Instructions to use xiulinyang/GPT2_RU_21042 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xiulinyang/GPT2_RU_21042 with Transformers:

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

pipe = pipeline("text-generation", model="xiulinyang/GPT2_RU_21042")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xiulinyang/GPT2_RU_21042")
model = AutoModelForCausalLM.from_pretrained("xiulinyang/GPT2_RU_21042", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xiulinyang/GPT2_RU_21042 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xiulinyang/GPT2_RU_21042"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xiulinyang/GPT2_RU_21042",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/xiulinyang/GPT2_RU_21042

SGLang

How to use xiulinyang/GPT2_RU_21042 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 "xiulinyang/GPT2_RU_21042" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xiulinyang/GPT2_RU_21042",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "xiulinyang/GPT2_RU_21042" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xiulinyang/GPT2_RU_21042",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use xiulinyang/GPT2_RU_21042 with Docker Model Runner:
```
docker model run hf.co/xiulinyang/GPT2_RU_21042
```

RU_21042_41

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 4.4379
Accuracy: 0.2986

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: 0.0006
train_batch_size: 32
eval_batch_size: 32
seed: 41
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 10.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	894	5.8006	0.1827
1.4587	2.0	1788	5.3948	0.2003
1.2604	3.0	2682	5.0759	0.2316
1.1611	4.0	3576	4.8898	0.2487
1.1035	5.0	4470	4.7652	0.2602
1.0629	6.0	5364	4.6752	0.2719
1.0269	7.0	6258	4.5901	0.2792
0.9986	8.0	7152	4.5173	0.2880
0.9729	9.0	8046	4.4658	0.2945
0.9729	10.0	8940	4.4379	0.2986

Framework versions

Transformers 4.51.3
Pytorch 2.5.1+cu121
Datasets 3.6.0
Tokenizers 0.21.1

Downloads last month: 39

Safetensors

Model size

0.1B params

Tensor type

F32

xiulinyang
/

GPT2_RU_21042

RU_21042_41

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

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