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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 / README.md

AhmedZaky1

End of training

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	---
	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: authorship_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. -->

	# authorship_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 authorship_train dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4835

	## 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: 5e-05
	- train_batch_size: 1
	- eval_batch_size: 1
	- seed: 42
	- gradient_accumulation_steps: 8
	- total_train_batch_size: 8
	- 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: 3.0
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|
	\| 1.3347 \| 0.0528 \| 500 \| 0.6354 \|
	\| 1.3194 \| 0.1057 \| 1000 \| 0.6109 \|
	\| 1.2027 \| 0.1585 \| 1500 \| 0.5906 \|
	\| 1.2360 \| 0.2114 \| 2000 \| 0.5835 \|
	\| 1.2672 \| 0.2642 \| 2500 \| 0.5752 \|
	\| 1.1666 \| 0.3171 \| 3000 \| 0.5580 \|
	\| 1.1543 \| 0.3699 \| 3500 \| 0.5537 \|
	\| 1.1933 \| 0.4227 \| 4000 \| 0.5499 \|
	\| 1.2075 \| 0.4756 \| 4500 \| 0.5418 \|
	\| 1.0687 \| 0.5284 \| 5000 \| 0.5443 \|
	\| 1.0309 \| 0.5813 \| 5500 \| 0.5371 \|
	\| 0.9416 \| 0.6341 \| 6000 \| 0.5339 \|
	\| 1.1705 \| 0.6869 \| 6500 \| 0.5248 \|
	\| 0.9056 \| 0.7398 \| 7000 \| 0.5215 \|
	\| 1.0791 \| 0.7926 \| 7500 \| 0.5182 \|
	\| 1.0082 \| 0.8455 \| 8000 \| 0.5137 \|
	\| 1.1300 \| 0.8983 \| 8500 \| 0.5123 \|
	\| 1.0804 \| 0.9512 \| 9000 \| 0.5095 \|
	\| 0.9295 \| 1.0039 \| 9500 \| 0.5073 \|
	\| 0.9995 \| 1.0568 \| 10000 \| 0.5065 \|
	\| 1.0430 \| 1.1096 \| 10500 \| 0.5050 \|
	\| 1.0754 \| 1.1624 \| 11000 \| 0.5025 \|
	\| 1.0258 \| 1.2153 \| 11500 \| 0.5010 \|
	\| 1.0720 \| 1.2681 \| 12000 \| 0.4990 \|
	\| 1.0141 \| 1.3210 \| 12500 \| 0.4977 \|
	\| 0.9102 \| 1.3738 \| 13000 \| 0.4960 \|
	\| 1.0301 \| 1.4266 \| 13500 \| 0.4951 \|
	\| 0.8990 \| 1.4795 \| 14000 \| 0.4934 \|
	\| 1.0046 \| 1.5323 \| 14500 \| 0.4922 \|
	\| 0.8761 \| 1.5852 \| 15000 \| 0.4909 \|
	\| 1.0435 \| 1.6380 \| 15500 \| 0.4897 \|
	\| 0.9703 \| 1.6909 \| 16000 \| 0.4875 \|
	\| 0.8901 \| 1.7437 \| 16500 \| 0.4857 \|
	\| 0.9523 \| 1.7965 \| 17000 \| 0.4855 \|
	\| 0.9663 \| 1.8494 \| 17500 \| 0.4838 \|
	\| 0.9741 \| 1.9022 \| 18000 \| 0.4831 \|
	\| 0.9686 \| 1.9551 \| 18500 \| 0.4817 \|
	\| 0.8208 \| 2.0078 \| 19000 \| 0.4860 \|
	\| 0.9067 \| 2.0607 \| 19500 \| 0.4867 \|
	\| 0.8943 \| 2.1135 \| 20000 \| 0.4873 \|
	\| 0.9204 \| 2.1663 \| 20500 \| 0.4863 \|
	\| 0.8343 \| 2.2192 \| 21000 \| 0.4863 \|
	\| 0.8542 \| 2.2720 \| 21500 \| 0.4871 \|
	\| 0.9198 \| 2.3249 \| 22000 \| 0.4863 \|
	\| 0.8754 \| 2.3777 \| 22500 \| 0.4859 \|
	\| 0.8645 \| 2.4306 \| 23000 \| 0.4852 \|
	\| 0.8500 \| 2.4834 \| 23500 \| 0.4841 \|
	\| 0.8699 \| 2.5362 \| 24000 \| 0.4842 \|
	\| 0.8449 \| 2.5891 \| 24500 \| 0.4841 \|
	\| 0.8593 \| 2.6419 \| 25000 \| 0.4841 \|
	\| 0.7919 \| 2.6948 \| 25500 \| 0.4837 \|
	\| 0.7707 \| 2.7476 \| 26000 \| 0.4841 \|
	\| 0.8306 \| 2.8005 \| 26500 \| 0.4839 \|
	\| 0.7969 \| 2.8533 \| 27000 \| 0.4837 \|
	\| 0.9231 \| 2.9061 \| 27500 \| 0.4837 \|
	\| 0.9109 \| 2.9590 \| 28000 \| 0.4836 \|


	### Framework versions

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

	---
	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: authorship_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. -->

	# authorship_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 authorship_train dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4835

	## 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: 5e-05
	- train_batch_size: 1
	- eval_batch_size: 1
	- seed: 42
	- gradient_accumulation_steps: 8
	- total_train_batch_size: 8
	- 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: 3.0
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|
	\| 1.3347 \| 0.0528 \| 500 \| 0.6354 \|
	\| 1.3194 \| 0.1057 \| 1000 \| 0.6109 \|
	\| 1.2027 \| 0.1585 \| 1500 \| 0.5906 \|
	\| 1.2360 \| 0.2114 \| 2000 \| 0.5835 \|
	\| 1.2672 \| 0.2642 \| 2500 \| 0.5752 \|
	\| 1.1666 \| 0.3171 \| 3000 \| 0.5580 \|
	\| 1.1543 \| 0.3699 \| 3500 \| 0.5537 \|
	\| 1.1933 \| 0.4227 \| 4000 \| 0.5499 \|
	\| 1.2075 \| 0.4756 \| 4500 \| 0.5418 \|
	\| 1.0687 \| 0.5284 \| 5000 \| 0.5443 \|
	\| 1.0309 \| 0.5813 \| 5500 \| 0.5371 \|
	\| 0.9416 \| 0.6341 \| 6000 \| 0.5339 \|
	\| 1.1705 \| 0.6869 \| 6500 \| 0.5248 \|
	\| 0.9056 \| 0.7398 \| 7000 \| 0.5215 \|
	\| 1.0791 \| 0.7926 \| 7500 \| 0.5182 \|
	\| 1.0082 \| 0.8455 \| 8000 \| 0.5137 \|
	\| 1.1300 \| 0.8983 \| 8500 \| 0.5123 \|
	\| 1.0804 \| 0.9512 \| 9000 \| 0.5095 \|
	\| 0.9295 \| 1.0039 \| 9500 \| 0.5073 \|
	\| 0.9995 \| 1.0568 \| 10000 \| 0.5065 \|
	\| 1.0430 \| 1.1096 \| 10500 \| 0.5050 \|
	\| 1.0754 \| 1.1624 \| 11000 \| 0.5025 \|
	\| 1.0258 \| 1.2153 \| 11500 \| 0.5010 \|
	\| 1.0720 \| 1.2681 \| 12000 \| 0.4990 \|
	\| 1.0141 \| 1.3210 \| 12500 \| 0.4977 \|
	\| 0.9102 \| 1.3738 \| 13000 \| 0.4960 \|
	\| 1.0301 \| 1.4266 \| 13500 \| 0.4951 \|
	\| 0.8990 \| 1.4795 \| 14000 \| 0.4934 \|
	\| 1.0046 \| 1.5323 \| 14500 \| 0.4922 \|
	\| 0.8761 \| 1.5852 \| 15000 \| 0.4909 \|
	\| 1.0435 \| 1.6380 \| 15500 \| 0.4897 \|
	\| 0.9703 \| 1.6909 \| 16000 \| 0.4875 \|
	\| 0.8901 \| 1.7437 \| 16500 \| 0.4857 \|
	\| 0.9523 \| 1.7965 \| 17000 \| 0.4855 \|
	\| 0.9663 \| 1.8494 \| 17500 \| 0.4838 \|
	\| 0.9741 \| 1.9022 \| 18000 \| 0.4831 \|
	\| 0.9686 \| 1.9551 \| 18500 \| 0.4817 \|
	\| 0.8208 \| 2.0078 \| 19000 \| 0.4860 \|
	\| 0.9067 \| 2.0607 \| 19500 \| 0.4867 \|
	\| 0.8943 \| 2.1135 \| 20000 \| 0.4873 \|
	\| 0.9204 \| 2.1663 \| 20500 \| 0.4863 \|
	\| 0.8343 \| 2.2192 \| 21000 \| 0.4863 \|
	\| 0.8542 \| 2.2720 \| 21500 \| 0.4871 \|
	\| 0.9198 \| 2.3249 \| 22000 \| 0.4863 \|
	\| 0.8754 \| 2.3777 \| 22500 \| 0.4859 \|
	\| 0.8645 \| 2.4306 \| 23000 \| 0.4852 \|
	\| 0.8500 \| 2.4834 \| 23500 \| 0.4841 \|
	\| 0.8699 \| 2.5362 \| 24000 \| 0.4842 \|
	\| 0.8449 \| 2.5891 \| 24500 \| 0.4841 \|
	\| 0.8593 \| 2.6419 \| 25000 \| 0.4841 \|
	\| 0.7919 \| 2.6948 \| 25500 \| 0.4837 \|
	\| 0.7707 \| 2.7476 \| 26000 \| 0.4841 \|
	\| 0.8306 \| 2.8005 \| 26500 \| 0.4839 \|
	\| 0.7969 \| 2.8533 \| 27000 \| 0.4837 \|
	\| 0.9231 \| 2.9061 \| 27500 \| 0.4837 \|
	\| 0.9109 \| 2.9590 \| 28000 \| 0.4836 \|


	### Framework versions

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