Instructions to use vericava/gpt2-medium-vericava-posts-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vericava/gpt2-medium-vericava-posts-v3 with Transformers:

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

pipe = pipeline("text-generation", model="vericava/gpt2-medium-vericava-posts-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vericava/gpt2-medium-vericava-posts-v3")
model = AutoModelForCausalLM.from_pretrained("vericava/gpt2-medium-vericava-posts-v3")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vericava/gpt2-medium-vericava-posts-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vericava/gpt2-medium-vericava-posts-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vericava/gpt2-medium-vericava-posts-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vericava/gpt2-medium-vericava-posts-v3

SGLang

How to use vericava/gpt2-medium-vericava-posts-v3 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 "vericava/gpt2-medium-vericava-posts-v3" \
    --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": "vericava/gpt2-medium-vericava-posts-v3",
		"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 "vericava/gpt2-medium-vericava-posts-v3" \
        --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": "vericava/gpt2-medium-vericava-posts-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vericava/gpt2-medium-vericava-posts-v3 with Docker Model Runner:
```
docker model run hf.co/vericava/gpt2-medium-vericava-posts-v3
```

gpt2-medium-vericava-posts-v3

This is a model trained from scratch using the shape of gpt2-medium on a dataset of my posts on the Internet.

It achieves the following results on the evaluation set:

Loss: 6.4732

Model description

It generates text resembling what I post on the Internet.

Intended uses & limitations

CAUTION: It may produce something I'd never say. I do not impose any restriction(s) on the use of this model.

Training and evaluation data

Twitter/X: https://x.com/vericava

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 128
eval_batch_size: 128
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 1024
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.1117	11.1176	100	6.5645
1.461	22.2353	200	5.6692
1.3005	33.3529	300	5.3980
1.1776	44.4706	400	5.2793
1.0325	55.5882	500	5.3445
0.8629	66.7059	600	5.5766
0.6811	77.8235	700	5.8608
0.4943	88.9412	800	6.1404
0.3243	100.0	900	6.4732

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

Downloads last month: -

Safetensors

Model size

0.4B params

Tensor type

F32

vericava
/

gpt2-medium-vericava-posts-v3