Instructions to use Gan1108/robertaForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gan1108/robertaForCausalLM with Transformers:

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

pipe = pipeline("text-generation", model="Gan1108/robertaForCausalLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Gan1108/robertaForCausalLM")
model = AutoModelForCausalLM.from_pretrained("Gan1108/robertaForCausalLM")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Gan1108/robertaForCausalLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gan1108/robertaForCausalLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gan1108/robertaForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Gan1108/robertaForCausalLM

SGLang

How to use Gan1108/robertaForCausalLM 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 "Gan1108/robertaForCausalLM" \
    --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": "Gan1108/robertaForCausalLM",
		"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 "Gan1108/robertaForCausalLM" \
        --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": "Gan1108/robertaForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Gan1108/robertaForCausalLM with Docker Model Runner:
```
docker model run hf.co/Gan1108/robertaForCausalLM
```

robertaForCausalLM

This model is a fine-tuned version of ukr-models/xlm-roberta-base-uk on the Gan1108/agriparts dataset. It achieves the following results on the evaluation set:

Loss: 1.0242

Model description

The model is trained to generate a text description in Ukrainian of spare parts for agricultural machinery based on their name.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.3574	1.0	2495	1.1717
1.1399	2.0	4990	1.0587
1.0802	3.0	7485	1.0242

Framework versions

Transformers 4.37.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.1

Downloads last month: 3

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for Gan1108/robertaForCausalLM

Base model

ukr-models/xlm-roberta-base-uk

Finetuned

(6)

this model