Instructions to use AhmedZaky1/authorship_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AhmedZaky1/authorship_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, "AhmedZaky1/authorship_model")

Transformers

How to use AhmedZaky1/authorship_model with Transformers:

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

pipe = pipeline("text-generation", model="AhmedZaky1/authorship_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AhmedZaky1/authorship_model", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AhmedZaky1/authorship_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AhmedZaky1/authorship_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": "AhmedZaky1/authorship_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AhmedZaky1/authorship_model

SGLang

How to use AhmedZaky1/authorship_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 "AhmedZaky1/authorship_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": "AhmedZaky1/authorship_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 "AhmedZaky1/authorship_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": "AhmedZaky1/authorship_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AhmedZaky1/authorship_model with Docker Model Runner:
```
docker model run hf.co/AhmedZaky1/authorship_model
```

authorship_model

This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on the authorship_train dataset. It achieves the following results on the evaluation set:

Loss: 0.5754

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
distributed_type: multi-GPU
gradient_accumulation_steps: 16
total_train_batch_size: 16
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: 20
num_epochs: 1.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.3515	0.0486	200	0.7383
1.2685	0.0972	400	0.7080
1.2484	0.1458	600	0.6671
1.2335	0.1945	800	0.6473
1.1783	0.2431	1000	0.6368
1.1715	0.2917	1200	0.6287
1.2187	0.3403	1400	0.6172
1.1708	0.3889	1600	0.6101
1.1623	0.4375	1800	0.6024
1.1197	0.4861	2000	0.6001
1.1420	0.5348	2200	0.5934
1.1861	0.5834	2400	0.5901
1.1843	0.6320	2600	0.5874
1.1776	0.6806	2800	0.5833
1.1156	0.7292	3000	0.5814
1.1301	0.7778	3200	0.5785
1.1356	0.8264	3400	0.5773
1.1167	0.8751	3600	0.5761
1.1620	0.9237	3800	0.5755
1.1024	0.9723	4000	0.5754

Framework versions

PEFT 0.18.1
Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

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Qwen/Qwen2.5-1.5B

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Qwen/Qwen2.5-1.5B-Instruct

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