Instructions to use gjonesQ02/WO_CausalModel_2x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gjonesQ02/WO_CausalModel_2x with Transformers:

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

pipe = pipeline("text-generation", model="gjonesQ02/WO_CausalModel_2x")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gjonesQ02/WO_CausalModel_2x")
model = AutoModelForCausalLM.from_pretrained("gjonesQ02/WO_CausalModel_2x")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use gjonesQ02/WO_CausalModel_2x with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gjonesQ02/WO_CausalModel_2x"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gjonesQ02/WO_CausalModel_2x",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gjonesQ02/WO_CausalModel_2x

SGLang

How to use gjonesQ02/WO_CausalModel_2x 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 "gjonesQ02/WO_CausalModel_2x" \
    --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": "gjonesQ02/WO_CausalModel_2x",
		"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 "gjonesQ02/WO_CausalModel_2x" \
        --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": "gjonesQ02/WO_CausalModel_2x",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gjonesQ02/WO_CausalModel_2x with Docker Model Runner:
```
docker model run hf.co/gjonesQ02/WO_CausalModel_2x
```

WO_CausalModel_2x

This model is a fine-tuned version of distilgpt2 on the generator dataset. It achieves the following results on the evaluation set:

Loss: 0.6035

Model description

It is focused on generating realistic WO descriptions when prompted with a given WO's priority, activity type, maintenance type, and location.

Intended uses & limitations

This is a proof of concept model for a larger project.

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	151	0.6217
No log	2.0	302	0.6133
No log	3.0	453	0.6087
0.6243	4.0	604	0.6079
0.6243	5.0	755	0.6049
0.6243	6.0	906	0.6035

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.17.1
Tokenizers 0.15.2

Downloads last month: 3

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for gjonesQ02/WO_CausalModel_2x

Base model

distilbert/distilgpt2

Finetuned

(1456)

this model