Ollama

25 de maio de 2021

Ollama

O Ollama é um software baseado em [llama.cpp] (https://github.com/ggerganov/llama.cpp) para inferência LLM.

llama.cpp é uma biblioteca para inferência LLM em C/C++, de modo que o Ollama foi desenvolvido com base nela, facilitando o uso por qualquer tipo de usuário. Ou seja, é um invólucro para poder usar llama.cpp com uma API Python, JavaScript e terminal.

Este caderno foi traduzido automaticamente para torná-lo acessível a mais pessoas, por favor me avise se você vir algum erro de digitação..

Instalação

Para instalar o ollama no Windows ou no Mac, é necessário fazer o download de um instalador na página [downloads] (https://ollama.com/download). No entanto, se você usa o Linux, como no meu caso, é necessário executar o seguinte comando

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

Executar

Para executar um LLM, basta executar ollama run <LLLM>, por exemplo, posso usar llama3 via

	
		!ollama run llama3

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

Como você pode ver, o ollama funciona como o docker, pois não tinha o llama3 baixado, ele o baixou e então podemos usá-lo.

Modelos

Se quisermos saber quais modelos estão disponíveis, podemos acessar a página models, onde é possível ver todos os modelos disponíveis.

Faça o download de um modelo

Se quisermos fazer o download de um modelo, faremos o mesmo que com o docker ollama pull <LLM>, por exemplo, farei o download do 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

Ajuda

Se quisermos ver todos os comandos disponíveis, podemos usar ollama -h.

	
		!ollama -h

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

Lista de modelos disponíveis

Podemos ver na ajuda que, se fizermos ollama list, poderemos ver todos os modelos disponíveis.

	
		!ollama list

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

E, como podemos ver, só temos os dois que baixamos até agora.

Veja os modelos em execução

Se fizermos call ps, poderemos ver os modelos em execução.

	
		!ollama ps

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

Podemos ver que temos o llama3 em execução porque, no início, fizemos ollama run llama3, mas se tentarmos agora phi3, veremos que temos o phi3.

	
		!ollama run phi3

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

Se agora voltarmos a fazer a 'chamada ps

	
		!ollama ps

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

Podemos ver que o modelo em execução é o phi3.

Modelos multimodais

Na lista de modelos do ollama, há modelos multimodais, como o moondream2, que, por ser um modelo com apenas 1,8B de parâmetros, tem um desempenho muito bom, portanto, vamos baixá-lo para testá-lo.

	
		!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

Após o download, solicitaremos essa imagem.

this_is_fine

Para fazer isso, precisamos passar o caminho onde temos a imagem no prompt.

	
		!ollama run moondream

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

Prompts de várias linhas

Quando precisamos escrever mais de uma linha em nosso prompt, basta colocá-la entre aspas triplas """.

	
		!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.

Arquivos GGUF

Conforme mencionado acima, o ollama é baseado no llama.cpp, portanto, os tipos de modelos usados pelo ollama são aqueles usados pelo llama.cpp e são os arquivos de dicas GGUF.

Os arquivos GGUF referem-se ao formato de arquivo Generalized GPU-Friendly Unified Format, que foi projetado para armazenar e executar modelos de linguagem grandes (LLMs) com eficiência em hardware acelerado por GPU.

O formato GGUF é especificamente otimizado para armazenar e executar modelos de linguagem grandes, como arquiteturas baseadas em transformadores. Ele oferece uma maneira compacta e eficiente de representar pesos de modelos, ativações e gráficos computacionais, que permite tempos de inferência mais rápidos e uso reduzido de memória.

Os arquivos GGUF normalmente contêm os seguintes componentes:

arquitetura do modelo: o arquivo GGUF armazena a arquitetura do modelo, incluindo o número de camadas, tamanhos ocultos e mecanismos de atenção.

2 Pesos: os pesos dos modelos são armazenados em um formato compactado para reduzir o uso da memória. Ou seja, os modelos são quantizados 3. acionadores: O arquivo GGUF contém ativações pré-calculadas para determinadas camadas ou subcamadas, que podem ser reutilizadas durante a inferência. 4. gráfico de computação: A rede de computação representa a sequência de operações necessárias para realizar a inferência com o modelo.

Usando arquivos GGUF, os desenvolvedores podem implementar grandes modelos de linguagem em uma variedade de hardwares, incluindo GPUs, TPUs e CPUs, obtendo tempos de inferência mais rápidos e melhor desempenho.

Por exemplo, o modelo llama3 de 70B paŕametre em FP32 deve ocupar 280 GB, em FP16 deve ocupar 140 GB e em INT8 deve ocupar 70 GB. No entanto, quando vamos fazer o download, a Web diz que ele ocupa 40 GB, portanto, quando ele ocupa um pouco mais da metade disso em INT8, podemos pensar que ele está quantizado em INT4.

Como tenho 2 GPUs de 24 GB, poderei usá-lo entre as duas GPUs, se eu carregá-lo em paralelo em ambas, então vou fazer o download e experimentar.

	
		!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

Eu testo

	
		!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!

Ele não apenas responde bem, mas também é muito rápido.

Ele também mostra que carregou o modelo em minhas duas GPUs em paralelo.

Isso pode ser presumido, pois uma delas não caberia, como eu disse antes. Mas também criei um [GPU monitor] (https://github.com/maximofn/gpu_monitor), com o qual posso ver que ambas as GPUs estão quase cheias.

A partir daqui, podemos tirar outra conclusão: por meio dos arquivos GGUF, podemos executar os LLMs da maneira mais eficiente em nossos computadores; no meu caso, ele carregou em paralelo o LLM em minhas duas GPUs.

Arquivo de modelo

Agora que já vimos o que são arquivos GGUF, podemos ver como criar novos arquivos a partir de outros. Assim como no docker podemos criar imagens a partir de imagens com um dockerfile, o ollama nos permite criar modelos a partir de modelos com um modelfile.

Para isso, começaremos com o arquivo Hermes-2-Pro-Llama-3-Instruct-Merged-DPO-Q4_K_M.gguf, que é o arquivo GGUF do modelo NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF. Esse modelo é uma versão 8B flame3 dos parâmetros

Ao criar modelos e carregá-los na Internet, o ideal é dar a eles nomes descritivos, por exemplo, neste caso, vemos que temos no nome

Flame-3-Instruct: Isso significa que é um modelo que começa com a instrução flame3 8B.
Mesclado: é um modelo de mesclagem.
DPO: o alinhamento com o DPO foi feito.
Q4: é quantizado em 4 bits.

Primeiro, vamos criar a pasta onde criaremos o arquivo de modelo

	
		!mkdir ollama_modelfile

Agora, criamos o arquivo de modelo

	
		!mkdir ollama_modelfile
!touch ollama_modelfile/Modelfile

E agora faremos o download do arquivo 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]

Após o download, começamos a criar o arquivo de modelo

DE

Primeiro, temos que especificar de qual modelo estamos partindo

Podemos começar com um modelo que está no hub ollama, por exemplo, poderíamos fazer

DE llama3

Mas como vamos começar a partir do arquivo que baixamos, nós o especificamos por meio do caminho

DE ./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

PARÂMETRO

Agora temos que definir os valores dos parâmetros do LLM, por exemplo, se quisermos modificar o valor da temperatura, colocamos

PARÂMETRO temperatura 0,5

Os parâmetros que podemos modificar são

Parâmetro	Descrição	Tipo de Valor	Exemplo de Uso
mirostat	Habilitar a amostragem `Mirostat` para controlar a perplexidade. (padrão: 0, 0 = desativado, 1 = Mirostat, 2 = Mirostat 2.0)	int	mirostat 0
mirostat_eta	Influi na rapidez com que o algoritmo responde ao feedback do texto gerado. Uma taxa de aprendizagem mais baixa resultará em ajustes mais lentos, enquanto uma taxa mais alta tornará o algoritmo mais sensível. (Padrão: 0,1)	float	mirostat_eta 0,1
mirostat_tau	Controla o equilíbrio entre a coerência e a diversidade do output. Um valor mais baixo resultará em um texto mais focado e coerente. (Padrão: 5,0)	float	mirostat_tau 5,0
num_ctx	Define o tamanho da janela de contexto usada para gerar o próximo token. (Padrão: 4096)	int	num_ctx 4096
repeat_last_n	Define quanto tempo atrás o modelo deve olhar para evitar repetições. (Padrão: 64, 0 = desativado, -1 = num_ctx)	int	repeat_last_n 64
repeat_penalty	Define a intensidade com que as repetições são penalizadas. Um valor mais alto (por exemplo, 1,5) penalizará as repetições com mais força, enquanto um valor mais baixo (por exemplo, 0,9) será mais indulgente. (Padrão: 1,1)	float	repeat_penalty 1,1
temperature	A temperatura do modelo. Aumentar a temperatura fará com que o modelo responda de forma mais criativa. (Padrão: 0,8)	float	temperature 0,7
seed	Define a semente dos números aleatórios para a geração. Definir isso para um número específico fará com que o modelo gere o mesmo texto para o mesmo prompt. (Padrão: 0)	int	seed 42
stop	Define as sequências de parada a serem usadas. Quando este padrão é encontrado, o LLM para de gerar texto e retorna. Vários padrões de parada podem ser definidos especificando múltiplos parâmetros stop em um arquivo de modelo.	string	stop "Assistente de IA:"
tfs_z	O amostragem livre de caudas é usado para reduzir o impacto dos tokens menos prováveis no output. Um valor mais alto (por exemplo, 2,0) reduzirá mais o impacto, enquanto um valor de 1,0 desativa esta configuração. (padrão: 1)	float	tfs_z 1
num_predict	Número máximo de tokens a prever ao gerar texto. (Padrão: 128, -1 = geração infinita, -2 = preencher contexto)	int	num_predict 42
top_k	Reduz a probabilidade de gerar sem sentido. Um valor mais alto (por exemplo, 100) dará respostas mais diversas, enquanto um valor mais baixo (por exemplo, 10) será mais conservador. (Padrão: 40)	int	top_k 40
top_p	Funciona junto com top-k. Um valor mais alto (por exemplo, 0,95) levará a um texto mais diverso, enquanto um valor mais baixo (por exemplo, 0,5) gerará um texto mais focado e conservador. (Padrão: 0,9)	float	top_p 0,9

Vamos dar uma olhada nos parâmetros do modelo llama3.

	
		!ollama show llama3 --modelfile | grep PARAMETER

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

Podemos ver que ele tem um parâmetro num_keep = 24 que não é mencionado na documentação do ollama e também três tokens de parada

Vamos tornar o modelo mais criativo aumentando a temperatura e vamos manter os tokens de parada, caso contrário, o modelo não deixará de gerar texto.

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

	
		Overwriting ollama_modelfile/Modelfile

SISTEMA

Podemos condicionar o modelo com uma mensagem do sistema

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

	
		Overwriting ollama_modelfile/Modelfile

TEMPLATE

Esse é o modelo que passaremos para o modelo; os tipos de dados que ele pode incluir são

Variável | Descrição | Variável | Descrição | --- | --- | | {{ .System }} | A mensagem do sistema usada para especificar um comportamento personalizado. | | {{ .Prompt }} | A mensagem de prompt do usuário. | | {{ .Response }} | A resposta do modelo. Ao gerar uma resposta, o texto após essa variável é omitido. |

Como visto dessa forma, não é muito intuitivo, vamos ver isso com um exemplo, imaginemos que temos o seguinte modelo

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

Se passarmos o seguinte prompt para o modelo Hello, how are you?, o modelo fará três coisas

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

Se houver uma mensagem do sistema, ela será passada para o modelo da seguinte forma

<|im_start|>mensagem do sistema <|im_end|>.

Como em nosso caso, há uma mensagem do sistema, isso acontecerá com você

<|im_start|>Você é um assistente de IA que quer ajudar, mas não tem muitas habilidades sociais<|im_end|>.
{{ if .Prompt }}<|im_start|>user{{{ .Prompt }}<|im_end|>{{ end }}:

Se houver uma mensagem do usuário, ela será passada para o modelo da seguinte forma

<|im_start|> mensagem do usuário <|im_end|>.

Como no nosso caso há uma mensagem do usuário (o prompt), ela será passada para você

<|im_start|>usuário Olá, como vai? <|im_end|>
<|im_start|>assistant:

Isso sempre acontecerá com o modelo

<|im_start|>assistant

para que o LLM saiba que agora é sua vez de responder

Depois de analisá-la em partes, podemos colocar a mensagem total que será passada para o LLM e ela é

<|im_start|>Você é um assistente de IA que quer ajudar, mas não tem muitas habilidades sociais<|im_end|>.
      <|im_start|>usuário Olá, como vai? <|im_end|>
      <|im_start|>assistente

Vamos ver como é o modelo llama3.

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

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

É muito semelhante ao que vimos, com o modelo llama3, mas, no nosso caso, ele será passado para o LLM.

<|start_header_id|>system<|end_header_id|>Você é um assistente de IA que quer ajudar, mas não tem muitas habilidades sociais<|eot_id|>.
      <|start_header_id|>usuário<|end_header_id|>Olá, como vai você?<|eot_id|>
      <|start_header_id|>assistant<|end_header_id|>{{ .Response }}<|eot_id|>

Como vamos usar um modelo baseado no llama3, usaremos seu modelo

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

	
		Overwriting ollama_modelfile/Modelfile

LICENÇA

Serve para estabelecer a licença do modelo

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

	
		Overwriting ollama_modelfile/Modelfile

MENSAGEM

Podemos lhe passar um histórico de bate-papo

	
		%%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

ADAPTADOR

Um adaptador é uma matriz que é adicionada ao modelo. Ele é usado para evitar o ajuste fino dos LLMs e fazê-lo apenas para essa matriz, de modo que, quando for adicionado ao modelo, o LLM se comporte como desejamos.

Portanto, para fazer isso, o caminho com o arquivo adaptador é passado para o arquivo adaptador.

ADAPTER ./ollama-lora.bin

Criar o modelo

Uma vez que o arquivo de modelo tenha sido escrito, podemos criar o modelo por meio de

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

	
		!ollama create assistanmal -f ollama_modelfile/Modelfile

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

Vemos que o resultado é muito semelhante ao que obtemos quando criamos uma imagem do docker.

Sim, agora listamos os modelos que temos disponíveis

	
		!ollama list

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

Vemos que há o assistente que acabamos de criar. Vamos testá-lo

	
		!ollama run assistanmal

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

API REST

Por padrão, o ollama é executado na porta 11434 do nosso host local e, como ele tem uma API REST implementada, podemos fazer chamadas usando essa API.

Podemos consultar um LLM usando o método POST, passando-lhe um json com, pelo menos, o modelo e o prompt

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

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

Como podemos ver, ele respondeu via stream. Se não quisermos isso e quisermos uma única resposta, teremos que passar "stream": false no 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}

API do Python

Para usar a API do Python, precisamos instalar o pacote Python do ollama

pip install ollama

Depois de instalado, temos os seguintes métodos

ollama.chat: para iniciar um bate-papo com um modelo
ollama.generate: para gerar uma resposta a um prompt.
ollama.list: Lista de modelos disponíveis
ollama.show: Exibe informações sobre um modelo
ollama.create: Cria um modelo
ollama.delete: exclui um modelo
ollama.pull: Download de um modelo
ollama.push: Fazer upload de um modelo
ollama.embeddings: retorna embeddings de um prompt

Vamos experimentar alguns deles

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

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

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

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

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

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

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

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

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

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

Continuar lendo

Fundamentos de RAG

Esqueça o Ctrl+F! 🤯 Com RAG, seus documentos responderão às suas perguntas diretamente. 😎 Tutorial passo a passo com Hugging Face e ChromaDB. Liberte o poder da IA (e impressione seus amigos)! 💪

Conventional commits

😠 Seus commits parecem escritos em uma língua alienígena? 👽 Junte-se ao clube! 😅 Aprenda Conventional Commits em Python e pare de tortura sua equipe com mensagens enigmáticas. git-changelog e commitizen serão seus novos melhores amigos. 🤝

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

Você já conversou com um LLM e ele lhe respondeu algo que parece ter bebido café de máquina a noite toda? 😂 Isso é o que chamamos de alucinação no mundo dos LLMs! Mas não se preocupe, pois não é que seu modelo de linguagem esteja louco (embora às vezes possa parecer isso 🤪). A verdade é que os LLMs podem ser um pouco... criativos quando se trata de gerar texto. Mas graças ao DoLa, um método que usa camadas de contraste para melhorar a viabilidade dos LLMs, podemos evitar que nossos modelos de linguagem se transformem em escritores de ficção científica 😂. Nesta publicação, explicarei como o DoLa funciona e mostrarei um exemplo de código para que você possa entender melhor como tornar seus LLMs mais confiáveis e menos propensos a inventar histórias. Vamos salvar nossos LLMs da loucura e torná-los mais úteis! 🚀

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Subtify

GPU monitor

Alfred

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Memory Calculator

Espaço para calcular a memória necessária para executar um modelo

Formatador de código Black

Seu colega Filipao está escrevendo código que é difícil de ler? Compartilhe com ele este formatador de código que eu mostro neste post! Venha e aprenda a formatar o código para torná-lo mais compreensível. Não vamos resolver os problemas de Filipao, mas pelo menos você não sofrerá ao lê-lo

Declare redes neurais de forma clara

Declare redes neurais de forma clara no Pytorch

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Use isso localmente

Os espaços do Hugging Face nos permitem executar modelos com demos muito simples, mas e se a demo quebrar? Ou se o usuário a deletar? Por isso, criei contêineres docker com alguns espaços interessantes, para poder usá-los localmente, aconteça o que acontecer. Na verdade, se você clicar em qualquer botão de visualização de projeto, ele pode levá-lo a um espaço que não funciona.