Instructions to use thisismyusername123/gpt_tos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thisismyusername123/gpt_tos with Transformers:

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

pipe = pipeline("text-generation", model="thisismyusername123/gpt_tos")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thisismyusername123/gpt_tos")
model = AutoModelForCausalLM.from_pretrained("thisismyusername123/gpt_tos", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thisismyusername123/gpt_tos with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thisismyusername123/gpt_tos"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thisismyusername123/gpt_tos",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/thisismyusername123/gpt_tos

SGLang

How to use thisismyusername123/gpt_tos 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 "thisismyusername123/gpt_tos" \
    --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": "thisismyusername123/gpt_tos",
		"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 "thisismyusername123/gpt_tos" \
        --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": "thisismyusername123/gpt_tos",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use thisismyusername123/gpt_tos with Docker Model Runner:
```
docker model run hf.co/thisismyusername123/gpt_tos
```

gpt_tos

This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 5.6644

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-06
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
lr_scheduler_warmup_steps: 500
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss
8.4766	0.62	1000	7.0356
6.6906	1.24	2000	6.4523
6.3735	1.86	3000	6.2716
6.2028	2.48	4000	6.1586
6.1092	3.09	5000	6.0782
6.0136	3.71	6000	6.0102
5.9325	4.33	7000	5.9612
5.8906	4.95	8000	5.9175
5.8132	5.57	9000	5.8860
5.7793	6.19	10000	5.8571
5.7357	6.81	11000	5.8311
5.6976	7.43	12000	5.8051
5.6553	8.04	13000	5.7859
5.6192	8.66	14000	5.7713
5.591	9.28	15000	5.7608
5.5728	9.9	16000	5.7453
5.5361	10.52	17000	5.7339
5.5166	11.14	18000	5.7259
5.5008	11.76	19000	5.7151
5.47	12.38	20000	5.7067
5.4621	13.0	21000	5.7004
5.4327	13.61	22000	5.6935
5.4219	14.23	23000	5.6876
5.4101	14.85	24000	5.6832
5.3975	15.47	25000	5.6778
5.3848	16.09	26000	5.6751
5.3707	16.71	27000	5.6726
5.3582	17.33	28000	5.6695
5.3569	17.95	29000	5.6667
5.3451	18.56	30000	5.6663
5.3487	19.18	31000	5.6652
5.3335	19.8	32000	5.6644

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3

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