Instructions to use Shreyansh234/restaurant_calling_agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shreyansh234/restaurant_calling_agent with Transformers:

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

pipe = pipeline("text-generation", model="Shreyansh234/restaurant_calling_agent")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Shreyansh234/restaurant_calling_agent")
model = AutoModelForCausalLM.from_pretrained("Shreyansh234/restaurant_calling_agent")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Shreyansh234/restaurant_calling_agent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shreyansh234/restaurant_calling_agent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shreyansh234/restaurant_calling_agent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Shreyansh234/restaurant_calling_agent

SGLang

How to use Shreyansh234/restaurant_calling_agent 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 "Shreyansh234/restaurant_calling_agent" \
    --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": "Shreyansh234/restaurant_calling_agent",
		"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 "Shreyansh234/restaurant_calling_agent" \
        --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": "Shreyansh234/restaurant_calling_agent",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Shreyansh234/restaurant_calling_agent with Docker Model Runner:
```
docker model run hf.co/Shreyansh234/restaurant_calling_agent
```

restaurant_calling_agent

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

Loss: 0.1524

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-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.6599	1.0	120	0.4404
0.3286	2.0	240	0.2236
0.3178	3.0	360	0.1899
0.2411	4.0	480	0.1712
0.2121	5.0	600	0.1633
0.2029	6.0	720	0.1607
0.1860	7.0	840	0.1569
0.1778	8.0	960	0.1533
0.1776	9.0	1080	0.1529
0.1730	10.0	1200	0.1524

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.8.5
Tokenizers 0.22.2

Downloads last month: 14

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for Shreyansh234/restaurant_calling_agent

Base model

distilbert/distilgpt2

Finetuned

(1514)

this model