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 / README.md

YousefBarakat

End of training

65fce91 verified 5 months ago

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metadata

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: []

Model

This model is a fine-tuned version of 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