Ollama

25 of may of 2021

Disclaimer: This post has been translated to English using a machine translation model. Please, let me know if you find any mistakes.

Ollama is a software based on llama.cpp for LLM inference.

llama.cpp is a library for LLM inference in C/C++, and Ollama has been built on top of it to make usage easier for any type of user. In other words, it is a wrapper to use llama.cpp with a Python, JavaScript, and terminal API.

Installation

To install Ollama on Windows or Mac, you need to download an installer from their downloads page. However, if you use Linux, as in my case, you have to run the following command

``` bash

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

Run

To run an LLM, it's enough to execute ollama run <LLM>, for example, I can use llama3 by

	
		!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
&gt;&gt;&gt; hola
Hola! ¿Cómo estás?
&gt;&gt;&gt; Send a message (/? for help)

As can be seen, llama works like docker. As I didn't have llama3 downloaded, it has been downloaded and then we can use it.

Models

If we want to know which models are available, we can go to their models page, where all the available models can be seen.

Download a model

If we want to download a model, we will do it the same way as with Docker ollama pull <LLM>, for example, I am going to download 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

Help

If we want to see all the commands available, we can use 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.

List the available models

We can see in the help that if we run ollama list we will be able to see all available models.

	
		!ollama list

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

And, as we can see, we only have the two that we have downloaded so far.

See the models running

If we run ollama ps we can see the models we have running

	
		!ollama ps

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

We see that we have llama3 running because at the beginning we did ollama run llama3, but if we now try it with phi3

	
		!ollama run phi3

	
		&gt;&gt;&gt; Send a message (/? for help)

If we now run ollama ps again

	
		!ollama ps

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

We can see that the model running is phi3

Multimodal Models

In the list of Ollama models, there are multimodal models like moondream2, which, despite having only 1.8B parameters, performs very well, so we are going to download it to test it.

	
		!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

Once downloaded, we are going to ask about this image

For that, we have to pass it in the prompt the path where we have the image

	
		!ollama run moondream

	
		&gt;&gt;&gt; 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.'

Multiline prompts

When we need to write more than one line in our prompt, we just have to put it between triple quotes """

	
		!ollama run llama3

&gt;&gt;&gt; """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.

GGUF Files

As we have said, ollama is based on llama.cpp, so the type of models used by ollama are those used by llama.cpp and they are GGUF files.

The GGUF files refer to the Generalized GPU-Friendly Unified Format, which is designed to store and

run large language models (LLMs) efficiently on GPU-accelerated hardware.

The GGUF format is specifically optimized for storing and executing large language models, such as transformer-based architectures. It provides a compact and efficient way to represent the model weights, activations, and computation graphs, which

Allows faster inference times and reduced memory usage.

GGUF files usually contain the following components:

Model architecture: The GGUF file stores the model architecture, including the number of layers, hidden sizes, and attention mechanisms.
Weights: The model weights are stored in a compressed format to reduce memory usage. That is, the models are quantized.
Activations: The GGUF file contains pre-computed activations for certain layers or sub-layers, which can be reused during inference.
Computation graph: The computation graph represents the sequence of operations necessary to perform inference with the model.

By using GGUF files, developers can deploy large language models on a variety of hardware,

Including GPUs, TPUs, and CPUs, while achieving faster inference times and improved performance.

For example, the 70B parameter llama3 model in FP32 should take up 280 GB, in FP16 it should take up 140 GB, and in INT8 it should take up 70GB. However, when we go to download it, the website states that it takes up 40 GB, so since it takes up a little more than half of what it would in INT8, we can assume it is quantized to INT4.

Since I have 2 GPUs with 24 GB each, I could use them between the two GPUs if I load it in parallel on both. Therefore, I'm going to download it and give it a try.

	
		!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

I test it

	
		!ollama run llama3:70B

	
		&gt;&gt;&gt; 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!

Not only does it answer correctly, but it's also incredibly fast.

Moreover, it is verified that the model has been loaded onto my two GPUs in parallel.

This can be assumed, because in a single one it wouldn't fit as I mentioned before. But moreover, I have created a GPU monitor, with which I can see that both GPUs are almost full.

From this, we can draw another conclusion: through the GGUF files, we can run LLMs in the most efficient way on our computers. In my case, I have loaded the LLM in parallel on both of my GPUs.

Modelfile

Now that we've seen what GGUF files are, we can look at how to create new ones from others. Just like with Docker, where we can create images based on others using a Dockerfile, Ollama allows us to create models based on others using a modelfile.

For this, we will start with the file Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf, which is the GGUF file of the model NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF. This model is an 8B parameter version of llama3.

When creating models and uploading them to the internet, it's best to give them descriptive names. For example, in this case, we can see that we have in the name

Llama-3-Instruct: This means it is a model that builds upon Llama3 8B Instruct
Merged: It is a merged model* DPO: The alignment with DPO has been done
Q4: It is quantized to 4 bits

First, we are going to create the folder where we will create the modelfile

	
		!mkdir ollama_modelfile

Now we create the modelfile

	
		!touch ollama_modelfile/Modelfile

And now we download the GGUF file

	
		!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&amp;Expires=1716697914&amp;Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxNjY5NzkxNH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzQ4LzBkLzQ4MGQxYjllYTNkZjgxYjZjZjk3NGQ2MmI2YmMwNGY0MDJhYzU2MDBhMWRmZGRlN2JiYjllODE5YTExMWM1NzIvNzYyYjkzNzFhMjk2YWIyNjI4NTkyYjk0NjJkYzY3NmIyN2Q4ODFhMzQwMjgxNjQ5MjgwMTY0MWE0Mzc2NjliMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&amp;Signature=LQecPNccMxv4R-BowDrcwORGpk%7ETTh-gVmMrAzzmdT8EFDVXNH7KTwmqKF-pEAbbir9iRS1pX7NFCterYF0w5UMe%7EWkvGg46Z73yMn3s96hm7edWypv9OCMFuBvcp8arYonKljUQ4UvdQKFHXAZxKhLrx6OVqx9YoTvHuXmpBoKXAPi1XNLPwY0YTX4wn3DnAFFzkHOsDK7p2TJ3z7RFzmfyZg%7E5daWm008oGBvIV192MKk5uqQGwqv3l-4mi2Nd8eOwMW2Yfzigz7EZJgPM7UDYzin5vABsnyXJorOSRdri13hlC-AMaY9db76vLsT1NBvVU1xVtWxAylDqU6XYlQ__&amp;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&amp;Expires=1716697914&amp;Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxNjY5NzkxNH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzQ4LzBkLzQ4MGQxYjllYTNkZjgxYjZjZjk3NGQ2MmI2YmMwNGY0MDJhYzU2MDBhMWRmZGRlN2JiYjllODE5YTExMWM1NzIvNzYyYjkzNzFhMjk2YWIyNjI4NTkyYjk0NjJkYzY3NmIyN2Q4ODFhMzQwMjgxNjQ5MjgwMTY0MWE0Mzc2NjliMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&amp;Signature=LQecPNccMxv4R-BowDrcwORGpk%7ETTh-gVmMrAzzmdT8EFDVXNH7KTwmqKF-pEAbbir9iRS1pX7NFCterYF0w5UMe%7EWkvGg46Z73yMn3s96hm7edWypv9OCMFuBvcp8arYonKljUQ4UvdQKFHXAZxKhLrx6OVqx9YoTvHuXmpBoKXAPi1XNLPwY0YTX4wn3DnAFFzkHOsDK7p2TJ3z7RFzmfyZg%7E5daWm008oGBvIV192MKk5uqQGwqv3l-4mi2Nd8eOwMW2Yfzigz7EZJgPM7UDYzin5vABsnyXJorOSRdri13hlC-AMaY9db76vLsT1NBvVU1xVtWxAylDqU6XYlQ__&amp;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%[===================&gt;]   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]

Once downloaded, we start creating the modelfile

FROM

First, we need to specify the model we are starting from.

We can start from a model that is in the Ollama hub, for example we could do

FROM llama3

But since we are starting from the file we have downloaded, we specify it through the 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

Now we need to set the values of the LLM parameters, for example, if we want to modify the value of the temperature we set

``` yaml

PARAMETER temperature 0.5```

The parameters we can modify are

Parameter	Description	Value Type	Example Usage
mirostat	Enable `Mirostat` sampling to control perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)	int	mirostat 0
mirostat_eta	Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher rate will make the algorithm more sensitive. (Default: 0.1)	float	mirostat_eta 0.1
mirostat_tau	Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)	float	mirostat_tau 5.0
num_ctx	Set the size of the context window used to generate the next token. (Default: 4096)	int	num_ctx 4096
repeat_last_n	Sets how far back the model should look to avoid repetitions. (Default: 64, 0 = disabled, -1 = num_ctx)	int	repeat_last_n 64
repeat_penalty	Sets the intensity with which repetitions are penalized. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)	float	repeat_penalty 1.1
temperature	The model's temperature. Increasing the temperature will make the model respond more creatively. (Default: 0.8)	float	temperature 0.7
seed	Sets the random number seed for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0)	int	seed 42
stop	Sets the stop sequences to be used. When this pattern is encountered, the LLM will stop generating text and return. Multiple stop patterns can be set by specifying multiple stop parameters in a model file.	string	stop "AI assistant:"
tfs_z	Tail-free sampling is used to reduce the impact of less probable tokens on the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)	float	tfs_z 1
num_predict	Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)	int	num_predict 42
top_k	Reduce the probability of generating nonsense. A higher value (e.g., 100) will give more diverse responses, while a lower value (e.g., 10) will be more conservative. (Default: 40)	int	top_k 40
top_p	Works in conjunction with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)	float	top_p 0.9

Let's check the parameters of the llama3 model

	
		!ollama show llama3 --modelfile | grep PARAMETER

	
		PARAMETER num_keep 24
PARAMETER stop "&lt;|start_header_id|&gt;"
PARAMETER stop "&lt;|end_header_id|&gt;"
PARAMETER stop "&lt;|eot_id|&gt;"

We can see that it has a parameter num_keep = 24 which is not in Ollama's documentation and also three stop tokens

Let's make the model more creative by increasing the temperature and we'll keep the stop tokens, because otherwise the model won't stop generating text.

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

	
		Overwriting ollama_modelfile/Modelfile

SYSTEM

We can condition the model with a system message

	
		%%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

This is the template we will pass to the model, the types of data it can include are

Variable	Description
{{ .System }}	The system message used to specify custom behavior.
{{ .Prompt }}	The user warning message.
{{ .Response }}	The model's response. When generating a response, the text after this variable is omitted.

As seen, this is not very intuitive, so let's look at an example. Imagine we have the following template:

TEMPLATE """{{ if .System }}
      {{ .System }}
      }}{ if .Prompt }}
      It seems like there was an issue with the input. Could you please provide the Markdown text to translate?
      }}
      """

If we pass the model the following prompt Hello, how are you?, the template will do three things

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

If there is a system message, it is passed to the model in the following way

System message

As in our case there is a system message, it will pass

You are an AI assistant who wants to help but doesn't have many social skills

{{ if .Prompt }}<|im_start|>user{{ .Prompt }}<|im_end|>}}{ end }}:

If there is a user message, it is passed to the model in the following way

As in our case there is a user message (the prompt), it will pass

Hello, how are you?

It will always happen to the model

so the LLM knows it's their turn to respond now

After looking at it piece by piece, we can put together the full message that will be passed to the LLM and it is

You are an AI assistant who wants to help but doesn't have many social skills.
      Hello, how are you?
      Sure, please provide the Markdown text you want to be translated into English.

Let's see how the llama3 template is

	
		!ollama show llama3 --modelfile | grep {{

	
		TEMPLATE "{{ if .System }}&lt;|start_header_id|&gt;system&lt;|end_header_id|&gt;
{{ .System }}&lt;|eot_id|&gt;{{ end }}{{ if .Prompt }}&lt;|start_header_id|&gt;user&lt;|end_header_id|&gt;
{{ .Prompt }}&lt;|eot_id|&gt;{{ end }}&lt;|start_header_id|&gt;assistant&lt;|end_header_id|&gt;
{{ .Response }}&lt;|eot_id|&gt;"

It is very similar to the one we have seen with the llama3 template, in our case it will be passed to the LLM

You are an AI assistant who wants to help but doesn't have many social skills.
      Hello, how are you?<|start_header_id|>assistant<|end_header_id|>{{ .Response }}<|eot_id|>

As we are going to use a model based on llama3, we will use its same 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 }}&lt;|start_header_id|&gt;system&lt;|end_header_id|&gt;
{{ .System }}&lt;|eot_id|&gt;{{ end }}{{ if .Prompt }}&lt;|start_header_id|&gt;user&lt;|end_header_id|&gt;
{{ .Prompt }}&lt;|eot_id|&gt;{{ end }}&lt;|start_header_id|&gt;assistant&lt;|end_header_id|&gt;
{{ .Response }}&lt;|eot_id|&gt;"""

	
		Overwriting ollama_modelfile/Modelfile

LICENSE

It is used to set the license of the model

	
		%%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 }}&lt;|start_header_id|&gt;system&lt;|end_header_id|&gt;
{{ .System }}&lt;|eot_id|&gt;{{ end }}{{ if .Prompt }}&lt;|start_header_id|&gt;user&lt;|end_header_id|&gt;
{{ .Prompt }}&lt;|eot_id|&gt;{{ end }}&lt;|start_header_id|&gt;assistant&lt;|end_header_id|&gt;
{{ .Response }}&lt;|eot_id|&gt;"""
 
LICENSE """Apache-2.0"""

	
		Overwriting ollama_modelfile/Modelfile

MESSAGE

We can pass it a chat history

	
		%%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 }}&lt;|start_header_id|&gt;system&lt;|end_header_id|&gt;
{{ .System }}&lt;|eot_id|&gt;{{ end }}{{ if .Prompt }}&lt;|start_header_id|&gt;user&lt;|end_header_id|&gt;
{{ .Prompt }}&lt;|eot_id|&gt;{{ end }}&lt;|start_header_id|&gt;assistant&lt;|end_header_id|&gt;
{{ .Response }}&lt;|eot_id|&gt;"""
 
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

An adapter is a matrix that is added to the model. It is used to avoid fine-tuning the entire LLM and instead only fine-tune this matrix, so that when it is added to the model, the LLM behaves as we want it to.

To do this, you pass the path with the adapter file.

ADAPTER ./ollama-lora.bin

Create the model

Once the modelfile is written, we can create the model using

``` bash

ollama create -f ```

	
		!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

We see that we have very similar output to what we get when we create a Docker image

If we now list the models that we have available

	
		!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

We see that the assistant we just created is there. Let's test it.

	
		!ollama run assistanmal

	
		&gt;&gt;&gt; Sabes cómo pasar un array de numpy a un tensor de torch?
Sí
&gt;&gt;&gt; Y me lo podrías decir?
Sí
&gt;&gt;&gt; Dímelo
Te lo puedo decir
&gt;&gt;&gt; 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])

REST API

By default, ollama runs on port 11434 of our localhost and since it has a REST API implemented, we can make calls through this API.

We can query an LLM using the POST method, for which we need to pass it a json with, at least, the model and the 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}

As we can see, it has responded to us via stream. If we don't want this and prefer a single response, we need to pass "stream": false in the 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

To use the Python API, we have to install the Ollama Python package.

``` bash

pip install ollama```

Once installed, we have the following methods

ollama.chat: To start a chat with a model
ollama.generate: To generate a response to a prompt
ollama.list: Lists the available models
ollama.show: Shows information about a model
ollama.create: Creates a model
ollama.delete: Deletes a model
ollama.pull: Downloads a model
ollama.push: Uploads a model
ollama.embeddings: Returns the embeddings of a prompt

Let's try some

	
		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 }}&lt;|start_header_id|&gt;system&lt;|end_header_id|&gt;
{{ .System }}&lt;|eot_id|&gt;{{ end }}{{ if .Prompt }}&lt;|start_header_id|&gt;user&lt;|end_header_id|&gt;
{{ .Prompt }}&lt;|eot_id|&gt;{{ end }}&lt;|start_header_id|&gt;assistant&lt;|end_header_id|&gt;
{{ .Response }}&lt;|eot_id|&gt;"
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 }}&lt;|start_header_id|&gt;system&lt;|end_header_id|&gt;
{{ .System }}&lt;|eot_id|&gt;{{ end }}{{ if .Prompt }}&lt;|start_header_id|&gt;user&lt;|end_header_id|&gt;
{{ .Prompt }}&lt;|eot_id|&gt;{{ end }}&lt;|start_header_id|&gt;assistant&lt;|end_header_id|&gt;
{{ .Response }}&lt;|eot_id|&gt;
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, -0.02863301709294319, 0.3893628418445587, 2.2699222564697266, 2.0410315990448, 0.5284076929092407, -1.3849315643310547, -3.2410078048706055, -5.413166522979736, -0.18938720226287842, -7.636954307556152, 1.0516839027404785, 1.8927298784255981, -1.8319122791290283, -0.3104879856109619, -3.192800283432007, 0.7249773144721985, 2.4628212451934814, -5.237030029296875, -1.6819825172424316, -2.6519153118133545, -5.452773094177246, 2.5719199180603027, 4.594746112823486, 0.26059404015541077, -0.5879826545715332, -2.012744426727295, 4.347339630126953, 1.1387996673583984, -1.585600733757019, -1.8488795757293701, 0.43234241008758545, -2.1706769466400146, 2.02614426612854, -1.3547757863998413, -0.23999670147895813, 0.5241819620132446, -1.751906394958496, -2.181796073913574, 2.4499764442443848, -1.9204010963439941, 1.730446696281433, 0.12385261058807373, 3.325094699859619, 1.1947877407073975, -0.759490430355072, 2.091949224472046, -2.352848529815674, 0.8918092846870422, -2.944584608078003, 2.403661012649536, -1.3479825258255005, 0.06610975414514542, -3.977243185043335, 1.213283896446228, -2.078611373901367, -0.5690178871154785, -2.212489366531372, 2.329470634460449, 4.6698198318481445, -2.2548561096191406, -0.6902344226837158, 3.2000536918640137, -3.4179816246032715, 2.3877899646759033, 0.803355872631073, 0.7195305824279785, -0.05020619183778763, -2.350513219833374, 0.8627676963806152, 0.892014741897583, -1.1318942308425903, -2.360058546066284, -0.9107275605201721, 5.429220199584961, 0.3160887658596039, -0.5821715593338013, -0.05387498438358307, -1.9310585260391235, 1.1864994764328003, -0.5223034024238586, -0.14695927500724792, -1.3558963537216187, 3.2761178016662598, 2.1174533367156982, -3.3351476192474365, -1.919097661972046, -1.6523269414901733, 8.498637199401855, 1.5841808319091797, 0.8351742029190063, -0.47643807530403137, 0.4335901737213135, 0.8168123960494995, -1.606881022453308, 3.033907651901245, -4.34182596206665, 13.113082885742188, -1.7362353801727295, 2.71997332572937, -2.058112382888794, -3.530165433883667, 0.6679645776748657, -1.8624101877212524, -2.792393922805786, 0.3409225642681122, -1.988881230354309, 0.2653841972351074, 0.8095135688781738, -0.4962392747402191, -3.7835464477539062, 1.6932401657104492, -1.4169583320617676, 0.5265898108482361, 0.3822944760322571, -0.40417489409446716, 1.0212734937667847, 2.9532642364501953, 2.503154754638672, 0.29280903935432434, 0.48731330037117004, 0.1463029682636261, -0.8889073729515076, -4.234628677368164, -3.614084243774414, -0.2760423719882965, -0.026265254244208336, 3.186485528945923, 0.9897671341896057, 3.198659896850586, 2.4788753986358643, -1.439963459968567, 1.8077031373977661, 1.842383623123169, 1.8660616874694824, -2.531989574432373, 0.6639688014984131, 3.2474002838134766, 2.137742519378662, 0.994397759437561, 3.4729905128479004, 2.8363358974456787, -0.06270971894264221, 5.061746120452881, -1.3068524599075317, 3.0064680576324463, -2.5484275817871094, 1.1107360124588013, 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MCP: Complete Guide to Create servers and clients MCP (Model Context Protocol) with FastMCP

Learn what is the Model Context Protocol (MCP), the open-source standard developed by Anthropic that revolutionizes how AI models interact with external tools. In this practical and detailed guide, I take you step by step in creating an MCP server and client from scratch using the fastmcp library. You will build an "intelligent" AI agent with Claude Sonnet, capable of interacting with the GitHub API to query issues and repository information. We will cover from basic concepts to advanced features like filtering tools by tags, server composition, static resources and dynamic templates (resource templates), prompt generation, and secure authentication. Discover how MCP can standardize and simplify the integration of tools in your AI applications, analogously to how USB unified peripherals!

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🚀 Revolutionize your AI agents! 🧠 LangGraph is not just another library, it's the orchestration framework that gives you total control to build complex agents, with long-term memory and even human intervention! Say goodbye to basic chatbots, it's time to create true intelligence. Dive into this post and discover it!

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Use this locally

Hugging Face spaces allow us to run models with very simple demos, but what if the demo breaks? Or if the user deletes it? That's why I've created docker containers with some interesting spaces, to be able to use them locally, whatever happens. In fact, if you click on any project view button, it may take you to a space that doesn't work.