Instructions to use Shahdddddd/news-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shahdddddd/news-analyzer 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, "Shahdddddd/news-analyzer")

Transformers

How to use Shahdddddd/news-analyzer with Transformers:

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

pipe = pipeline("text-generation", model="Shahdddddd/news-analyzer")
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("Shahdddddd/news-analyzer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Shahdddddd/news-analyzer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Shahdddddd/news-analyzer

SGLang

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

Docker Model Runner
How to use Shahdddddd/news-analyzer with Docker Model Runner:
```
docker model run hf.co/Shahdddddd/news-analyzer
```

news-analyzer

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

Loss: 0.3888

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
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 8
total_eval_batch_size: 2
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_ratio: 0.1
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.4231	0.2963	100	0.4306
0.4024	0.5926	200	0.3985
0.4269	0.8889	300	0.3828
0.305	1.1837	400	0.3812
0.2767	1.48	500	0.3802
0.2204	1.7763	600	0.3886
0.1858	2.0741	700	0.3927
0.2011	2.3704	800	0.3918
0.1861	2.6667	900	0.3911
0.1971	2.9630	1000	0.3887

Framework versions

PEFT 0.18.1
Transformers 4.56.1
Pytorch 2.10.0+cu128
Datasets 3.2.0
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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