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

Libraries

How to use AdoCleanCode/Fakeddit_real_moderate with Transformers:

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

pipe = pipeline("text-generation", model="AdoCleanCode/Fakeddit_real_moderate")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AdoCleanCode/Fakeddit_real_moderate")
model = AutoModelForCausalLM.from_pretrained("AdoCleanCode/Fakeddit_real_moderate", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AdoCleanCode/Fakeddit_real_moderate with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AdoCleanCode/Fakeddit_real_moderate

SGLang

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

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

Fakeddit_real_moderate / README.md

AdoCleanCode

Model save

1ddaf5e verified 6 months ago

1.62 kB

library_name: transformers
license: mit
base_model: gpt2
tags:
  - generated_from_trainer
model-index:
  - name: Fakeddit_real_moderate
    results: []

Fakeddit_real_moderate

This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 4.8315

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: 16
seed: 42
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: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
5.1562	1.0	14906	4.9737
4.9445	2.0	29812	4.8704
4.7605	3.0	44718	4.8337
4.5901	4.0	59624	4.8243
4.5575	5.0	74530	4.8315

Framework versions

Transformers 4.46.3
Pytorch 2.1.2+cu121
Datasets 2.19.1
Tokenizers 0.20.3