Instructions to use LouisSanna/HW2-supervised with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LouisSanna/HW2-supervised with Transformers:

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

pipe = pipeline("text-generation", model="LouisSanna/HW2-supervised")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LouisSanna/HW2-supervised")
model = AutoModelForCausalLM.from_pretrained("LouisSanna/HW2-supervised")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LouisSanna/HW2-supervised with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LouisSanna/HW2-supervised"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LouisSanna/HW2-supervised",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LouisSanna/HW2-supervised

SGLang

How to use LouisSanna/HW2-supervised 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 "LouisSanna/HW2-supervised" \
    --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": "LouisSanna/HW2-supervised",
		"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 "LouisSanna/HW2-supervised" \
        --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": "LouisSanna/HW2-supervised",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LouisSanna/HW2-supervised with Docker Model Runner:
```
docker model run hf.co/LouisSanna/HW2-supervised
```

HW2-supervised

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

Loss: 2.9822

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: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
3.2637	0.9991	805	3.0219
3.0283	1.9994	1611	2.9898
2.964	2.9972	2415	2.9822

Framework versions

Transformers 4.44.2
Pytorch 2.0.0
Datasets 2.16.1
Tokenizers 0.19.1

Downloads last month: -

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for LouisSanna/HW2-supervised

Base model

openai-community/gpt2

Finetuned

(2159)

this model

LouisSanna
/

HW2-supervised

HW2-supervised

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for LouisSanna/HW2-supervised

Dataset used to train LouisSanna/HW2-supervised