Instructions to use gjonesQ02/StatementOfWork_Generator_Omega2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gjonesQ02/StatementOfWork_Generator_Omega2 with Transformers:

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

pipe = pipeline("text-generation", model="gjonesQ02/StatementOfWork_Generator_Omega2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gjonesQ02/StatementOfWork_Generator_Omega2")
model = AutoModelForCausalLM.from_pretrained("gjonesQ02/StatementOfWork_Generator_Omega2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gjonesQ02/StatementOfWork_Generator_Omega2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gjonesQ02/StatementOfWork_Generator_Omega2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gjonesQ02/StatementOfWork_Generator_Omega2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gjonesQ02/StatementOfWork_Generator_Omega2

SGLang

How to use gjonesQ02/StatementOfWork_Generator_Omega2 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/StatementOfWork_Generator_Omega2" \
    --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/StatementOfWork_Generator_Omega2",
		"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/StatementOfWork_Generator_Omega2" \
        --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/StatementOfWork_Generator_Omega2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gjonesQ02/StatementOfWork_Generator_Omega2 with Docker Model Runner:
```
docker model run hf.co/gjonesQ02/StatementOfWork_Generator_Omega2
```

StatementOfWork_Generator_Omega2 / README.md

gjonesQ02

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	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	base_model: distilgpt2
	model-index:
	- name: StatementOfWork_Generator_Omega2
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# StatementOfWork_Generator_Omega2

	This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.9436

	## 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: 50
	- eval_batch_size: 50
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|
	\| No log \| 1.0 \| 15 \| 0.9674 \|
	\| No log \| 2.0 \| 30 \| 0.9673 \|
	\| No log \| 3.0 \| 45 \| 0.9633 \|
	\| No log \| 4.0 \| 60 \| 0.9629 \|
	\| No log \| 5.0 \| 75 \| 0.9633 \|
	\| No log \| 6.0 \| 90 \| 0.9634 \|
	\| No log \| 7.0 \| 105 \| 0.9635 \|
	\| No log \| 8.0 \| 120 \| 0.9603 \|
	\| No log \| 9.0 \| 135 \| 0.9550 \|
	\| No log \| 10.0 \| 150 \| 0.9583 \|
	\| No log \| 11.0 \| 165 \| 0.9574 \|
	\| No log \| 12.0 \| 180 \| 0.9544 \|
	\| No log \| 13.0 \| 195 \| 0.9540 \|
	\| No log \| 14.0 \| 210 \| 0.9575 \|
	\| No log \| 15.0 \| 225 \| 0.9530 \|
	\| No log \| 16.0 \| 240 \| 0.9519 \|
	\| No log \| 17.0 \| 255 \| 0.9514 \|
	\| No log \| 18.0 \| 270 \| 0.9534 \|
	\| No log \| 19.0 \| 285 \| 0.9498 \|
	\| No log \| 20.0 \| 300 \| 0.9554 \|
	\| No log \| 21.0 \| 315 \| 0.9474 \|
	\| No log \| 22.0 \| 330 \| 0.9539 \|
	\| No log \| 23.0 \| 345 \| 0.9470 \|
	\| No log \| 24.0 \| 360 \| 0.9491 \|
	\| No log \| 25.0 \| 375 \| 0.9478 \|
	\| No log \| 26.0 \| 390 \| 0.9454 \|
	\| No log \| 27.0 \| 405 \| 0.9472 \|
	\| No log \| 28.0 \| 420 \| 0.9481 \|
	\| No log \| 29.0 \| 435 \| 0.9467 \|
	\| No log \| 30.0 \| 450 \| 0.9473 \|
	\| No log \| 31.0 \| 465 \| 0.9478 \|
	\| No log \| 32.0 \| 480 \| 0.9439 \|
	\| No log \| 33.0 \| 495 \| 0.9453 \|
	\| 0.2954 \| 34.0 \| 510 \| 0.9446 \|
	\| 0.2954 \| 35.0 \| 525 \| 0.9453 \|
	\| 0.2954 \| 36.0 \| 540 \| 0.9452 \|
	\| 0.2954 \| 37.0 \| 555 \| 0.9442 \|
	\| 0.2954 \| 38.0 \| 570 \| 0.9459 \|
	\| 0.2954 \| 39.0 \| 585 \| 0.9442 \|
	\| 0.2954 \| 40.0 \| 600 \| 0.9443 \|
	\| 0.2954 \| 41.0 \| 615 \| 0.9445 \|
	\| 0.2954 \| 42.0 \| 630 \| 0.9442 \|
	\| 0.2954 \| 43.0 \| 645 \| 0.9441 \|
	\| 0.2954 \| 44.0 \| 660 \| 0.9453 \|
	\| 0.2954 \| 45.0 \| 675 \| 0.9447 \|
	\| 0.2954 \| 46.0 \| 690 \| 0.9441 \|
	\| 0.2954 \| 47.0 \| 705 \| 0.9438 \|
	\| 0.2954 \| 48.0 \| 720 \| 0.9438 \|
	\| 0.2954 \| 49.0 \| 735 \| 0.9437 \|
	\| 0.2954 \| 50.0 \| 750 \| 0.9436 \|


	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.2.1+cu121
	- Datasets 2.18.0
	- Tokenizers 0.15.2

	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	base_model: distilgpt2
	model-index:
	- name: StatementOfWork_Generator_Omega2
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# StatementOfWork_Generator_Omega2

	This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.9436

	## 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: 50
	- eval_batch_size: 50
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|
	\| No log \| 1.0 \| 15 \| 0.9674 \|
	\| No log \| 2.0 \| 30 \| 0.9673 \|
	\| No log \| 3.0 \| 45 \| 0.9633 \|
	\| No log \| 4.0 \| 60 \| 0.9629 \|
	\| No log \| 5.0 \| 75 \| 0.9633 \|
	\| No log \| 6.0 \| 90 \| 0.9634 \|
	\| No log \| 7.0 \| 105 \| 0.9635 \|
	\| No log \| 8.0 \| 120 \| 0.9603 \|
	\| No log \| 9.0 \| 135 \| 0.9550 \|
	\| No log \| 10.0 \| 150 \| 0.9583 \|
	\| No log \| 11.0 \| 165 \| 0.9574 \|
	\| No log \| 12.0 \| 180 \| 0.9544 \|
	\| No log \| 13.0 \| 195 \| 0.9540 \|
	\| No log \| 14.0 \| 210 \| 0.9575 \|
	\| No log \| 15.0 \| 225 \| 0.9530 \|
	\| No log \| 16.0 \| 240 \| 0.9519 \|
	\| No log \| 17.0 \| 255 \| 0.9514 \|
	\| No log \| 18.0 \| 270 \| 0.9534 \|
	\| No log \| 19.0 \| 285 \| 0.9498 \|
	\| No log \| 20.0 \| 300 \| 0.9554 \|
	\| No log \| 21.0 \| 315 \| 0.9474 \|
	\| No log \| 22.0 \| 330 \| 0.9539 \|
	\| No log \| 23.0 \| 345 \| 0.9470 \|
	\| No log \| 24.0 \| 360 \| 0.9491 \|
	\| No log \| 25.0 \| 375 \| 0.9478 \|
	\| No log \| 26.0 \| 390 \| 0.9454 \|
	\| No log \| 27.0 \| 405 \| 0.9472 \|
	\| No log \| 28.0 \| 420 \| 0.9481 \|
	\| No log \| 29.0 \| 435 \| 0.9467 \|
	\| No log \| 30.0 \| 450 \| 0.9473 \|
	\| No log \| 31.0 \| 465 \| 0.9478 \|
	\| No log \| 32.0 \| 480 \| 0.9439 \|
	\| No log \| 33.0 \| 495 \| 0.9453 \|
	\| 0.2954 \| 34.0 \| 510 \| 0.9446 \|
	\| 0.2954 \| 35.0 \| 525 \| 0.9453 \|
	\| 0.2954 \| 36.0 \| 540 \| 0.9452 \|
	\| 0.2954 \| 37.0 \| 555 \| 0.9442 \|
	\| 0.2954 \| 38.0 \| 570 \| 0.9459 \|
	\| 0.2954 \| 39.0 \| 585 \| 0.9442 \|
	\| 0.2954 \| 40.0 \| 600 \| 0.9443 \|
	\| 0.2954 \| 41.0 \| 615 \| 0.9445 \|
	\| 0.2954 \| 42.0 \| 630 \| 0.9442 \|
	\| 0.2954 \| 43.0 \| 645 \| 0.9441 \|
	\| 0.2954 \| 44.0 \| 660 \| 0.9453 \|
	\| 0.2954 \| 45.0 \| 675 \| 0.9447 \|
	\| 0.2954 \| 46.0 \| 690 \| 0.9441 \|
	\| 0.2954 \| 47.0 \| 705 \| 0.9438 \|
	\| 0.2954 \| 48.0 \| 720 \| 0.9438 \|
	\| 0.2954 \| 49.0 \| 735 \| 0.9437 \|
	\| 0.2954 \| 50.0 \| 750 \| 0.9436 \|


	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.2.1+cu121
	- Datasets 2.18.0
	- Tokenizers 0.15.2