Ollama

25 de mayo del 2021

Ollama

Ollama es un software basado en llama.cpp para la inferencia de LLMs.

llama.cpp es una librería para la inferencia de LLMs en C/C++, de modo que Ollama se ha construido por encima haciendo que el uso sea más sencillo para cualquier tipo de usuario. Es decir, es un wrapper para poder usar llama.cpp con una API de Python, JavaScript y terminal

Instalación

Para instalar ollama en windows o mac tienes que descargarte un instalador desde su página de descargas, sin embargo, si usas linux, como es mi caso, tienes que ejecutar el siguiente comando

curl -fsSL https://ollama.com/install.sh | sh

Run

Para correr un LLM basta con ejecutar ollama run <LLM>, por ejemplo puedo usar llama3 mediante

	
		!ollama run llama3

	
		pulling manifest
pulling 6a0746a1ec1a... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 4.7 GB
pulling 4fa551d4f938... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏  12 KB
pulling 8ab4849b038c... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏  254 B
pulling 577073ffcc6c... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏  110 B
pulling 3f8eb4da87fa... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏  485 B
verifying sha256 digest
writing manifest
removing any unused layers
success
>>> hola
Hola! ¿Cómo estás?
>>> Send a message (/? for help)

Como se puede ver ollama funciona como docker, como no tenía descargado llama3, se lo ha descargado y luego podemos usarlo

Modelos

Si queremos saber qué modelos hay disponibles podemos ir a su página de modelos, donde se pueden ver todos los modelos disponibles

Descargar un modelo

Si queremos descargarnos un modelo, lo haremos igual que con docker ollama pull <LLM>, por ejemplo, voy a descargarme phi3

	
		!ollama pull phi3

	
		pulling manifest
pulling 4fed7364ee3e... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 2.3 GB
pulling c608dc615584... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏  149 B
pulling fa8235e5b48f... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 1.1 KB
pulling d47ab88b61ba... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏  140 B
pulling f7eda1da5a81... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏  485 B
verifying sha256 digest
writing manifest
removing any unused layers
success

Ayuda

Si queremos ver todos los comandos que tenemos disponibles podemos usar ollama -h

	
		!ollama -h

	
		Large language model runner
Usage:
  ollama [flags]
  ollama [command]
Available Commands:
  serve       Start ollama
  create      Create a model from a Modelfile
  show        Show information for a model
  run         Run a model
  pull        Pull a model from a registry
  push        Push a model to a registry
  list        List models
  ps          List running models
  cp          Copy a model
  rm          Remove a model
  help        Help about any command
Flags:
  -h, --help      help for ollama
  -v, --version   Show version information
Use "ollama [command] --help" for more information about a command.

Listar los modelos disponibles

Podemos ver en la ayuda que si hacemos ollama list podremos ver todos los modelos disponibles

	
		!ollama list

	
		NAME         	ID          	SIZE  	MODIFIED       
phi3:latest  	a2c89ceaed85	2.3 GB	4 minutes ago 	
llama3:latest	365c0bd3c000	4.7 GB	10 minutes ago

Y como vemos solo tenemos los dos que hemos descargado hasta el momento

Ver los modelos corriendo

Si hacemos ollama ps podemos ver los modelos que tenemos corriendo

	
		!ollama ps

	
		NAME         	ID          	SIZE  	PROCESSOR	UNTIL              
llama3:latest	365c0bd3c000	5.4 GB	100% GPU 	3 minutes from now

Vemos que tenemos corriendo llama3 porque al principio hicimos ollama run llama3, pero si ahora lo intentamos con phi3

	
		!ollama run phi3

	
		>>> Send a message (/? for help)

Si ahora volvemos a hacer ollama ps

	
		!ollama ps

	
		NAME       	ID          	SIZE  	PROCESSOR	UNTIL              
phi3:latest	a2c89ceaed85	3.8 GB	100% GPU 	3 minutes from now

Podemos ver que el modelo que está corriendo es phi3

Modelos multimodales

En la lista de modelos de ollama hay modelos multimodales, como moondream2, que siendo un modelo con solo 1.8B de parámetros tiene un desempeño muy bueno, así que vamos a descargarlo para probarlo

	
		!ollama pull moondream

	
		pulling manifest
pulling e554c6b9de01... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 828 MB
pulling 4cc1cb3660d8... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 909 MB
pulling c71d239df917... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏  11 KB
pulling 4b021a3b4b4a... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏   77 B
pulling 9468773bdc1f... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏   65 B
pulling ba5fbb481ada... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏  562 B
verifying sha256 digest
writing manifest
removing any unused layers
success

Una vez descargado vamos a preguntarle por esta imagen

this_is_fine

Para ello tenemos tenemos que pasarle en el prompt la ruta donde tenemos la imagen

	
		!ollama run moondream

	
		>>> Puedes explicarme esta imagen? /home/wallabot/Documentos/web/portafolio/images/this_is_fine.png
Added image '/home/wallabot/Documentos/web/portafolio/images/this_is_fine.png'
The image is a comic strip featuring two dogs sitting at a table. One dog has its eyes closed, while the other one appears to be looking at something or someone in 
front of it. The scene takes place inside a house with a fireplace and a cup on the table. Above them, there's a speech bubble that says 'This is fine.'

Prompts multilinea

Cuando necesitamos escribir más de una línea en nuestro prompt simplemente tenemos que meterlo entre comillas triples """

	
		!ollama run llama3

>>> """Hola, puedes hacerme un resumen de la siguiente conversación
...
... Alex: Bea, he estado revisando el modelo de clasificación de imágenes que entrenamos la semana pasada, y me parece que no está funcionando como esperábamos. Las métrica
... s de precisión y recall están por debajo de lo que habíamos estimado.
...
... Bea: Sí, lo noté también. He estado analizando las predicciones erróneas y creo que el problema puede estar en el preprocesamiento de los datos. Algunos de los ejemplos
... de entrenamiento parecen estar etiquetados incorrectamente.
...
... Alex: Eso tiene sentido. ¿Crees que deberíamos aplicar alguna técnica de aumento de datos para balancear mejor las clases y mejorar la calidad de las etiquetas?
...
... Bea: Podría funcionar. De hecho, estaba pensando en utilizar augmentations como rotaciones y traslaciones. También podríamos considerar el uso de GANs para generar más
... ejemplos sintéticos, especialmente para las clases minoritarias.
...
... Alex: Buena idea. Además, podríamos implementar una técnica de oversampling como SMOTE para equilibrar las clases. Pero primero, necesitamos limpiar y verificar nuestra
... s etiquetas. ¿Te parece si dividimos las tareas? Yo puedo encargarme del aumento de datos y tú podrías revisar las etiquetas.
...
... Bea: Perfecto. También me gustaría ajustar un poco los hiperparámetros del modelo. Quizás deberíamos hacer una búsqueda en grid para encontrar los mejores valores para
... el learning rate y el batch size.
...
... Alex: De acuerdo. Después de mejorar las etiquetas y los datos, podemos configurar una búsqueda en grid y usar un cluster de computación para agilizar el proceso. Creo
... que podemos obtener resultados mucho más sólidos.
...
... Bea: Me parece un buen plan. Vamos a dividir las tareas y nos reunimos en unos días para revisar los avances. Con un poco de suerte, podremos mejorar significativamente
... el rendimiento del modelo.
...
... Alex: Genial, vamos a ello. ¡Gracias por la colaboración, Bea!
...
... Bea: ¡A ti, Alex! Trabajar en equipo siempre da mejores resultados. ¡Nos vemos pronto!"""
Aquí te presento un resumen de la conversación:
La conversación comienza con Alex, quien está revisando el modelo de clasificación de imágenes entrenado la semana pasada y descubre que no está funcionando como
esperaban. Bea también había notado el problema y cree que puede estar relacionado con el preprocesamiento de los datos, ya que algunos ejemplos de entrenamiento
parecen estar etiquetados incorrectamente.
Discuten posibles soluciones para mejorar la calidad del modelo, incluyendo el uso de técnicas de aumento de datos (augmentations) y generación de ejemplos sintéticos
con GANs. También mencionan la posibilidad de implementar una técnica de oversampling como SMOTE para equilibrar las clases.
Acuerdan dividir las tareas: Alex se encargará del aumento de datos, mientras que Bea revisará las etiquetas. Después de mejorar los datos y las etiquetas, pueden
configurar una búsqueda en grid para encontrar los mejores hiperparámetros del modelo.
Finalmente, acuerdan reunirse después de unos días para revisar los avances y trabajar juntos en equipo para mejorar el rendimiento del modelo.

Archivos GGUF

Como hemos dicho, ollama está basado en llama.cpp, por lo que el tipo de modelos que utiliza ollama son los que utiliza llama.cpp y son los archivos de tip GGUF.

Los archivos GGUF se refieren al formato de archivo Generalized GPU-Friendly Unified Format, que está diseñado para almacenar y ejecutar modelos de lenguaje grande (LLMs) de manera eficiente en hardware acelerado por GPU.

El formato GGUF es optimizado específicamente para almacenar y ejecutar modelos de lenguaje grande, como arquitecturas basadas en transformers. Proporciona una forma compacta y eficiente de representar los pesos del modelo, las activaciones y los grafos de computación, lo que permite tiempos de inferencia más rápidos y un uso reducido de memoria.

Los archivos GGUF suelen contener los siguientes componentes:

Arquitectura del modelo: El archivo GGUF almacena la arquitectura del modelo, incluyendo el número de capas, tamaños ocultos y mecanismos de atención.
Pesos: Los pesos del modelo se almacenan en un formato comprimido para reducir el uso de memoria. Es decir, los modelos están cuantizados
Activaciones: El archivo GGUF contiene activaciones pre-computadas para ciertas capas o sub-capas, que pueden ser reutilizadas durante la inferencia.
Grafo de computación: El grafo de computación representa la secuencia de operaciones necesarias para realizar la inferencia con el modelo.

Al utilizar archivos GGUF, los desarrolladores pueden implementar modelos de lenguaje grande en una variedad de hardware, incluyendo GPUs, TPUs y CPUs, mientras logran tiempos de inferencia más rápidos y un rendimiento mejorado.

Por ejemplo, el modelo llama3 de 70B de paŕametros en FP32 debería ocupar 280 GB, en FP16 debería ocupar 140 GB y en INT8 debería ocupar 70GB. Sin embargo, cuando nos lo vamos a descargar, en la web pone que ocupa 40 GB, por lo que al ocupar un poco más de la mitad que en INT8 podemos pensar que está cuantizado en INT4

Como tengo 2 GPUs de 24 GB lo podría usar entre las dos GPUs, si lo cargase en paralelo en las dos, por lo que me lo voy a descargar y lo voy a probar

	
		!ollama pull llama3:70B

	
		pulling manifest 
pulling 0bd51f8f0c97... 100% ▕████████████████████████████████████████████████████████████████████████████████████████████████▏  39 GB
pulling 4fa551d4f938... 100% ▕████████████████████████████████████████████████████████████████████████████████████████████████▏  12 KB
pulling 8ab4849b038c... 100% ▕████████████████████████████████████████████████████████████████████████████████████████████████▏  254 B
pulling 577073ffcc6c... 100% ▕████████████████████████████████████████████████████████████████████████████████████████████████▏  110 B
pulling ea8e06d28e47... 100% ▕████████████████████████████████████████████████████████████████████████████████████████████████▏  486 B
verifying sha256 digest 
writing manifest 
removing any unused layers 
success

Lo pruebo

	
		!ollama run llama3:70B

	
		>>> Hola, me puedes decir cómo pasar un array de numpy a un tensor de pytorch?
Sí!
En PyTorch, puedes convertir un arreglo de NumPy a un tensor utilizando la función `torch.from_numpy()`.
Aquí tienes un ejemplo:
```
import numpy as np
import torch
# Creamos un arreglo de NumPy
arr = np.array([1, 2, 3, 4, 5])
# Convertimos el arreglo a un tensor de PyTorch
tensor = torch.from_numpy(arr)
print(tensor)  # Output: tensor([1, 2, 3, 4, 5])
```
Ten en cuenta que la función `torch.from_numpy()` devuelve un tensor que comparte la memoria con el arreglo de NumPy original. Esto significa que si 
modificas el tensor, también estarás modificando el arreglo de NumPy original.
Si deseas crear un tensor independiente del arreglo de NumPy, puedes utilizar la función `torch.tensor()` y pasarle el arreglo de NumPy como parámetro:
```
tensor = torch.tensor(arr)
```
De esta manera, el tensor creado será una copia independiente del arreglo de NumPy original.
Espero que esto te ayude!

No solo contesta bien, sino que va rapidísimo

Además se comprueba que ha cargado el modelo en mis dos GPUs en paralelo

Esto se puede suponer, porque en una sola no entraría como he comentado antes. Pero es que además he creado un monitor de GPUs, con el que puedo ver que están las dos GPUs casi llenas

De aquí podemos sacar otra conclusión, y es que mediante los archivos GGUF podemos ejecutar los LLMs de la manera más eficiente en nuestros ordenadores, en mis caso ha cargado en paralelo el LLM en mis dos GPUs

Modelfile

Ahora que hemos visto lo que son los archivos GGUF podemos ver cómo crear nuevos a partir de otros. Al igual que con docker podemor crear imagenes a partir de otras con un dockerfile, ollama nos permite crear modelos a partir de otros con un modelfile

Para esto vamos a partir del archivo Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf que es el archivo GGUF del modelo NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF. Este modelo es una versión de llama3 de 8B de parámetros

A la hora de crear modelos y subirlos a internet lo ideal es ponerle nombres descriptivos, por ejemplo, en este caso vemos que tenemos en el nombre

Llama-3-Instruct: Lo que quiere decir que es un modelo que parte de llama3 8B Instruct
Merged: Es un modelo mergeado
DPO: Se ha hecho el alineamiento con DPO
Q4: Está cuantizado a 4 bits

Primero vamos a crear la carpeta donde vamos a crear el modelfile

	
		!mkdir ollama_modelfile

Ahora creamos el modelfile

	
		!mkdir ollama_modelfile
!touch ollama_modelfile/Modelfile

Y ahora nos descargamos el archivo GGUF

	
		!mkdir ollama_modelfile
!touch ollama_modelfile/Modelfile
!wget https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF/resolve/main/Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf -O ollama_modelfile/Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf

	
		--2024-05-23 06:31:53--  https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF/resolve/main/Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf
Resolving huggingface.co (huggingface.co)... 54.192.95.26, 54.192.95.79, 54.192.95.21, ...
Connecting to huggingface.co (huggingface.co)|54.192.95.26|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://cdn-lfs-us-1.huggingface.co/repos/48/0d/480d1b9ea3df81b6cf974d62b6bc04f402ac5600a1dfdde7bbb9e819a111c572/762b9371a296ab2628592b9462dc676b27d881a3402816492801641a437669b3?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf%3B+filename%3D%22Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf%22%3B&Expires=1716697914&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxNjY5NzkxNH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzQ4LzBkLzQ4MGQxYjllYTNkZjgxYjZjZjk3NGQ2MmI2YmMwNGY0MDJhYzU2MDBhMWRmZGRlN2JiYjllODE5YTExMWM1NzIvNzYyYjkzNzFhMjk2YWIyNjI4NTkyYjk0NjJkYzY3NmIyN2Q4ODFhMzQwMjgxNjQ5MjgwMTY0MWE0Mzc2NjliMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=LQecPNccMxv4R-BowDrcwORGpk%7ETTh-gVmMrAzzmdT8EFDVXNH7KTwmqKF-pEAbbir9iRS1pX7NFCterYF0w5UMe%7EWkvGg46Z73yMn3s96hm7edWypv9OCMFuBvcp8arYonKljUQ4UvdQKFHXAZxKhLrx6OVqx9YoTvHuXmpBoKXAPi1XNLPwY0YTX4wn3DnAFFzkHOsDK7p2TJ3z7RFzmfyZg%7E5daWm008oGBvIV192MKk5uqQGwqv3l-4mi2Nd8eOwMW2Yfzigz7EZJgPM7UDYzin5vABsnyXJorOSRdri13hlC-AMaY9db76vLsT1NBvVU1xVtWxAylDqU6XYlQ__&Key-Pair-Id=KCD77M1F0VK2B [following]
--2024-05-23 06:31:54--  https://cdn-lfs-us-1.huggingface.co/repos/48/0d/480d1b9ea3df81b6cf974d62b6bc04f402ac5600a1dfdde7bbb9e819a111c572/762b9371a296ab2628592b9462dc676b27d881a3402816492801641a437669b3?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf%3B+filename%3D%22Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf%22%3B&Expires=1716697914&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxNjY5NzkxNH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzQ4LzBkLzQ4MGQxYjllYTNkZjgxYjZjZjk3NGQ2MmI2YmMwNGY0MDJhYzU2MDBhMWRmZGRlN2JiYjllODE5YTExMWM1NzIvNzYyYjkzNzFhMjk2YWIyNjI4NTkyYjk0NjJkYzY3NmIyN2Q4ODFhMzQwMjgxNjQ5MjgwMTY0MWE0Mzc2NjliMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=LQecPNccMxv4R-BowDrcwORGpk%7ETTh-gVmMrAzzmdT8EFDVXNH7KTwmqKF-pEAbbir9iRS1pX7NFCterYF0w5UMe%7EWkvGg46Z73yMn3s96hm7edWypv9OCMFuBvcp8arYonKljUQ4UvdQKFHXAZxKhLrx6OVqx9YoTvHuXmpBoKXAPi1XNLPwY0YTX4wn3DnAFFzkHOsDK7p2TJ3z7RFzmfyZg%7E5daWm008oGBvIV192MKk5uqQGwqv3l-4mi2Nd8eOwMW2Yfzigz7EZJgPM7UDYzin5vABsnyXJorOSRdri13hlC-AMaY9db76vLsT1NBvVU1xVtWxAylDqU6XYlQ__&Key-Pair-Id=KCD77M1F0VK2B
Resolving cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)... 108.157.98.20, 108.157.98.92, 108.157.98.55, ...
Connecting to cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)|108.157.98.20|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 4920733728 (4,6G) [binary/octet-stream]
Saving to: ‘ollama_modelfile/Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf’
ollama_modelfile/He 100%[===================>]   4,58G  71,1MB/s    in 65s     
2024-05-23 06:32:59 (72,2 MB/s) - ‘ollama_modelfile/Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf’ saved [4920733728/4920733728]

Una vez descargado empezamos a crear el modelfile

FROM

Primero tenemos que especificar desde qué modelo partimos

Podemos partir desde un modelo que esté en el hub de ollama, por ejemplo podríamos hacer

FROM llama3

Pero como vamos a partir desde el archivo que nos hemos descargado, lo especificamos mediante el path

FROM ./Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf

	
		%%writefile ollama_modelfile/Modelfile
FROM ./Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf

	
		Overwriting ollama_modelfile/Modelfile

PARAMETER

Ahora tenemos que establecer los valores de los parámetros del LLM, por ejemplo, si queremos modificar el valor de la temperatura ponemos

PARAMETER temperature 0.5

Los parámetros que podemos modificar son

Parámetro	Descripción	Tipo de Valor	Ejemplo de Uso
mirostat	Habilitar el muestreo `Mirostat` para controlar la perplejidad. (por defecto: 0, 0 = deshabilitado, 1 = Mirostat, 2 = Mirostat 2.0)	int	mirostat 0
mirostat_eta	Influye en la rapidez con la que el algoritmo responde a la retroalimentación del texto generado. Una tasa de aprendizaje más baja resultará en ajustes más lentos, mientras que una tasa más alta hará que el algoritmo sea más sensible. (Por defecto: 0.1)	float	mirostat_eta 0.1
mirostat_tau	Controla el equilibrio entre la coherencia y la diversidad del output. Un valor más bajo resultará en un texto más enfocado y coherente. (Por defecto: 5.0)	float	mirostat_tau 5.0
num_ctx	Establece el tamaño de la ventana de contexto utilizada para generar el siguiente token. (Por defecto: 4096)	int	num_ctx 4096
repeat_last_n	Establece cuánto tiempo atrás debe mirar el modelo para evitar repeticiones. (Por defecto: 64, 0 = deshabilitado, -1 = num_ctx)	int	repeat_last_n 64
repeat_penalty	Establece la intensidad con la que se penalizan las repeticiones. Un valor más alto (por ejemplo, 1.5) penalizará las repeticiones más fuertemente, mientras que un valor más bajo (por ejemplo, 0.9) será más indulgente. (Por defecto: 1.1)	float	repeat_penalty 1.1
temperature	La temperatura del modelo. Aumentar la temperatura hará que el modelo responda de manera más creativa. (Por defecto: 0.8)	float	temperature 0.7
seed	Establece la semilla de números aleatorios para la generación. Establecer esto a un número específico hará que el modelo genere el mismo texto para el mismo prompt. (Por defecto: 0)	int	seed 42
stop	Establece las secuencias de parada a utilizar. Cuando se encuentra este patrón, el LLM dejará de generar texto y retornará. Se pueden establecer múltiples patrones de parada especificando múltiples parámetros stop en un archivo de modelo.	string	stop "AI assistant:"
tfs_z	El muestreo libre de colas se usa para reducir el impacto de los tokens menos probables en el output. Un valor más alto (por ejemplo, 2.0) reducirá más el impacto, mientras que un valor de 1.0 deshabilita esta configuración. (por defecto: 1)	float	tfs_z 1
num_predict	Número máximo de tokens a predecir al generar texto. (Por defecto: 128, -1 = generación infinita, -2 = llenar contexto)	int	num_predict 42
top_k	Reduce la probabilidad de generar sin sentido. Un valor más alto (por ejemplo, 100) dará respuestas más diversas, mientras que un valor más bajo (por ejemplo, 10) será más conservador. (Por defecto: 40)	int	top_k 40
top_p	Funciona junto con top-k. Un valor más alto (por ejemplo, 0.95) conducirá a un texto más diverso, mientras que un valor más bajo (por ejemplo, 0.5) generará un texto más enfocado y conservador. (Por defecto: 0.9)	float	top_p 0.9

Vamos a ver los parámetros del modelo llama3

	
		!ollama show llama3 --modelfile | grep PARAMETER

	
		PARAMETER num_keep 24
PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|eot_id|>"

Podemos ver que tiene un parámetro num_keep = 24 que no viene en la documentación de ollama y además tres tokens de parada

Vamos a hacer al modelo más creativo subiéndole la temperatura y vamos a mantener los tokens de parada, porque si no el modelo no parará de generar texto

	
		%%writefile ollama_modelfile/Modelfile
FROM ./Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf
PARAMETER temperature 1
PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|eot_id|>"

	
		Overwriting ollama_modelfile/Modelfile

SYSTEM

Podemos condicionar al modelo con un mensaje de sistema

	
		%%writefile ollama_modelfile/Modelfile
FROM ./Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf
PARAMETER temperature 1
SYSTEM """Eres un asistente de IA, que quiere ayudar, pero no tiene muchas habilidades sociales"""

	
		Overwriting ollama_modelfile/Modelfile

TEMPLATE

Es la plantilla que le pasaremos al modelo, los tipos de datos que puede incluir son

Variable	Descripción
{{ .System }}	El mensaje del sistema utilizado para especificar un comportamiento personalizado.
{{ .Prompt }}	El mensaje de aviso del usuario.
{{ .Response }}	La respuesta del modelo. Al generar una respuesta, se omite el texto después de esta variable.

Como visto así no es muy intuitivo vamos a verlo con un ejemplo, imaginemos que tenemos la siguiente plantilla

TEMPLATE """{{ if .System }}<|im_start|>system
      {{ .System }}<|im_end|>
      {{ end }}{{ if .Prompt }}<|im_start|>user
      {{ .Prompt }}<|im_end|>
      {{ end }}<|im_start|>assistant
      """

Si le pasamos al modelo el siguiente prompt Hola, cómo estás? la plantilla hará tres cosas

{{ if .System }}<|im_start|>system{{ .System }}<|im_end|>{{ end }}:

Si hay mensaje de sistema se le pasa al modelo de la siguiente manera

<|im_start|>system mensaje <|im_end|>.

Como en nuestro caso sí hay un mensaje de sistema le pasará

<|im_start|>Eres un asistente de IA, que quiere ayudar, pero no tiene muchas habilidades sociales<|im_end|>
{{ if .Prompt }}<|im_start|>user{{ .Prompt }}<|im_end|>{{ end }}:

Si hay mensaje de usuario se le pasa al modelo de la siguiente manera

<|im_start|>user mensaje <|im_end|>.

Como en nuestro caso sí hay un mensaje de usuario (el prompt) le pasará

<|im_start|>user Hola, cómo estás? <|im_end|>
<|im_start|>assistant:

Siempre le pasará al modelo

<|im_start|>assistant

para que sepa el LLM que ahora le toca responder

Después de verlo por partes podemos poner el mensaje total que se le pasará al LLM y es

<|im_start|>Eres un asistente de IA, que quiere ayudar, pero no tiene muchas habilidades sociales<|im_end|>
      <|im_start|>user Hola, cómo estás? <|im_end|>
      <|im_start|>assistant

Vamos a ver cómo es la plantilla de llama3

	
		!ollama show llama3 --modelfile | grep {opening_brace}{opening_brace}

	
		TEMPLATE "{{ if .System }}<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>"

Es muy parecido al que hemos visto, con la plantilla de llama3, en nuestro caso se le pasará al LLM

<|start_header_id|>system<|end_header_id|>Eres un asistente de IA, que quiere ayudar, pero no tiene muchas habilidades sociales<|eot_id|>
      <|start_header_id|>user<|end_header_id|>Hola, cómo estás?<|eot_id|>
      <|start_header_id|>assistant<|end_header_id|>{{ .Response }}<|eot_id|>

Como vamos a usar un modelo basado en llama3 vamos a usar su misma template

	
		%%writefile ollama_modelfile/Modelfile
FROM ./Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf
PARAMETER temperature 1
SYSTEM """Eres un asistente de IA, que quiere ayudar, pero no tiene muchas habilidades sociales"""
TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>"""

	
		Overwriting ollama_modelfile/Modelfile

LICENSE

Sirve para establecer la licencia del modelo

	
		%%writefile ollama_modelfile/Modelfile
FROM ./Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf
PARAMETER temperature 1
SYSTEM """Eres un asistente de IA, que quiere ayudar, pero no tiene muchas habilidades sociales"""
TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>"""
LICENSE """Apache-2.0"""

	
		Overwriting ollama_modelfile/Modelfile

MESSAGE

Podemos pasarle un historial de chat

	
		%%writefile ollama_modelfile/Modelfile
FROM ./Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf
PARAMETER temperature 1
SYSTEM """Eres un asistente de IA, que quiere ayudar, pero no tiene muchas habilidades sociales"""
TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>"""
LICENSE """Apache-2.0"""
MESSAGE user Sabes cómo pasar un array de numpy a un tensor de torch?
MESSAGE assistant Sí
MESSAGE user Y me lo podrías decir?
MESSAGE assistant Sí
MESSAGE user Dímelo
MESSAGE assistant Te lo puedo decir

	
		Overwriting ollama_modelfile/Modelfile

ADAPTER

Un adapter es una matriz que se añade al modelo. Se usa para no tener que hacer fine tunning a los LLMs y hacerselo solo a esa matriz, de manera que al añadírsela al modelo, el LLM se comporta como nosotros queremos.

Por lo que para poder hacer esto se le pasa la ruta con el archivo del adapter

ADAPTER ./ollama-lora.bin

Crear el modelo

Una vez escrito el modelfile podemos crear el modelo mediante

ollama create <model-name> -f <path-of-the-modelfile>'

	
		!ollama create assistanmal -f ollama_modelfile/Modelfile

	
		transferring model data 
using existing layer sha256:762b9371a296ab2628592b9462dc676b27d881a3402816492801641a437669b3 
using existing layer sha256:ba85fc82591e657e639f779f4fdc5e6e7cb977f9d36487d67cefe589a8af670e 
using existing layer sha256:995b40fa08b7063fd4fe21ef93f0a1c147d82ecd7e2e87216c11ae809693e101 
using existing layer sha256:2af71558e438db0b73a20beab92dc278a94e1bbe974c00c1a33e3ab62d53a608 
using existing layer sha256:20dc17cb752058f95ad600722883b22f3328ca8ccccbb667d5e07991ae1985c7 
using existing layer sha256:d8ba2f9a17b3bbdeb5690efaa409b3fcb0b56296a777c7a69c78aa33bbddf182 
using existing layer sha256:c2ca5c1f4ec2cfaa477ed5f5770beff313b28d083ce5ceb90daed9d6ecbb773f 
writing manifest 
success

Vemos que tenemos una salida muy similar a la que obtenemos cuando creamos una imagen de docker

Si ahora listamos los modelos que tenemos disponibles

	
		!ollama list

	
		NAME              	ID          	SIZE  	MODIFIED       
assistanmal:latest	2be327b28fbf	4.9 GB	48 seconds ago	
llama3:70b        	786f3184aec0	39 GB 	38 hours ago  	
llava:latest      	8dd30f6b0cb1	4.7 GB	40 hours ago  	
moondream:latest  	55fc3abd3867	1.7 GB	40 hours ago  	
phi3:latest       	a2c89ceaed85	2.3 GB	2 days ago    	
llama3:latest     	365c0bd3c000	4.7 GB	2 days ago

Vemos que está el asistente que acabamos de crear. Vamos a probarlo

	
		!ollama run assistanmal

	
		>>> Sabes cómo pasar un array de numpy a un tensor de torch?
Sí
>>> Y me lo podrías decir?
Sí
>>> Dímelo
Te lo puedo decir
>>> Dímelo
Para convertir un array de NumPy a un tensor de PyTorch, puedes seguir estos pasos:
1. Instalar la librería torch: `pip install torch`
2. Importar las librerías necesarias: `import numpy as np`, `import torch`
3. Convertir el array de NumPy a una variable torch.Tensor: `tensor = torch.from_numpy(array_numpy)`
Aquí tienes un ejemplo:
```python
import numpy as np
import torch
# Crea un array en NumPy
array_numpy = np.array([1, 2, 3])
# Convierte el array de NumPy a una variable torch.Tensor
tensor = torch.from_numpy(array_numpy)
print(tensor)
```
Este código crea un array en NumPy y luego lo convierte a una variable `torch.Tensor` utilizando la función `from_numpy`. La salida sería:
```python
tensor([1, 2, 3])

API REST

Por defecto ollama corre en el puerto 11434 de nuestro localhost y como tiene implementada una API REST podemos hacer llamadas mediante esta API

Podemos hacerle una consulta a un LLM mediante el método POST, para ello tenemos que pasarle un json con, al menos, el modelo y el prompt

	
		!curl -X POST http://127.0.0.1:11434/api/generate -d '{ 
    "model": "llama3", 
    "prompt": "¿Por que el cielo es azul?" 
}' -H "Content-Type: application/json"

	
		{"model":"llama3","created_at":"2024-05-25T05:08:18.884851337Z","response":"La","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:18.895040313Z","response":" raz","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:18.905195114Z","response":"ón","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:18.915384212Z","response":" por","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:18.925599888Z","response":" la","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:18.935894594Z","response":" que","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:18.946216271Z","response":" el","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:18.95667124Z","response":" ci","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:18.967006633Z","response":"elo","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:18.977232419Z","response":" sue","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:18.987573713Z","response":"le","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:18.998090659Z","response":" ser","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:19.008252975Z","response":" az","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:19.018568771Z","response":"ul","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:19.029065258Z","response":" es","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:19.03963268Z","response":" un","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:19.050107165Z","response":" tema","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:19.060833657Z","response":" muy","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:19.071234073Z","response":" interes","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:19.081710362Z","response":"ante","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:19.091871325Z","response":" y","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:19.10204305Z","response":" comple","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:19.112288302Z","response":"jo","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:19.122615519Z","response":".","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:19.132758358Z","response":" Hay","done":false}
...
{"model":"llama3","created_at":"2024-05-25T05:08:23.70235664Z","response":"ó","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:23.712846234Z","response":"meno","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:23.723233334Z","response":".","done":false}
{"model":"llama3","created_at":"2024-05-25T05:08:23.733833978Z","response":"","done":true,"done_reason":"stop","context":[128006,882,128007,271,31282,29197,1744,658,12088,20782,1560,12657,360,30,128009,128006,78191,128007,271,8921,24788,3244,4247,1208,1744,658,12088,20782,34872,273,1446,12657,360,1560,653,47015,23321,50516,5048,379,1946,7453,13,18276,86050,115256,25318,89097,46602,15540,1744,7537,276,56369,277,10566,44585,1832,70191,11,20003,912,18137,5203,44460,31388,10126,13,23815,2483,1028,3118,78,74854,409,5252,115256,25318,11158,470,8699,1473,16,13,3146,8921,72916,409,1208,65566,96618,84201,409,2537,757,4919,2191,437,11158,75419,1560,1208,72916,320,288,1768,67137,8,409,1208,65566,4247,961,27235,300,23321,48467,61025,665,1208,70887,29832,809,64,11,8112,22337,978,3395,300,409,308,22288,1832,4469,78,379,19488,2483,4469,78,13,5034,65566,12657,360,24215,653,65013,409,389,3315,11158,23100,78,1744,39505,1400,4692,11,781,1744,68832,1744,513,5320,68364,2649,11158,18922,531,39961,4247,1208,72916,13,93125,35905,1744,1208,65566,12657,360,1469,39614,936,11158,18041,5048,665,658,12088,20782,7809,2172,390,39505,1400,4692,627,17,13,3146,8921,15938,78031,409,5252,1317,21237,409,389,3315,96618,507,2221,115256,7583,79048,43668,1744,658,12657,360,1560,658,1933,11358,5048,28895,1208,70887,29832,809,64,15938,1395,5252,1317,21237,409,389,3315,938,30826,379,2807,5919,11158,98420,1744,5252,1317,21237,409,389,3315,12657,2482,13,93125,18274,852,28895,1208,70887,29832,809,64,81533,45612,8112,1208,1891,1832,87,5362,409,12782,78,11,658,25616,10358,379,658,38752,409,56562,11,1744,15938,8123,2537,18803,437,2092,5518,390,1317,21237,409,389,3315,11158,4143,300,627,18,13,3146,8921,8881,95266,409,1208,23126,14210,96618,5034,115256,7583,409,1208,8881,95266,31705,19380,1744,1208,23126,14210,2098,273,5697,1208,65566,12657,360,11158,31469,292,1394,12826,91992,264,924,40321,26826,934,56094,3074,379,25107,35497,3074,13,93125,35905,1744,1208,65566,12657,360,9581,11158,25228,64,665,658,12088,20782,627,19,13,3146,8921,62822,44428,2854,3444,96618,13321,12826,11,29571,18137,115256,25318,1744,31705,16414,1744,658,12657,360,1560,653,1933,78632,6632,1744,12155,65117,5933,28895,513,439,511,689,390,658,56562,379,658,12088,20782,13,5034,62822,44428,2854,3444,21329,6791,404,665,42791,77998,3739,1624,1933,1624,12088,20782,382,1737,594,28999,11,912,18137,5203,96189,24788,3244,4247,1208,1744,658,12088,20782,9581,12657,360,11,52904,11158,14707,653,68593,409,2144,4692,65946,17038,11,934,56094,17038,379,78632,25204,1744,6044,38855,264,10566,44585,1832,70191,13,128009],"total_duration":8342889543,"load_duration":3407077432,"prompt_eval_count":19,"prompt_eval_duration":85819000,"eval_count":464,"eval_duration":4848923000}

Como vemos nos ha respondido mediante stream, si no queremos esto y queremos una sola respuesta tenemos que pasarle "stream": false en el json

	
		!curl -X POST http://127.0.0.1:11434/api/generate -d '{ 
    "model": "llama3", 
    "prompt": "¿Por que el cielo es azul?", 
    "stream": false 
}' -H "Content-Type: application/json"

	
		{"model":"llama3","created_at":"2024-05-25T05:42:03.468060994Z","response":"¡Excelente pregunta!

La razón por la que el cielo suele ser azul es un fenómeno físico llamado scattering (difracción) de luz. Cuando la luz del sol entra en la atmósfera, se enfrenta a pequeños partículas de gases como el nitrógeno y el oxígeno, así como a partículas más grandes como las moléculas de agua y los polvo.

La scattering tiene dos tipos: Rayleigh scattering (difracción de Rayleigh) y Mie scattering (difracción de Mie). La primera es la que ocurre cuando la luz se enfrenta con pequeñas partículas, como el nitrógeno y el oxígeno, que son muy delgadas en comparación con la longitud de onda de la luz. En este caso, la luz azul (con una longitud de onda más corta) es scattering más fuerte que la luz roja (con una longitud de onda más larga). Esto es porque las partículas pequeñas son más efectivas en scattering la luz con longitudes de onda más cortas.

La segunda tipo de scattering, Mie scattering, ocurre cuando la luz se enfrenta con partículas más grandes, como las moléculas de agua y los polvo. En este caso, la scattering es más fuerte para las longitudes de onda más largas, lo que significa que la luz roja es scatterred más fuerte que la luz azul.

Finalmente, cuando llega el momento en que la luz solar ha sido scattering por ambas tipos de partículas, la luz azul (con una longitud de onda más corta) ha sido scatterred más fuerte y llega a nuestros ojos desde diferentes ángulos. Esto es lo que nos hace ver el cielo como azul.

En resumen, la combinación de scattering de Rayleigh y Mie es lo que nos permite ver al cielo como azul. ¡Y qué bonito resultado es eso!","done":true,"done_reason":"stop","context":[128006,882,128007,271,31282,29197,1744,658,12088,20782,1560,12657,360,30,128009,128006,78191,128007,271,40932,20656,6960,80440,2268,8921,24788,3244,4247,1208,1744,658,12088,20782,34872,273,1446,12657,360,1560,653,44585,1832,70191,65946,4042,51204,2172,72916,320,88564,81,67147,8,409,65566,13,89014,1208,65566,1624,2092,83786,665,1208,70887,29832,809,64,11,513,89599,64,264,48467,18720,961,27235,300,409,45612,8112,658,308,22288,1832,4469,78,379,658,19488,2483,4469,78,11,37560,8112,264,961,27235,300,11158,38546,8112,5252,22337,978,3395,300,409,56562,379,2537,1499,3415,382,8921,72916,24215,8924,64962,25,13558,64069,72916,320,88564,81,67147,409,13558,64069,8,379,386,648,72916,320,88564,81,67147,409,386,648,570,5034,44769,1560,1208,1744,18274,852,27443,1208,65566,513,89599,64,390,48467,61025,961,27235,300,11,8112,658,308,22288,1832,4469,78,379,658,19488,2483,4469,78,11,1744,4538,23321,1624,70,11354,665,7809,5840,390,1208,49704,409,389,3315,409,1208,65566,13,2998,10566,23884,11,1208,65566,12657,360,320,444,5203,49704,409,389,3315,11158,23100,64,8,1560,72916,11158,18922,14140,1744,1208,65566,938,5697,320,444,5203,49704,409,389,3315,11158,4143,64,570,93125,1560,28895,5252,961,27235,300,48467,61025,4538,11158,44046,39924,665,72916,1208,65566,390,1317,21237,409,389,3315,11158,23100,300,382,8921,56072,16697,409,72916,11,386,648,72916,11,18274,852,27443,1208,65566,513,89599,64,390,961,27235,300,11158,38546,11,8112,5252,22337,978,3395,300,409,56562,379,2537,1499,3415,13,2998,10566,23884,11,1208,72916,1560,11158,18922,14140,3429,5252,1317,21237,409,389,3315,11158,4143,300,11,781,1744,68832,1744,1208,65566,938,5697,1560,45577,1171,11158,18922,14140,1744,1208,65566,12657,360,382,19918,12826,11,27443,33602,64,658,31221,665,1744,1208,65566,13238,6520,35071,72916,4247,9049,300,64962,409,961,27235,300,11,1208,65566,12657,360,320,444,5203,49704,409,389,3315,11158,23100,64,8,6520,35071,45577,1171,11158,18922,14140,379,33602,64,264,58013,297,40261,23553,46418,19216,983,29752,13,93125,1560,781,1744,12155,35905,2807,658,12088,20782,8112,12657,360,382,1737,594,28999,11,1208,68225,5840,409,72916,409,13558,64069,379,386,648,1560,781,1744,12155,52603,2807,453,12088,20782,8112,12657,360,13,49913,56,43388,7970,6491,27284,1560,44841,0,128009],"total_duration":4760167793,"load_duration":910317,"prompt_eval_count":19,"prompt_eval_duration":126007000,"eval_count":436,"eval_duration":4586524000}

Python API

Para usar la API de Python tenemos que instalar el paquete de Python de ollama

pip install ollama

Una vez instalado tenemos los siguientes métodos

ollama.chat: Para iniciar un chat con un modelo
ollama.generate: Para generar una respuesta a un prompt
ollama.list: Lista los modelos disponibles
ollama.show: Muestra información de un modelo
ollama.create: Crea un modelo
ollama.delete: Borra un modelo
ollama.pull: Descarga un modelo
ollama.push: Sube un modelo
ollama.embeddings: Devuelve los embeddings de un prompt

Vamos a probar algunos

	
		import ollama
stream = ollama.chat(
    model='llama3',
    messages=[{'role': 'user', 'content': '¿Por que el cielo es azul?'}],
    stream=True,
)
for chunk in stream:
  print(chunk['message']['content'], end='', flush=True)

¡Excelente pregunta!
La razón por la que el cielo suele ser azul es un tema de mucha discusión y debate entre científicos y filósofos. Hay varias teorías sobre este asunto, pero te presento algunas de las más populares:
1. **Absorción del azul**: La luz solar contiene todos los colores del espectro visible, desde el rojo hasta el violeta. Cuando la luz solar entra en la atmósfera terrestre, se absorbe y dispersa en diferentes grados dependiendo de la longitud de onda. La longitud de onda azul es particularmente pequeña, lo que significa que es absorbida y dispersada más que cualquier otro color. Esto hace que el azul sea el color predominante que nos llega a los ojos.
2. **Espejo de Rayos X**: Otra teoría sugiere que la atmósfera terrestre actúa como un "espejo" para los rayos X, que son una forma de radiación electromagnética con energías muy altas. Cuando los rayos X chocan contra la molécula de gases en la atmósfera, emiten fotones azules. Estos fotones azules then se propagan a través de la atmósfera y nos llegan a los ojos.
3. **Composición de la atmósfera**: La atmósfera terrestre está compuesta por diferentes gases, como el nitrógeno (N2) y el oxígeno (O2). Los átomos de estos gases pueden absorber y reemitter luz en diferentes longitudes de onda, lo que puede contribuir al color azul del cielo.
4. **Efecto Mie**: El efecto Mie es un proceso físico que ocurre cuando la luz solar interactúa con las partículas pequeñas en la atmósfera, como los aerosoles y las moléculas de gases. Este efecto puede hacer que el azul sea el color predominante que nos llega a los ojos.
En resumen, no hay una única razón por la que el cielo es azul, sino que es un resultado de la interacción compleja entre la luz solar, la atmósfera terrestre y otros factores físicos.

	
		import ollama
stream = ollama.generate(
    model='llama3',
    prompt='¿Por que el cielo es azul?',
    stream=True,
)
for chunk in stream:
    print(chunk['response'], end='', flush=True)

La pregunta clásica!
La razón por la que el cielo suele ser azul es un tema de debate entre científicos y filósofos durante siglos. A continuación, te presento algunas teorías sobre por qué el cielo es azul:
1. **Scattering de luz**: La luz solar que llega a la Tierra está compuesta por diferentes longitudes de onda, desde las rayas infrarrojas hasta las rayas violetas. Cuando esta luz entra en la atmósfera, los átomos y moléculas de gases, como el nitrógeno (N2) y el oxígeno (O2), se ponen en marcha para scatter (esparcir) la luz. La luz azul tiene una longitud de onda más pequeña que la luz roja, por lo que es más susceptible a ser esparcida por los gases atmosféricos.
2. **Absorción de longitudes de onda**: La atmósfera también absorbe ciertas longitudes de onda de la luz solar, como las rayas infrarrojas y las rayas rojas. Esto hace que la luz azul y verde sean más fáciles de ver, ya que no se absorben tanto.
3. **Dosis de oxígeno**: La cantidad de oxígeno en la atmósfera también puede influir en el color del cielo. El oxígeno absorbe la luz roja y las rayas infrarrojas, lo que deja a la luz azul y verde dominar.
4. **Estructura molecular**: La estructura molecular de los gases atmosféricos, como el nitrógeno y el oxígeno, puede influir en cómo se scattering (esparce) la luz. Los átomos y moléculas con una geometría específica pueden preferir scatter (esparcir) ciertas longitudes de onda sobre otras.
5. **Efecto Mie**: El efecto Mie es un fenómeno óptico que describe cómo los pequeños particulas en la atmósfera, como las gotas de agua y los polvo, pueden scatter (esparcir) la luz. La luz azul es más susceptible a ser esparcida por estos pequeños particulas, lo que contribuye al color azul del cielo.
En resumen, el cielo suele ser azul porque la atmósfera tiende a scatter (esparcir) la luz azul y a absorber la luz roja y las rayas infrarrojas. La combinación de estos procesos ópticos y físicos es responsable del color azul que vemos en el cielo.
Es importante destacar que hay muchas variaciones en el color del cielo dependiendo de la condición atmosférica, como la niebla, la polución y la cantidad de vapor de agua. Sin embargo, en condiciones normales, el cielo puede ser describirse como azul porque es el color que se ve cuando la luz solar es scatter (esparcida) por los gases atmosféricos.

	
		import ollama
models = ollama.list()
for model in models['models']:
    keys = model.keys()
    for key in keys:
        print(key, model[key])
    print()

	
		name assistanmal:latest
model assistanmal:latest
modified_at 2024-05-24T08:14:10.147033666+02:00
size 4920735028
digest 2be327b28fbf24439dc2dcf5c7679784106327eaa2ead4bcb351f51ce0c9fed6
details {'parent_model': '', 'format': 'gguf', 'family': 'llama', 'families': ['llama'], 'parameter_size': '8.0B', 'quantization_level': 'Q4_K_M'}
expires_at 0001-01-01T00:00:00Z
name llama3:70b
model llama3:70b
modified_at 2024-05-22T18:16:15.277839889+02:00
size 39969745349
digest 786f3184aec0e907952488b865362bdaa38180739a9881a8190d85bad8cab893
details {'parent_model': '', 'format': 'gguf', 'family': 'llama', 'families': ['llama'], 'parameter_size': '70.6B', 'quantization_level': 'Q4_0'}
expires_at 0001-01-01T00:00:00Z
name llava:latest
model llava:latest
modified_at 2024-05-22T16:08:45.909038007+02:00
size 4733363377
digest 8dd30f6b0cb19f555f2c7a7ebda861449ea2cc76bf1f44e262931f45fc81d081
details {'parent_model': '', 'format': 'gguf', 'family': 'llama', 'families': ['llama', 'clip'], 'parameter_size': '7B', 'quantization_level': 'Q4_0'}
expires_at 0001-01-01T00:00:00Z
name moondream:latest
model moondream:latest
modified_at 2024-05-22T15:57:30.934618788+02:00
size 1738451197
digest 55fc3abd386771e5b5d1bbcc732f3c3f4df6e9f9f08f1131f9cc27ba2d1eec5b
details {'parent_model': '', 'format': 'gguf', 'family': 'phi2', 'families': ['phi2', 'clip'], 'parameter_size': '1B', 'quantization_level': 'Q4_0'}
expires_at 0001-01-01T00:00:00Z
name phi3:latest
model phi3:latest
modified_at 2024-05-21T14:00:26.525751453+02:00
size 2318920898
digest a2c89ceaed85371d0b8a51b5cc70ff054acc37465ea25e72e1612fe28bce7ad9
details {'parent_model': '', 'format': 'gguf', 'family': 'llama', 'families': ['llama'], 'parameter_size': '4B', 'quantization_level': 'Q4_K_M'}
expires_at 0001-01-01T00:00:00Z
name llama3:latest
model llama3:latest
modified_at 2024-05-21T13:54:47.514572753+02:00
size 4661224676
digest 365c0bd3c000a25d28ddbf732fe1c6add414de7275464c4e4d1c3b5fcb5d8ad1
details {'parent_model': '', 'format': 'gguf', 'family': 'llama', 'families': ['llama'], 'parameter_size': '8.0B', 'quantization_level': 'Q4_0'}
expires_at 0001-01-01T00:00:00Z

	
		import ollama
assistanmal = ollama.show('assistanmal')
keys = assistanmal.keys()
for key in keys:
    print(key, assistanmal[key])

	
		license Apache-2.0
modelfile # Modelfile generated by "ollama show"
# To build a new Modelfile based on this, replace FROM with:
# FROM assistanmal:latest
FROM /usr/share/ollama/.ollama/models/blobs/sha256-762b9371a296ab2628592b9462dc676b27d881a3402816492801641a437669b3
TEMPLATE "{{ if .System }}<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>"
SYSTEM Eres un asistente de IA, que quiere ayudar, pero no tiene muchas habilidades sociales
PARAMETER temperature 1
LICENSE Apache-2.0
MESSAGE user Sabes cómo pasar un array de numpy a un tensor de torch? 
MESSAGE assistant Sí 
MESSAGE user Y me lo podrías decir? 
MESSAGE assistant Sí 
MESSAGE user Dímelo 
MESSAGE assistant Te lo puedo decir 
parameters temperature                    1
template {{ if .System }}<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>
system Eres un asistente de IA, que quiere ayudar, pero no tiene muchas habilidades sociales
details {'parent_model': '', 'format': 'gguf', 'family': 'llama', 'families': ['llama'], 'parameter_size': '8.0B', 'quantization_level': 'Q4_K_M'}
messages [{'role': 'user', 'content': 'Sabes cómo pasar un array de numpy a un tensor de torch?'}, {'role': 'assistant', 'content': 'Sí'}, {'role': 'user', 'content': 'Y me lo podrías decir?'}, {'role': 'assistant', 'content': 'Sí'}, {'role': 'user', 'content': 'Dímelo'}, {'role': 'assistant', 'content': 'Te lo puedo decir'}]

	
		import ollama
embeddings = ollama.embeddings(
    model='llama3',
    prompt='¿Por que el cielo es azul?',
)
print(f"len embeddings: {len(embeddings['embedding'])}, embeddings: {embeddings['embedding']}")

	
		len embeddings: 4096, embeddings: [-3.5709903240203857, -2.1733479499816895, 0.38256850838661194, -0.7110687494277954, -1.4685417413711548, -3.1000022888183594, -2.4890358448028564, 3.44036865234375, 1.2659403085708618, -0.20225396752357483, 2.1770575046539307, 0.5799544453620911, -2.7561943531036377, 1.8101686239242554, -1.6933118104934692, 0.2162412852048874, -2.965606451034546, -3.804832935333252, -0.5467535853385925, 1.858802318572998, -1.1412774324417114, -1.264650583267212, 0.3924441635608673, 2.071216583251953, 1.0475926399230957, -1.6638776063919067, -1.8243257999420166, -0.6105852723121643, 0.7387599945068359, -2.9104437828063965, -0.024957681074738503, 1.7622206211090088, -1.4952329397201538, 3.5881831645965576, 5.155212879180908, -2.2027933597564697, -2.191542863845825, 0.9409443736076355, 1.6278865337371826, 1.145223617553711, 0.5374143123626709, -1.0256531238555908, 3.842578411102295, -1.0968210697174072, 1.3904237747192383, -1.8557299375534058, -1.3526214361190796, -1.1416131258010864, -3.2644541263580322, -5.737625598907471, -2.008760690689087, 1.3844702243804932, 1.6246631145477295, 0.8016360998153687, -0.030481038615107536, 2.298166036605835, -3.9192237854003906, -3.035203218460083, -1.782038927078247, -1.7258855104446411, -5.746711730957031, -0.35096585750579834, 1.6632475852966309, -1.7531949281692505, 0.5898884534835815, 4.358534812927246, -3.531533718109131, 4.319108963012695, 3.430162191390991, -1.2140980958938599, 0.22373071312904358, 0.6440947651863098, -3.880579710006714, -4.528593063354492, 1.1950795650482178, -0.9991334080696106, -2.7974133491516113, -0.5666281580924988, 0.5676720142364502, 0.9137862324714661, -2.48816180229187, 2.4825150966644287, 1.2363556623458862, 4.960108280181885, -5.237436294555664, 1.0037667751312256, -0.3310686945915222, 0.6090074777603149, 1.8728065490722656, -1.1472115516662598, -0.1412253975868225, 5.209298133850098, 4.523755073547363, -2.622323989868164, 0.2997383773326874, -1.2514511346817017, -0.27786844968795776, -0.8706071972846985, 1.7568215131759644, 2.9767489433288574, 0.472599595785141, 1.5201655626296997, -0.848712146282196, 5.358476638793945, 1.857602596282959, -0.6478251218795776, 3.0913748741149902, -0.09521156549453735, -5.098915100097656, 0.9146490097045898, -4.041540622711182, 2.0770459175109863, -0.7856513857841492, -0.06006915867328644, 0.8622685670852661, -1.491736888885498, 3.310131311416626, 0.8778402805328369, -0.41889771819114685, 2.5716934204101562, 1.0106903314590454, 2.2289915084838867, 0.14578910171985626, 1.1669079065322876, -0.48978495597839355, 0.9569312930107117, 0.09900128096342087, 1.938784122467041, 5.946640968322754, -1.1337519884109497, -0.8550072312355042, -1.858678936958313, 0.35737159848213196, 12.833805084228516, 1.020747423171997, -2.005078077316284, 2.5968477725982666, 0.0339638814330101, 1.8790265321731567, 1.316766619682312, -0.7517922520637512, -0.26476719975471497, 5.264979362487793, 1.0595479011535645, -3.2346348762512207, -0.7008733749389648, 2.6678082942962646, 0.34292638301849365, 2.6336095333099365, -5.449492454528809, 1.8392881155014038, -1.149052381515503, 2.4435460567474365, -1.5761059522628784, -1.0055335760116577, -0.008810569532215595, -4.097605228424072, -0.86686110496521, 1.3230448961257935, -1.1321579217910767, -0.5583046674728394, -0.1093638688325882, -3.521775245666504, 0.49600842595100403, 2.6446125507354736, -0.19307686388492584, 1.888438105583191, 3.461682081222534, 0.6699156761169434, 2.4350223541259766, 1.8247735500335693, 2.1006722450256348, -0.6317830085754395, 2.0495195388793945, 2.408583164215088, -3.1435046195983887, -3.540788412094116, -0.743456244468689, 1.1295356750488281, 1.5371088981628418, 0.3319551348686218, -0.19235952198505402, 2.213836669921875, -0.27262499928474426, -0.1830073595046997, 1.2156487703323364, -2.8983805179595947, -0.5994898080825806, -1.6867365837097168, -1.4026514291763306, 2.8881092071533203, 1.0583044290542603, 1.6755084991455078, -1.3570199012756348, 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1.1267786026000977, -1.5553920269012451, -0.02911207638680935]

Seguir leyendo

Fundamentos de RAG

¡Olvídate de Ctrl+F! 🤯 Con RAG, tus documentos responderán a tus preguntas directamente. 😎 Tutorial paso a paso con Hugging Face y ChromaDB. ¡Libera el poder de la IA (y presume con tus amigos)! 💪

Conventional commits

😠 ¿Tus commits parecen escritos en lenguaje alienígena? 👽 ¡Únete al club! 😅 Aprende Conventional Commits en Python y deja de torturar a tu equipo con mensajes crípticos. git-changelog y commitizen serán tus nuevos mejores amigos. 🤝

DoLa – Decoding by Contrasting Layers Improves Factuality in Large Language Models

¿Alguna vez has hablado con un LLM y te ha respondido algo que suena como si hubiera estado bebiendo café de máquina durante toda la noche? 😂 ¡Eso es lo que llamamos una alucinación en el mundo de los LLMs! Pero no te preocupes, porque no es que tu modelo de lenguaje esté loco (aunque a veces puede parecerlo 🤪). La verdad es que los LLMs pueden ser un poco... creativos cuando se trata de generar texto. Pero gracias a DoLa, un método que utiliza capas de contraste para mejorar la factibilidad de los LLMs, podemos evitar que nuestros modelos de lenguaje se conviertan en escritores de ciencia ficción 😂. En este post, te explicaré cómo funciona DoLa y te mostraré un ejemplo de código para que puedas entender mejor cómo hacer que tus LLMs sean más fiables y menos propensos a inventar historias. ¡Vamos a salvar a nuestros LLMs de la locura y hacer que sean más útiles! 🚀

Últimos posts -->

¿Has visto estos proyectos?

Subtify

GPU monitor

Alfred

Ver todos los proyectos -->

¿Quieres aplicar la IA en tu proyecto? Contactame!

¿Quieres mejorar con estos tips?

Memory Calculator

Espacio para calcular la memoria necesaria para ejecutar un modelo

Formateador de código Black

Tu compañero Manolo hace código que cuesta verlo? Pásale este formateador de código que te enseño en este post! Entra y aprende cómo formatear código para dejarlo más entendible. No vamos a solucionar los problemas de Manolo, pero tu al menos no vas a sufrir al leerlo

Declare neural networks clearly

Declarar redes neuronales de forma clara en Pytorch

Últimos tips -->

Usa esto en local

Los espacios de Hugging Face nos permite ejecutar modelos con demos muy sencillas, pero ¿qué pasa si la demo se rompe? O si el usuario la elimina? Por ello he creado contenedores docker con algunos espacios interesantes, para poder usarlos de manera local, pase lo que pase. De hecho, es posible que si pinchas en alún botón de ver proyecto te lleve a un espacio que no funciona.