Instructions to use seccret444/qwen-skripsi-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use seccret444/qwen-skripsi-v1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "seccret444/qwen-skripsi-v1")

Transformers

How to use seccret444/qwen-skripsi-v1 with Transformers:

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

pipe = pipeline("text-generation", model="seccret444/qwen-skripsi-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("seccret444/qwen-skripsi-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use seccret444/qwen-skripsi-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "seccret444/qwen-skripsi-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "seccret444/qwen-skripsi-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/seccret444/qwen-skripsi-v1

SGLang

How to use seccret444/qwen-skripsi-v1 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 "seccret444/qwen-skripsi-v1" \
    --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": "seccret444/qwen-skripsi-v1",
		"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 "seccret444/qwen-skripsi-v1" \
        --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": "seccret444/qwen-skripsi-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use seccret444/qwen-skripsi-v1 with Docker Model Runner:
```
docker model run hf.co/seccret444/qwen-skripsi-v1
```

qwen-skripsi-v1

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

Loss: 0.1767

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: 0.0002
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 16
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.5668	0.3968	50	0.5044
0.4804	0.7937	100	0.3970
0.302	1.1905	150	0.2971
0.2426	1.5873	200	0.2380
0.1924	1.9841	250	0.1977
0.2158	2.3810	300	0.1855
0.1642	2.7778	350	0.1767

Framework versions

PEFT 0.18.0
Transformers 4.57.1
Pytorch 2.9.1+cu126
Datasets 4.4.1
Tokenizers 0.22.1

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Model tree for seccret444/qwen-skripsi-v1

Base model

Qwen/Qwen2.5-1.5B

Finetuned

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Finetuned

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