Instructions to use YousefBarakat/Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YousefBarakat/Model with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "YousefBarakat/Model")

Transformers

How to use YousefBarakat/Model with Transformers:

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

pipe = pipeline("text-generation", model="YousefBarakat/Model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("YousefBarakat/Model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use YousefBarakat/Model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/YousefBarakat/Model

SGLang

How to use YousefBarakat/Model 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 "YousefBarakat/Model" \
    --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": "YousefBarakat/Model",
		"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 "YousefBarakat/Model" \
        --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": "YousefBarakat/Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use YousefBarakat/Model with Docker Model Runner:
```
docker model run hf.co/YousefBarakat/Model
```

Model

File size: 2,020 Bytes

---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
- llama-factory
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: Model
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Model

This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the finance_qa_train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3503

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7185        | 0.1221 | 50   | 0.4380          |
| 0.9345        | 0.2442 | 100  | 0.4109          |
| 0.3584        | 0.3663 | 150  | 0.3940          |
| 0.6376        | 0.4884 | 200  | 0.3822          |
| 0.7135        | 0.6105 | 250  | 0.3672          |
| 0.5756        | 0.7326 | 300  | 0.3557          |
| 0.4457        | 0.8547 | 350  | 0.3516          |
| 0.5859        | 0.9768 | 400  | 0.3502          |
| 0.6935        | 1.0    | 410  | 0.3503          |


### Framework versions

- PEFT 0.18.1
- Transformers 5.6.0
- Pytorch 2.11.0+cu128
- Datasets 3.2.0
- Tokenizers 0.22.2