Instructions to use mhidper/aplicadaT1-complete with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mhidper/aplicadaT1-complete with Transformers:

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

pipe = pipeline("text-generation", model="mhidper/aplicadaT1-complete")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mhidper/aplicadaT1-complete")
model = AutoModelForCausalLM.from_pretrained("mhidper/aplicadaT1-complete")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mhidper/aplicadaT1-complete with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mhidper/aplicadaT1-complete"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mhidper/aplicadaT1-complete",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mhidper/aplicadaT1-complete

SGLang

How to use mhidper/aplicadaT1-complete 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 "mhidper/aplicadaT1-complete" \
    --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": "mhidper/aplicadaT1-complete",
		"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 "mhidper/aplicadaT1-complete" \
        --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": "mhidper/aplicadaT1-complete",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mhidper/aplicadaT1-complete with Docker Model Runner:
```
docker model run hf.co/mhidper/aplicadaT1-complete
```

A newer version of this model is available: mistralai/Mistral-7B-Instruct-v0.1

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Modelo aplicadaT1

Este es un modelo de lenguaje basado en Mistral, ajustado para aplicaciones educativas.

Uso del modelo

from transformers import AutoModelForCausalLM, AutoTokenizer

# Cargar modelo y tokenizer
tokenizer = AutoTokenizer.from_pretrained("mhidper/aplicadaT1-complete")
model = AutoModelForCausalLM.from_pretrained("mhidper/aplicadaT1-complete")

# Ejemplo de uso
input_text = "### Instruction: Explica el concepto de derivadas en cálculo.\n\n### Response:"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
output = model.generate(input_ids, max_length=500)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

F16

Model tree for mhidper/aplicadaT1-complete

Base model

mistralai/Mixtral-8x7B-v0.1

Finetuned

mistralai/Mixtral-8x7B-Instruct-v0.1

Finetuned

(65)

this model