 1.5: 0.295
Epoch 2.0: 0.244
Epoch 2.5: 0.192
Epoch 3.0: 0.181 (final)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Alamori/GoldenNet-Qwen2.5-3B-QLoRA-v1",
    device_map="auto",
    torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Alamori/GoldenNet-Qwen2.5-3B-QLoRA-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 3B-QLoRA-v1 for best accuracy when you have sufficient VRAM (~6GB for inference)
Use 0.5B-QLoRA-v1 for fast inference on constrained hardware
The 3B model shows ~16% improvement in eval loss over the 0.5B models