Instructions to use Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs with Transformers:

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

pipe = pipeline("text-generation", model="Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs")
model = AutoModelForCausalLM.from_pretrained("Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs

SGLang

How to use Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs 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 "Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs" \
    --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": "Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs",
		"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 "Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs" \
        --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": "Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs with Docker Model Runner:
```
docker model run hf.co/Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs
```

distilroberta-base-finetuned-github_cybersecurity_READMEs

This model is a fine-tuned version of distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.3245

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: 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
1.6676	1.0	23957	1.5030
1.5269	2.0	47914	1.3645
1.4585	3.0	71871	1.3280

Framework versions

Transformers 4.39.3
Pytorch 2.2.1
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 3

Safetensors

Model size

82.2M params

Tensor type

F32

Model tree for Mou11209203/distilroberta-base-finetuned-github_cybersecurity_READMEs

Base model

distilbert/distilroberta-base

Finetuned

(770)

this model