Ollama

25 of may of 2021

Ollama

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

llama.cpp is a library for LLMs inference in C/C++, so Ollama has been built on top of it making it easier to use for any kind of user. That is, it is a wrapper to be able to use llama.cpp with a Python, JavaScript and terminal API.

This notebook has been automatically translated to make it accessible to more people, please let me know if you see any typos.

Installation

To install ollama on windows or mac you have to download an installer from their [downloads] page (https://ollama.com/download), however, if you use linux, as in my case, you have to run the following command

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

Run

To run an LLM it is enough to execute ollama run <LLLM>, for example I can use llama3 using

	
		!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)

As you can see ollama works like docker, as it did not have llama3 downloaded, it has downloaded it and then we can use it.

Models

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

Download a model

If we want to download a model, we will do the same as with docker ollama pull <LLM>, for example, I will 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 we have 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 available models

We can see in the help that if we make ollama list we can see all the 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 up to now

See models running

If we do call 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 phi3

	
		!ollama run phi3

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

If we now go back to making call ps.

	
		!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 ollama model list there are multimodal models, such as moondream2, which being a model with only 1.8B of parameters has a very good performance, so let's 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 for this image

this_is_fine

To do this we have to pass in the prompt the path where we have the image

	
		!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.'

Multiline Prompts

When we need to write more than one line in our prompt we simply have to put it in triple quotes """.

	
		!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.

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 are the GGUF tip files.

The GGUF files refer to the Generalized GPU-Friendly Unified Format file 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 model weights, activations, and computational 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: Model weights are stored in a compressed format to reduce memory usage. That is, the models are quantized 3. Activations: The GGUF file contains pre-computed activations for certain layers or sub-layers, which can be reused during inference. 4. Computation network: The computation network represents the sequence of operations required to perform inference with the model.

By using GGUF files, developers can implement large language models on a variety of hardware, including GPUs, TPUs and CPUs, while achieving faster inference times and improved performance.

For example, the model llama3 of 70B of paŕametros in FP32 should occupy 280 GB, in FP16 should occupy 140 GB and in INT8 should occupy 70GB. However, when we are going to download it, in the web it says that it occupies 40 GB, reason why when occupying a little more than half that in INT8 we can think that it is quantized in INT4.

As I have 2 GPUs of 24 GB I could use it between the two GPUs, if I load it in parallel on both of them, so I'm going to download it and try it out

	
		!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

	
		>>> 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 well, but it goes very fast.

It also shows that it has loaded the model on my two GPUs in parallel.

This can be assumed, because in a single one it would not fit as I have commented before. But I have also created a GPU monitor, with which I can see that the two GPUs are almost full.

From here we can draw another conclusion, and that is that through the GGUF files we can run the LLMs in the most efficient way in our computers, in my case I have loaded in parallel the LLM in my two GPUs.

Modelfile

Now that we have seen what GGUF files are, we can see how to create new ones from others. Just as with docker we can create images from other images with a dockerfile, ollama allows us to create models from other models with a modelfile.

For this we will start from the file Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf which is the GGUF file of the NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF model. This model is a flame3 version of 8B parameters.

When creating models and uploading them to the Internet, the ideal is to give them descriptive names, for example, in this case we see that we have in the name

Llama-3-Instruct: This means that it is a model that starts from llama3 8B Instruct.
Merged: It is a merge model.
DPO: Alignment with DPO has been done.
Q4: It is quantized to 4 bits.

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

	
		!mkdir ollama_modelfile

Now we create the modelfile

	
		!mkdir ollama_modelfile
!touch ollama_modelfile/Modelfile

And now we download the file 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]

Once downloaded we start to create the modelfile

FROM

First we have to specify which model we are starting from

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

FROM llama3

But as we are going to start from the file we have downloaded, we specify it by means of the path

FROM ./Hermes-2-Pro-Llamama-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 have to set the values of the LLM parameters, for example, if we want to modify the value of the temperature we put

PARAMETER temperature 0.5

The parameters that can be modified 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	Sets 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 temperature of the model. Increasing the temperature will make the model respond more creatively. (Default: 0.8)	float	temperature 0.7
seed	Sets the seed for random number 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 use. When this pattern is found, 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	Top-k sampling is used to reduce the impact of less likely 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	Reduces 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 together 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 look at the parameters of the llama3 model.

	
		!ollama show llama3 --modelfile | grep PARAMETER

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

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

Let's make the model more creative by raising the temperature and keep the stop tokens, otherwise the model will not 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 "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|eot_id|>"

	
		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

It is the template that we will pass to the model, the types of data that can be included are

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

As it is not very intuitive, let's see it with an example, let's imagine that we have the following template

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

If we pass the following prompt to the template 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

<|im_start|>system message <|im_end|>.

As in our case there is a system message, it will happen to you

`<|im_start|>You are an AI assistant, who wants to help, but does not have many social skills<|im_end|>``.
{{ 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

<|im_start|>user message <|im_end|>.

As in our case there is a user message (the prompt) it will be passed to you

<|im_start|>user Hi, how are you? <|im_end|>
<|im_start|>assistant:

It will always happen to the model

<|im_start|>assistant

so that the LLM knows that it is now his turn to respond

After looking at it in parts we can put the total message that will be passed to the LLM and it is

<|im_start|>You are an AI assistant, who wants to help, but does not have many social skills<|im_end|>.
      <|im_start|>user Hello, how are you? <|im_end|>
      <|im_start|>assistant

Let's see what the llama3 template looks like.

	
		!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|>"

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

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

As we are going to use a model based on llama3 we are going to use its 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

Serves to establish the model license

	
		%%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

We can pass you 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 }}<|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

An adapter is a matrix that is added to the model. It is used to avoid having to do fine tuning to the LLMs and to do it only to that matrix, so that when it is added to the model, the LLM behaves as we want it to.

So in order to do this we pass the path to the adapter file

ADAPTER ./ollama-lora.bin

Create the model

Once the modelfile has been written we can create the model by means of

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

We see that we get an output very similar to the one we get when we create a docker image

Yes, now we list the models 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 there is the wizard we just created. Let's try it out

	
		!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])

REST API

By default ollama runs on port 11434 of our localhost and as it has implemented a REST API we can make calls using this API

We can make a query to an LLM using the POST method, for this we have 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 answered us by stream, if we do not want this and we want a single answer we have 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 need to install the ollama Python package

pip install ollama

Once installed we have the following methods

ollama.chat`: To initiate a chat with a model
ollama.generate`: To generate a response to a prompt
ollama.list: List of available models
ollama.show`: Displays information of a model
ollama.create`: Creates a model
ollama.delete`: Deletes a template
ollama.pull`: Download a model
ollama.push`: Upload a model
ollama.embeddings`: Return embeddings from a prompt

Let's try some of them

	
		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, -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, 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RAG fundamentals

Forget about Ctrl+F! 🤯 With RAG, your documents will answer your questions directly. 😎 Step-by-step tutorial with Hugging Face and ChromaDB. Unleash the power of AI (and show off to your friends)! 💪

Conventional commits

😠 Are your commits written in alien language? 👽 Join the club! 😅 Learn Conventional Commits in Python and stop torturing your team with cryptic messages. git-changelog and commitizen will be your new best friends. 🤝

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

Have you ever talked to an LLM and they answered you something that sounds like they've been drinking machine coffee all night long 😂 That's what we call a hallucination in the LLM world! But don't worry, because it's not that your language model is crazy (although it can sometimes seem that way 🤪). The truth is that LLMs can be a bit... creative when it comes to generating text. But thanks to DoLa, a method that uses contrast layers to improve the feasibility of LLMs, we can keep our language models from turning into science fiction writers 😂. In this post, I'll explain how DoLa works and show you a code example so you can better understand how to make your LLMs more reliable and less prone to making up stories. Let's save our LLMs from insanity and make them more useful! 🚀

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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.