what is the equivelant of make -j in cmake?
In CMake, the equivalent of the `make -j` command is the `-j` or `–parallel` flag for the `cmake –build` command. This flag allows you to specify the number of parallel build processes to use.
For example, to build a project in parallel using four processes, you can run the following command:
“`
cmake –build . –parallel 4
“`
This will instruct CMake to build the project using four parallel processes, which can speed up the build process on machines with multiple cores or CPUs.
How many processes can I run with 12 cores
The number of processes you can run on a 12-core processor depends on several factors, such as the specific processor model, the available memory, and the workload of the processes.
In general, a 12-core processor can support running up to 12 processes simultaneously, one on each core. However, this assumes that each process can fully utilize a single core. In practice, the number of processes that can run efficiently on a 12-core processor may be lower, especially if the processes are CPU-intensive and require significant amounts of processing power.
To determine the optimal number of processes to run on a 12-core processor, you may need to experiment with different configurations and adjust the number of processes based on the performance of the system. You may also want to consider factors such as memory usage and I/O throughput when determining the optimal number of processes to run on your system.
I have 24 cores with 12 being e-cores
If you have 24 cores with 12 being e-cores, the number of processes you can run simultaneously will depend on the workload and the nature of the processes.
The e-cores are typically lower-power cores designed to handle lighter workloads such as background tasks, while the other cores (often referred to as the “performance” cores) are optimized for more demanding tasks that require higher processing power.
In general, you can expect to run up to 12 CPU-intensive processes on the performance cores simultaneously. However, if your workload consists of a mix of CPU-intensive and lighter tasks, you may be able to run additional tasks on the e-cores simultaneously.
It’s also worth noting that some operating systems and applications may be optimized to automatically take advantage of the different types of cores in a heterogeneous processor such as yours, while others may require manual configuration to do so.
I have a makefile do I call cmake on it?
If you have a Makefile for your project, it means that the project has already been configured and built using another build system, most likely using `make`. You do not need to call `cmake` on it.
However, if you want to switch to using `cmake` as your build system, you can create a CMakeLists.txt file that describes how your project should be built and use `cmake` to generate a new Makefile based on that description.
To do this, you can create a CMakeLists.txt file in the root directory of your project and specify the project name, source files, and any other configuration options needed to build your project. Once you have created the CMakeLists.txt file, you can run the following commands in your terminal:
“`
mkdir build
cd build
cmake ..
make
“`
This will create a build directory, generate a Makefile based on the instructions in your CMakeLists.txt file, and then build the project using the new Makefile.
Note that you may need to modify your project source files to work with `cmake` if they were originally written to work only with `make`.
how to run wget in windows
`wget` is a command-line utility commonly used to download files from the web. While `wget` is available natively on most Linux distributions, it is not available on Windows by default. However, there are several ways to run `wget` on Windows.
1. Using WSL (Windows Subsystem for Linux): You can install and use `wget` on Windows by installing WSL, which allows you to run a Linux distribution on your Windows machine. Once you have installed WSL, you can open a Linux terminal and install `wget` using your distribution’s package manager. For example, if you are using Ubuntu, you can run the following command to install `wget`:
“`
sudo apt-get install wget
“`
2. Using Cygwin: Cygwin is a Unix-like environment for Windows that provides a collection of Unix tools and libraries. You can install Cygwin and use its package manager to install `wget`. Once you have installed Cygwin, you can open a Cygwin terminal and run the following command to install `wget`:
“`
apt-cyg install wget
“`
3. Using pre-built binaries: There are several websites that offer pre-built `wget` binaries for Windows that you can download and run. For example, you can download the latest version of `wget` for Windows from the following website:
“`
https://eternallybored.org/misc/wget/
“`
Once you have downloaded the binary, you can open a Command Prompt or PowerShell terminal and run the `wget` command followed by the URL of the file you want to download. For example:
“`
wget https://example.com/file.zip
“`
Note that the exact steps to run `wget` on Windows may vary depending on the specific method you choose.
how about rewrite this so I can use it in windows command line: wget https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g-GGML/resolve/main/vicuna-13B-1.1-GPTQ-4bit-128g.GGML.bin
To download the file from the given URL using the Windows Command Prompt, you can use the `curl` utility instead of `wget`. The `curl` utility is a command-line tool for transferring data from or to a server using various protocols including HTTP, HTTPS, FTP, and more. Here’s how you can download the file using `curl`:
“`
curl -o vicuna-13B-1.1-GPTQ-4bit-128g.GGML.bin https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g-GGML/resolve/main/vicuna-13B-1.1-GPTQ-4bit-128g.GGML.bin
“`
This command will download the file and save it to the current directory with the name `vicuna-13B-1.1-GPTQ-4bit-128g.GGML.bin`. You can replace this name with any other desired name.
Note that you need to have `curl` installed on your Windows machine to use this command. If `curl` is not already installed, you can download it from the following website:
“`
https://curl.se/windows/
“`
Alternatively, you can also use the `Invoke-WebRequest` cmdlet in PowerShell to download the file:
“`
Invoke-WebRequest -Uri “https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g-GGML/resolve/main/vicuna-13B-1.1-GPTQ-4bit-128g.GGML.bin” -OutFile “vicuna-13B-1.1-GPTQ-4bit-128g.GGML.bin”
“`
This command will also download the file and save it to the current directory with the name `vicuna-13B-1.1-GPTQ-4bit-128g.GGML.bin`.
bash command?
If you want to use the `wget` command in a Bash terminal on a Windows machine, you can use the Windows Subsystem for Linux (WSL) to run a Linux distribution on your Windows machine. Once you have installed WSL, you can open a Linux terminal and use the `wget` command as you would on a Linux machine. Here’s how you can download the file using `wget` in a Bash terminal:
“`
wget https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g-GGML/resolve/main/vicuna-13B-1.1-GPTQ-4bit-128g.GGML.bin
“`
This command will download the file to the current directory in the Bash terminal.
Note that to use `wget` in a Bash terminal on a Windows machine, you need to have WSL installed and configured. Once you have installed WSL, you can open a Bash terminal by typing `bash` in the Windows Command Prompt or by installing a Bash terminal emulator like Git Bash.
Traceback (most recent call last):
File “E:GPT4ALLAuto-GPT-WebUIscriptsmain.py”, line 89, in print_assistant_thoughts
assistant_thoughts = assistant_reply_json.get(“thoughts”, {})
AttributeError: ‘str’ object has no attribute ‘get’
This error occurs because you are trying to call the `get()` method on a string object instead of a dictionary object.
The `assistant_reply_json` object is expected to be a dictionary object with a key “thoughts” that contains the thoughts generated by the assistant. However, the error message suggests that `assistant_reply_json` is actually a string object.
To fix this error, you should check the code that generates `assistant_reply_json` and ensure that it is properly formatted as a dictionary object. You should also ensure that the JSON string is properly parsed into a dictionary object before calling the `get()` method on it.
Here’s an example of how to properly parse a JSON string into a dictionary object in Python:
“`
import json
# JSON string
json_str = ‘{“thoughts”: {“greeting”: “Hello, how can I help you?”}}’
# Parse JSON string into dictionary object
assistant_reply_json = json.loads(json_str)
# Get thoughts from dictionary object
assistant_thoughts = assistant_reply_json.get(“thoughts”, {})
# Print thoughts
print(assistant_thoughts)
“`
In this example, the `json.loads()` method is used to parse the JSON string into a dictionary object, which can then be used to extract the thoughts generated by the assistant.
Evaluate this and create an file from the instructions How to use
For large OPT models, such as this one, it is not recommend to make use of the text-generation pipeline because one should load the model in half-precision to accelerate generation and optimize memory consumption on GPU. It is recommended to directly call the generate method as follows:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(“facebook/opt-6.7b”, torch_dtype=torch.float16).cuda()
# the fast tokenizer currently does not work correctly
tokenizer = AutoTokenizer.from_pretrained(“facebook/opt-6.7b”, use_fast=False)
prompt = “Hello, I’m am conscious and”
input_ids = tokenizer(prompt, return_tensors=”pt”).input_ids.cuda()
generated_ids = model.generate(input_ids)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
[“Hello, I’m am conscious and aware of my surroundings. I’m not sure what you mean”]
By default, generation is deterministic. In order to use the top-k
To use the top-k and temperature sampling for text generation, modify the `generate()` method call as follows:
“`
generated_ids = model.generate(
input_ids,
do_sample=True,
max_length=50,
top_k=50,
temperature=0.7
)
“`
In this example, the `do_sample` argument is set to `True` to enable sampling, `max_length` sets the maximum length of the generated text, `top_k` sets the number of highest probability words to consider at each step, and `temperature` controls the randomness of the sampling.
To save the above code as a Python script file, you can do the following:
1. Open a text editor (e.g. Notepad, Sublime Text, Visual Studio Code).
2. Copy and paste the code into the text editor.
3. Save the file with a `.py` file extension (e.g. `text_generation.py`).
4. Run the script using a Python interpreter (e.g. `python text_generation.py`).
Note that you will need to have the `transformers` library and its dependencies installed in order to run the script. You can install it using `pip` by running `pip install transformers`.