Instructions to use beston91/gpt2-xl_ft_mult_25k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beston91/gpt2-xl_ft_mult_25k with Transformers:

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

pipe = pipeline("text-generation", model="beston91/gpt2-xl_ft_mult_25k")

# Load model directly
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("beston91/gpt2-xl_ft_mult_25k")
model = AutoModelWithLMHead.from_pretrained("beston91/gpt2-xl_ft_mult_25k")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use beston91/gpt2-xl_ft_mult_25k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beston91/gpt2-xl_ft_mult_25k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beston91/gpt2-xl_ft_mult_25k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/beston91/gpt2-xl_ft_mult_25k

SGLang

How to use beston91/gpt2-xl_ft_mult_25k 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 "beston91/gpt2-xl_ft_mult_25k" \
    --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": "beston91/gpt2-xl_ft_mult_25k",
		"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 "beston91/gpt2-xl_ft_mult_25k" \
        --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": "beston91/gpt2-xl_ft_mult_25k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use beston91/gpt2-xl_ft_mult_25k with Docker Model Runner:
```
docker model run hf.co/beston91/gpt2-xl_ft_mult_25k
```

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gpt2-xl_ft_mult_25k

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

Loss: 0.5782

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: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100.0
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.99	136	0.6434
No log	1.99	272	0.5941
No log	2.99	408	0.5811
1.1604	3.99	544	0.5782

Framework versions

Transformers 4.17.0
Pytorch 1.10.0+cu111
Datasets 2.0.0
Tokenizers 0.11.6

Perplexity

Score: 47.53093719482422

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