Instructions to use hoganpham/LLMGenFoodRecipe-GPT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hoganpham/LLMGenFoodRecipe-GPT2 with Transformers:

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

pipe = pipeline("text-generation", model="hoganpham/LLMGenFoodRecipe-GPT2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hoganpham/LLMGenFoodRecipe-GPT2")
model = AutoModelForCausalLM.from_pretrained("hoganpham/LLMGenFoodRecipe-GPT2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hoganpham/LLMGenFoodRecipe-GPT2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hoganpham/LLMGenFoodRecipe-GPT2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hoganpham/LLMGenFoodRecipe-GPT2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hoganpham/LLMGenFoodRecipe-GPT2

SGLang

How to use hoganpham/LLMGenFoodRecipe-GPT2 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 "hoganpham/LLMGenFoodRecipe-GPT2" \
    --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": "hoganpham/LLMGenFoodRecipe-GPT2",
		"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 "hoganpham/LLMGenFoodRecipe-GPT2" \
        --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": "hoganpham/LLMGenFoodRecipe-GPT2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hoganpham/LLMGenFoodRecipe-GPT2 with Docker Model Runner:
```
docker model run hf.co/hoganpham/LLMGenFoodRecipe-GPT2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LLMGenFoodRecipe-GPT2

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

Loss: 0.0490

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	56	0.0966
No log	2.0	112	0.0490

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.2.0
Tokenizers 0.19.1

Downloads last month: 3

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for hoganpham/LLMGenFoodRecipe-GPT2

Base model

distilbert/distilgpt2

Finetuned

(1455)

this model

Quantizations

1 model

hoganpham
/

LLMGenFoodRecipe-GPT2

LLMGenFoodRecipe-GPT2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for hoganpham/LLMGenFoodRecipe-GPT2

Dataset used to train hoganpham/LLMGenFoodRecipe-GPT2