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

Libraries

How to use ostorc/Conversational_Spanish_GPT with Transformers:

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

pipe = pipeline("text-generation", model="ostorc/Conversational_Spanish_GPT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ostorc/Conversational_Spanish_GPT")
model = AutoModelForCausalLM.from_pretrained("ostorc/Conversational_Spanish_GPT")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ostorc/Conversational_Spanish_GPT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ostorc/Conversational_Spanish_GPT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ostorc/Conversational_Spanish_GPT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ostorc/Conversational_Spanish_GPT

SGLang

How to use ostorc/Conversational_Spanish_GPT 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 "ostorc/Conversational_Spanish_GPT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ostorc/Conversational_Spanish_GPT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ostorc/Conversational_Spanish_GPT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ostorc/Conversational_Spanish_GPT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Me puedes ayudar como crear una IA conversacional igual que la tuya

by Sebastian2903 - opened Aug 26, 2024

Discussion

Sebastian2903

Aug 26, 2024

Me haria de gran ayuda

ostorc

Owner Aug 26, 2024

¡Hola, Sebastian2903!

Gracias por tu interés en mi modelo. Si estás pensando en crear una IA conversacional similar a la mía, aquí tienes un consejo importante: en lugar de replicar modelos existentes, te recomiendo entender el proceso de creación y adaptarlo a tus objetivos específicos.

Por ejemplo, si tu meta es desarrollar un chatbot para un restaurante de comida rápida, es posible que no necesites una IA muy compleja. Un árbol lógico podría ser suficiente y te ayudaría a ahorrar en recursos computacionales, datos y tiempo.

En cambio, si quieres construir un modelo GPT basado en DialoGPT, te recomiendo este tutorial: https://towardsdatascience.com/make-your-own-rick-sanchez-bot-with-transformers-and-dialogpt-fine-tuning-f85e6d1f4e30?gi=46fd5cd5a010

Este tutorial me resultó muy útil para crear mi primer modelo y definir mis objetivos. Gracias a esto, pude desarrollar el modelo Conversational_Spanish_GPT.

¡Te animo a seguir adelante y te deseo mucho éxito en tu proyecto!

ostorc changed discussion status to closed Aug 28, 2024

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