Instructions to use LuangMV97/Roberta-DialoGPT_EmpAI_Prueba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LuangMV97/Roberta-DialoGPT_EmpAI_Prueba with Transformers:

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

pipe = pipeline("text-generation", model="LuangMV97/Roberta-DialoGPT_EmpAI_Prueba")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LuangMV97/Roberta-DialoGPT_EmpAI_Prueba")
model = AutoModelForCausalLM.from_pretrained("LuangMV97/Roberta-DialoGPT_EmpAI_Prueba")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LuangMV97/Roberta-DialoGPT_EmpAI_Prueba with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LuangMV97/Roberta-DialoGPT_EmpAI_Prueba"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LuangMV97/Roberta-DialoGPT_EmpAI_Prueba",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LuangMV97/Roberta-DialoGPT_EmpAI_Prueba

SGLang

How to use LuangMV97/Roberta-DialoGPT_EmpAI_Prueba 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 "LuangMV97/Roberta-DialoGPT_EmpAI_Prueba" \
    --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": "LuangMV97/Roberta-DialoGPT_EmpAI_Prueba",
		"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 "LuangMV97/Roberta-DialoGPT_EmpAI_Prueba" \
        --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": "LuangMV97/Roberta-DialoGPT_EmpAI_Prueba",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LuangMV97/Roberta-DialoGPT_EmpAI_Prueba with Docker Model Runner:
```
docker model run hf.co/LuangMV97/Roberta-DialoGPT_EmpAI_Prueba
```

Roberta-DialoGPT_EmpAI_Prueba

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

eval_loss: 4.5237
eval_runtime: 348.0919
eval_samples_per_second: 22.224
eval_steps_per_second: 2.778
epoch: 3.0
step: 11601

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
num_epochs: 5
mixed_precision_training: Native AMP

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 1

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for LuangMV97/Roberta-DialoGPT_EmpAI_Prueba

Base model

microsoft/DialoGPT-medium

Finetuned

(93)

this model