Instructions to use MHGanainy/gpt2-xl-lora-multi-shared-512-top with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MHGanainy/gpt2-xl-lora-multi-shared-512-top with Transformers:

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

pipe = pipeline("text-generation", model="MHGanainy/gpt2-xl-lora-multi-shared-512-top")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MHGanainy/gpt2-xl-lora-multi-shared-512-top")
model = AutoModelForCausalLM.from_pretrained("MHGanainy/gpt2-xl-lora-multi-shared-512-top", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MHGanainy/gpt2-xl-lora-multi-shared-512-top with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MHGanainy/gpt2-xl-lora-multi-shared-512-top

SGLang

How to use MHGanainy/gpt2-xl-lora-multi-shared-512-top 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 "MHGanainy/gpt2-xl-lora-multi-shared-512-top" \
    --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": "MHGanainy/gpt2-xl-lora-multi-shared-512-top",
		"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 "MHGanainy/gpt2-xl-lora-multi-shared-512-top" \
        --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": "MHGanainy/gpt2-xl-lora-multi-shared-512-top",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MHGanainy/gpt2-xl-lora-multi-shared-512-top with Docker Model Runner:
```
docker model run hf.co/MHGanainy/gpt2-xl-lora-multi-shared-512-top
```

gpt2-xl-lora-multi-shared-512-top

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

eval_loss: 2.9468
eval_model_preparation_time: 0.0024
eval_runtime: 1258.8862
eval_samples_per_second: 150.453
eval_steps_per_second: 75.227
step: 0

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: 2
eval_batch_size: 2
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: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1
mixed_precision_training: Native AMP

Framework versions

Transformers 4.47.1
Pytorch 2.1.0a0+32f93b1
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 7

Safetensors

Model size

0.1B params

Tensor type

F16

Model tree for MHGanainy/gpt2-xl-lora-multi-shared-512-top

Base model

openai-community/gpt2

Finetuned

(2262)

this model