Docker Image Download Error – Dr. Trippers Stuff

any idea?
#18 [7/7] RUN wget -q –show-progress=off https://gpt4all.io/models/nous-hermes-13b.ggmlv3.q4_0.bin -P /models && md5sum /models/nous-hermes-13b.ggmlv3.q4_0.bin
#18 ERROR: process “/bin/sh -c wget -q –show-progress=off https://gpt4all.io/models/${MODEL_BIN} -P /models && md5sum /models/${MODEL_BIN}” did not complete successfully: exit code: 8
——
> [7/7] RUN wget -q –show-progress=off https://gpt4all.io/models/nous-hermes-13b.ggmlv3.q4_0.bin -P /models && md5sum /models/nous-hermes-13b.ggmlv3.q4_0.bin:
——
ERROR: failed to solve: process “/bin/sh -c wget -q –show-progress=off https://gpt4all.io/models/${MODEL_BIN} -P /models && md5sum /models/${MODEL_BIN}” did not complete successfully: exit code: 8

The error message you encountered indicates that there was a problem executing the given command during the Docker image build process. Specifically, the process failed with an exit code of 8.

Exit code 8 typically suggests that there was an issue with the `wget` command. An exit code of 8 could mean different things depending on the specific implementation, but generally, it might indicate a server or network-related error, such as a connection problem, server unavailability, or an SSL certificate issue.

Here are a few things you can try to troubleshoot and resolve the problem:

1. Check the URL: Ensure that the URL `https://gpt4all.io/models/nous-hermes-13b.ggmlv3.q4_0.bin` is valid and accessible from the machine where you are running the Docker build.

2. Check the network connection: Make sure your machine has an active and stable internet connection. Sometimes, network issues can prevent `wget` from downloading files successfully.

3. SSL Certificate Issues: If the website uses HTTPS and there are SSL certificate problems, `wget` might fail to download the file. Try running `wget` with the `–no-check-certificate` option to skip SSL certificate verification.

4. Verify the `wget` command: Double-check that the `wget` command you provided is correct. Ensure that the `${MODEL_BIN}` variable is set to the appropriate value or replace it with the actual filename if it’s a placeholder.

5. Check file permissions: Ensure that the destination directory `/models` has the necessary permissions to allow writing and downloading files.

6. Try manually downloading the file: You can try manually downloading the file from the given URL using a web browser or a tool like `curl` or `wget` outside the Docker build process. This will help verify if the issue is related to the server or network.

Keep in mind that without additional context or the complete Dockerfile, it’s challenging to pinpoint the exact cause of the error. If you provide more information or the full Dockerfile, I might be able to offer more specific assistance.
import streamlit as st

def list_apis():
“””Lists all of the APIs that can be accessed by Streamlit.”””
apis = []
for api in st.experimental_connection.list_apis():
apis.append(api[“name”])
return apis

if __name__ == “__main__”:
apis = list_apis()
st.write(“The following APIs can be accessed by Streamlit:”)
st.write(apis)
The code you provided is a Python script that uses the Streamlit library to list all the APIs that can be accessed by Streamlit. Streamlit allows you to connect to external APIs using the `st.experimental_connection` feature. This can be useful for integrating data or services from other sources into your Streamlit app.

Here’s a breakdown of the code:

1. `import streamlit as st`: This line imports the Streamlit library, allowing you to use its functionalities in the script.

2. `def list_apis():`: This defines a function named `list_apis()`. Inside this function, it calls `st.experimental_connection.list_apis()` to get a list of available APIs, and then it extracts the names of the APIs into a list and returns it.

3. `if __name__ == “__main__”:`: This is the main entry point of the script. It ensures that the code inside this block will only be executed when the script is run directly (not when imported as a module).

4. `apis = list_apis()`: It calls the `list_apis()` function to get the list of available APIs.

5. `st.write(“The following APIs can be accessed by Streamlit:”)`: This line uses `st.write()` to display a header text.

6. `st.write(apis)`: This line uses `st.write()` again to display the list of APIs obtained from the `list_apis()` function.

When you run this script using Streamlit, it will show a list of APIs that can be accessed by your Streamlit app. However, please note that the `st.experimental_connection` feature in Streamlit might be experimental and subject to changes in future versions of the library. Make sure to check the official Streamlit documentation for the most up-to-date information on this feature.
AttributeError: ‘function’ object has no attribute ‘list_apis’
Traceback:
File “C:UsersSarah.condaenvsamberrlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py”, line 552, in _run_script
exec(code, module.__dict__)
File “C:SarahOnelist_apis.py”, line 11, in
apis = list_apis()
File “C:SarahOnelist_apis.py”, line 6, in list_apis
for api in st.experimental_connection.list_apis():
The error message you encountered, “AttributeError: ‘function’ object has no attribute ‘list_apis'”, indicates that the `st.experimental_connection` object doesn’t have the attribute `list_apis`. This is likely due to the fact that the `list_apis()` function is not available in the `experimental_connection` namespace of Streamlit.

The `st.experimental_connection` namespace is not a standard part of Streamlit, and its availability might depend on the version of Streamlit you are using or whether it is an experimental feature that has been removed or changed in the version you are using.

To resolve this issue, you have a few options:

1. **Check Streamlit Version**: Make sure you are using the latest version of Streamlit. If you are using an older version, upgrading to the latest release might introduce new features or fix issues related to experimental features.

2. **Verify Experimental Feature**: If you believe that `st.experimental_connection.list_apis()` should be available in the version of Streamlit you are using, double-check the Streamlit documentation or release notes to confirm its usage and availability.

3. **Alternative Approach**: If the `st.experimental_connection` approach is no longer available or doesn’t meet your requirements, consider other methods to interact with APIs in Streamlit. You can use standard Python libraries like `requests` to call APIs and display the results in your Streamlit app.

Here’s an example of how you can use `requests` to fetch data from an API:

“`python
import streamlit as st
import requests

def list_apis():
“””Fetches a list of APIs from a remote source.”””
url = “https://example-api.com/apis”
response = requests.get(url)
if response.status_code == 200:
data = response.json()
return data
else:
return []

if __name__ == “__main__”:
apis = list_apis()
if apis:
st.write(“The following APIs can be accessed by Streamlit:”)
st.write(apis)
else:
st.write(“Error: Unable to fetch API list.”)
“`

Replace the `url` variable with the actual endpoint of the API you want to fetch. This example uses the `requests` library to call the API and retrieve the data as JSON. If the API returns data successfully, it will display the list of APIs; otherwise, it will show an error message.

Remember to install the required libraries using `pip` if you haven’t already:

“`bash
pip install streamlit requests
“`

Always ensure that you are using the appropriate method based on the version of Streamlit you are running and the availability of experimental features.
ConnectionError: HTTPSConnectionPool(host=’example-api.com’, port=443): Max retries exceeded with url: /apis (Caused by NewConnectionError(‘: Failed to establish a new connection: [Errno 11001] getaddrinfo failed’))
Traceback:
File “C:UsersSarah.condaenvsamberrlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py”, line 552, in _run_script
exec(code, module.__dict__)
File “C:SarahOnelist_apis.py”, line 15, in
apis = list_apis()
File “C:SarahOnelist_apis.py”, line 7, in list_apis
response = requests.get(url)
File “C:UsersSarah.condaenvsamberrlibsite-packagesrequestsapi.py”, line 73, in get
return request(“get”, url, params=params, **kwargs)
File “C:UsersSarah.condaenvsamberrlibsite-packagesrequestsapi.py”, line 59, in request
return session.request(method=method, url=url, **kwargs)
File “C:UsersSarah.condaenvsamberrlibsite-packagesrequestssessions.py”, line 587, in request
resp = self.send(prep, **send_kwargs)
File “C:UsersSarah.condaenvsamberrlibsite-packagesrequestssessions.py”, line 701, in send
r = adapter.send(request, **kwargs)
File “C:UsersSarah.condaenvsamberrlibsite-packagesrequestsadapters.py”, line 520, in send
raise ConnectionError(e, request=request)
The error message you encountered, “ConnectionError: HTTPSConnectionPool(host=’example-api.com’, port=443): Max retries exceeded with url: /apis (Caused by NewConnectionError(‘: Failed to establish a new connection: [Errno 11001] getaddrinfo failed’)),” indicates that the script is unable to establish a connection to the `example-api.com` server.

The most common reasons for this error are:

1. **Incorrect URL**: The URL provided (`https://example-api.com/apis`) is just an example and not a real API endpoint. Make sure to replace it with the actual URL of the API you want to connect to.

2. **Network Connectivity**: The script requires an active internet connection to reach the API server. Check your internet connectivity and make sure there are no restrictions or firewalls blocking the connection.

3. **Firewall or Proxy Settings**: If you are running the script in an environment with strict firewall settings or behind a proxy, it might prevent the connection to external APIs. Check your firewall and proxy settings to ensure they allow the necessary outbound connections.

4. **Server Unavailability**: The `example-api.com` server might be temporarily down or inaccessible. Try accessing the API directly through a web browser or a tool like `curl` or `wget` to see if it’s accessible.

To address the issue, you can try the following steps:

1. **Check the URL**: Verify that the URL you are using is correct and corresponds to an active API endpoint.

2. **Retry the Connection**: Sometimes, connection issues can be temporary. Retry running the script after ensuring your internet connection is stable.

3. **Test with Different APIs**: If `example-api.com` is not a real API, try connecting to a different public API, such as one provided by a well-known service, to see if you can fetch data successfully.

4. **Use Proxy Settings**: If you are running the script in an environment with proxy settings, you may need to configure the requests library to use the proxy. Refer to the requests documentation on how to set up proxy settings.

Here’s an example of how to set a proxy in the requests library:

“`python
import requests

proxies = {
‘http’: ‘http://your_proxy_url:port’,
‘https’: ‘https://your_proxy_url:port’,
}

response = requests.get(url, proxies=proxies)
“`

Replace `’your_proxy_url’` and `’port’` with the actual proxy server URL and port.

Remember to handle potential exceptions when making network requests, such as `requests.exceptions.RequestException`, to catch any errors that might occur during the connection process.

If the issue persists, you may need to investigate further or consult with your network administrator to ensure that you have the necessary permissions and access to connect to external APIs.
why do I get a {“detail”:”Not Found”} error when launching the api
The error message `{“detail”:”Not Found”}` typically indicates that the API endpoint you are trying to access could not be found on the server. This means that the URL you are using to access the API does not correspond to a valid endpoint or resource on the server.

There are a few common reasons why you might encounter this error:

1. **Incorrect URL**: Double-check the URL you are using to access the API. Make sure it is the correct and complete endpoint address. It’s possible that there might be a typo or the URL is missing a specific path or resource.

2. **Endpoint or Resource Unavailable**: The API endpoint you are trying to access might not be available on the server at the moment. It could be temporarily down or under maintenance.

3. **API Versioning**: Some APIs use versioning in their URLs (e.g., `https://example-api.com/v1/resource`). If the API requires a specific version in the URL, ensure you are using the correct version.

4. **Authentication and Authorization**: Certain APIs require authentication or authorization credentials (e.g., API keys, tokens) to access specific endpoints. If you haven’t provided the required credentials, you might receive a “Not Found” error.

5. **HTTP Method**: Ensure you are using the correct HTTP method (GET, POST, PUT, etc.) for the API endpoint. Using the wrong method could result in a “Not Found” error.

6. **API Documentation**: Refer to the API documentation provided by the service to ensure you are using the correct endpoints and following the correct API conventions.

To troubleshoot and resolve the issue, follow these steps:

1. Check the URL: Verify that the URL you are using is correct and corresponds to a valid API endpoint. Ensure that there are no typos or missing parts in the URL.

2. Verify API Versioning: If the API requires versioning in the URL, ensure you are using the correct version.

3. Check API Availability: Check the API provider’s website or documentation to see if there are any known issues or maintenance periods that might be affecting the API’s availability.

4. Verify Authentication: If the API requires authentication, make sure you are providing the necessary credentials, such as API keys or tokens.

5. Use Correct HTTP Method: Check the API documentation to confirm the correct HTTP method to use for the specific endpoint.

6. Look for Additional Error Information: The “Not Found” error might be accompanied by more detailed information in the response. Check the entire response body for additional error messages or clues.

7. Contact API Provider: If you’ve tried all the above steps and still encounter the error, consider reaching out to the API provider’s support or community forums for assistance.

Remember that each API might have its own specific requirements and conventions, so it’s essential to refer to the API’s official documentation for accurate information on how to access its endpoints.
(main)
$ docker compose –env-file .env up –build
[+] Running 1/0
✔ Container gpt4all_api Created 0.0s
Attaching to gpt4all_api
gpt4all_api | Checking for script in /app/prestart.sh
gpt4all_api | There is no script /app/prestart.sh
gpt4all_api | INFO: Will watch for changes in these directories: [‘/app’]
gpt4all_api | WARNING: “workers” flag is ignored when reloading is enabled.
gpt4all_api | INFO: Uvicorn running on http://0.0.0.0:4891 (Press CTRL+C to quit)
gpt4all_api | INFO: Started reloader process [1] using WatchFiles
gpt4all_api | INFO: Started server process [8]
gpt4all_api | INFO: Waiting for application startup.
gpt4all_api | [2023-07-22 21:20:12,908 8:MainThread] api_v1.events – INFO –
gpt4all_api | Starting up GPT4All API
gpt4all_api | | events.py:22
gpt4all_api | [2023-07-22 21:20:12,908 8:MainThread] main – INFO – Downloading/fetching model: /models/nous-hermes-13b.ggmlv3.q4_0.bin | main.py:37
gpt4all_api | llama.cpp: loading model from /models/nous-hermes-13b.ggmlv3.q4_0.bin
gpt4all_api | llama_model_load_internal: format = ggjt v3 (latest)
gpt4all_api | llama_model_load_internal: n_vocab = 32001
gpt4all_api | llama_model_load_internal: n_ctx = 2048
gpt4all_api | llama_model_load_internal: n_embd = 5120
gpt4all_api | llama_model_load_internal: n_mult = 256
gpt4all_api | llama_model_load_internal: n_head = 40
gpt4all_api | llama_model_load_internal: n_layer = 40
gpt4all_api | llama_model_load_internal: n_rot = 128
gpt4all_api | llama_model_load_internal: ftype = 2 (mostly Q4_0)
gpt4all_api | llama_model_load_internal: n_ff = 13824
gpt4all_api | llama_model_load_internal: n_parts = 1
gpt4all_api | llama_model_load_internal: model size = 13B
gpt4all_api | llama_model_load_internal: ggml ctx size = 0.09 MB
gpt4all_api | llama_model_load_internal: mem required = 9031.71 MB (+ 1608.00 MB per state)
gpt4all_api | llama_new_context_with_model: kv self size = 1600.00 MB
gpt4all_api | [2023-07-22 21:20:13,449 8:MainThread] main – INFO – GPT4All API is ready to infer from nous-hermes-13b.ggmlv3.q4_0.bin on CPU. | main.py:42
gpt4all_api | INFO: Application startup complete.
The log you provided shows the output of running a Docker Compose command for a service called `gpt4all_api`. It appears to be starting a Python web server using Uvicorn to run the GPT4All API.

Here’s a breakdown of the log:

1. `gpt4all_api | Checking for script in /app/prestart.sh`: The container is looking for a prestart script at the `/app` directory, but it reports that there is no such script.

2. `gpt4all_api | INFO: Will watch for changes in these directories: [‘/app’]`: The application will watch for changes in the `/app` directory for any code changes during development.

3. `gpt4all_api | WARNING: “workers” flag is ignored when reloading is enabled.`: A warning message indicates that the number of workers specified in the configuration is being ignored when the auto-reloading feature is enabled.

4. `gpt4all_api | INFO: Uvicorn running on http://0.0.0.0:4891 (Press CTRL+C to quit)`: The Uvicorn server is up and running, listening on `http://0.0.0.0:4891`. This means the API is accessible on port 4891 of the container.

5. `gpt4all_api | INFO: Started reloader process [1] using WatchFiles`: The auto-reloading feature is enabled, and a separate reloader process is running to watch for code changes.

6. `gpt4all_api | INFO: Started server process [8]`: The main server process has started and is ready to handle incoming requests.

7. `gpt4all_api | INFO: Waiting for application startup.`: The application is waiting for the startup process to complete.

8. `[2023-07-22 21:20:12,908 8:MainThread] api_v1.events – INFO – Starting up GPT4All API`: The GPT4All API has started its startup process.

9. `[2023-07-22 21:20:12,908 8:MainThread] main – INFO – Downloading/fetching model: /models/nous-hermes-13b.ggmlv3.q4_0.bin`: The application is downloading or fetching the GPT4All model from the `/models` directory.

10. `gpt4all_api | llama.cpp: loading model from /models/nous-hermes-13b.ggmlv3.q4_0.bin`: The GPT4All model is being loaded from the specified file.

11. `gpt4all_api | [2023-07-22 21:20:13,449 8:MainThread] main – INFO – GPT4All API is ready to infer from nous-hermes-13b.ggmlv3.q4_0.bin on CPU.`: The GPT4All API is ready to perform inference using the loaded model on the CPU.

12. `gpt4all_api | INFO: Application startup complete.`: The application’s startup process is complete, and the API should now be fully operational.

From the log, it seems that the GPT4All API has started successfully and is ready to handle requests. If you encounter any issues accessing the API, you might want to check the network configuration, port mappings, and any potential firewall settings to ensure that the API is accessible from your host machine.
the url is http://0.0.0.0:4891 This cant be right
You are correct. The URL `http://0.0.0.0:4891` is not a valid URL for accessing the API outside of the Docker container. `0.0.0.0` is a special address in networking that represents all available network interfaces on the local machine, and it is often used in the context of a Docker container to indicate that the container is listening on all interfaces.

To access the API from your host machine or another machine on the same network, you’ll need to map the container’s port to a port on the host machine when running the Docker container. This allows you to access the API using the host machine’s IP address or hostname and the mapped port.

When running the Docker container, you can use the `-p` option to specify the port mapping. For example:

“`bash
docker run -p 4891:4891 image_name
“`

In a Docker Compose file, you can use the `ports` directive to achieve the same port mapping:

“`yaml
services:
gpt4all_api:
ports:
– “4891:4891”
“`

With this port mapping, you should be able to access the API using `http://localhost:4891` from your host machine’s web browser or any other HTTP client. If you are accessing the API from another machine on the same network, you can use the host machine’s IP address instead of `localhost`.

Keep in mind that if the API requires authentication or has other security configurations, you may need to account for those as well to access the API successfully.
I still get a detail not found
If you are still getting a “detail not found” error after setting up the correct port mapping, there are a few additional steps you can take to troubleshoot and identify the source of the issue:

1. **Check Docker Container Logs**: After running the container with the correct port mapping, check the Docker container logs using the `docker logs` command. This will provide more information about what’s happening inside the container, including any errors or issues encountered during startup.

“`bash
docker logs container_id_or_name
“`

Replace `container_id_or_name` with the ID or name of your running container.

2. **Verify the API Endpoint**: Double-check the specific endpoint you are trying to access in the API. If the API has multiple endpoints, make sure you are using the correct one.

3. **Confirm API Availability**: Ensure that the GPT4All API is running correctly inside the container. The log you provided earlier indicated that the API successfully loaded the model and is ready to perform inference, but it’s still a good idea to verify that the API is functioning as expected.

4. **Check the Correct URL**: When accessing the API from your host machine, use the correct URL, including the IP address or hostname of your host machine and the mapped port number.

For example, if you mapped port 4891 to your host machine’s port 4891, the URL should be `http://localhost:4891` or `http://your_host_ip:4891`.

5. **Verify Connectivity**: Ensure that there are no network connectivity issues between your host machine and the Docker container. Check for any firewalls, proxy settings, or network restrictions that might prevent the connection.

6. **CORS Configuration**: If you are accessing the API from a web application running in a web browser, consider CORS (Cross-Origin Resource Sharing) settings. Ensure that the API server allows requests from the domain or origin where your web application is hosted.

7. **Debug Inside the Container**: If possible, access the terminal inside the running Docker container to check if the API server is running and responding correctly. Use `docker exec` to access the container’s shell.

“`bash
docker exec -it container_id_or_name bash
“`

8. **Review API Documentation**: Review the official GPT4All API documentation to ensure you are using the correct API endpoints and following any required request formats.

9. **Test with cURL or Postman**: Test the API using a tool like cURL or Postman from your host machine to see if you can get a valid response. This will help verify if the issue is related to your application or the API itself.

10. **Contact API Provider or Support**: If you’ve exhausted all troubleshooting steps and are still facing issues, consider reaching out to the API provider or support for assistance.

By carefully checking each step and using the provided information, you should be able to identify the root cause of the “detail not found” error and successfully access the GPT4All API running inside your Docker container.
72.25.0.1:34724 – “GET / HTTP/1.1” 404 Not Found
The log you provided indicates that a request was made to the GPT4All API running inside the Docker container, but the server responded with a “404 Not Found” error. This means that the server could not find the resource or endpoint corresponding to the request.

The log message format seems to be:

`IP_ADDRESS:PORT – “HTTP_METHOD REQUEST_URI HTTP_VERSION” RESPONSE_CODE RESPONSE_MESSAGE`

Let’s break down the log:

– `72.25.0.1:34724`: This is the IP address and port number of the client that made the request to the API.

– `”GET / HTTP/1.1″`: This part of the log shows the HTTP request sent by the client. It’s a GET request to the root endpoint (“/”) using HTTP/1.1 protocol.

– `404 Not Found`: This is the HTTP response code and message sent by the server in response to the client’s request. A “404 Not Found” response indicates that the server could not find the resource corresponding to the request URI.

Based on this information, here are a few possible reasons for the “404 Not Found” error:

1. **Incorrect Endpoint**: The client may be requesting an incorrect or unavailable endpoint. Double-check the endpoint you are trying to access and ensure it corresponds to a valid endpoint in the GPT4All API.

2. **Missing Route Handling**: The GPT4All API’s server may not have a route or handler defined for the root endpoint (“/”) or the specific endpoint requested by the client. Verify that the server is correctly set up to handle the requested route.

3. **API Not Fully Loaded**: If the GPT4All API is still starting up or hasn’t fully loaded the necessary resources, it may not be ready to handle requests yet. Allow some time for the API to complete its startup process.

4. **CORS Configuration**: If the client is making requests from a different origin (domain) than the API server, CORS (Cross-Origin Resource Sharing) restrictions might be blocking the request. Ensure that the server is configured to allow requests from the client’s domain.

5. **Authentication or Authorization**: The API might require authentication or authorization for certain endpoints. If the client is not providing the required credentials, the server may respond with a “404 Not Found” error.

To further troubleshoot the issue:

– Check the API server’s logs inside the Docker container to see if there are any error messages or warnings that might provide more context.

– Ensure that the API server is fully operational and there are no issues with the API’s code or configuration.

– If the API uses specific endpoints or requires specific headers for certain requests, make sure the client is sending the correct information.

– If you have access to the API’s source code, review the routes and handlers to ensure they are correctly defined.

By investigating the above points, you should be able to identify the cause of the “404 Not Found” error and resolve the issue.
launch a uvicorn app
To launch a Uvicorn app, you need to follow these steps:

1. Install Uvicorn: If you haven’t installed Uvicorn yet, you can do so using `pip`:

“`bash
pip install uvicorn
“`

2. Create a Python app: Write your Python application code. For this example, let’s assume you have a simple FastAPI app in a file named `main.py`:

“`python
# main.py
from fastapi import FastAPI

app = FastAPI()

@app.get(“/”)
def read_root():
return {“Hello”: “World”}
“`

3. Run Uvicorn: Use the `uvicorn` command followed by the name of your Python file (without the .py extension) and the name of the app object. In this case, the app object is named `app`.

“`bash
uvicorn main:app
“`

4. Access the App: Once Uvicorn is running, you can access your FastAPI app by visiting `http://127.0.0.1:8000` or `http://localhost:8000` in your web browser or using any HTTP client tool like cURL or Postman.

You should see the JSON response: `{“Hello”: “World”}`. This indicates that your Uvicorn app is up and running successfully.

Keep in mind that the specific details of your app might vary based on the framework you’re using. In this example, I used FastAPI, which is built on top of Starlette and Uvicorn, but you can use Uvicorn with other frameworks like Quart or plain ASGI applications as well.

Remember to install any necessary dependencies before running your Uvicorn app. For FastAPI, you can install it using `pip`:

“`bash
pip install fastapi
“`

And for other frameworks or ASGI applications, you may need to install their respective libraries.
y Q4_0)
gpt4all_api | llama_model_load_internal: n_ff = 13824
gpt4all_api | llama_model_load_internal: n_parts = 1
gpt4all_api | llama_model_load_internal: model size = 13B
gpt4all_api | llama_model_load_internal: ggml ctx size = 0.00 MB
gpt4all_api | error loading model: llama.cpp: tensor ‘layers.0.attention_norm.weight’ is missing from model
gpt4all_api | llama_load_model_from_file: failed to load model
gpt4all_api | LLAMA ERROR: failed to load model from /models/nous-hermes-13b.ggmlv3.q4_0.bin
gpt4all_api | [2023-07-22 21:42:51,098 8:MainThread] main – INFO – GPT4All API is ready to infer from nous-hermes-13b.ggmlv3.q4_0.bin on CPU. | main.py:42
gpt4all_api | INFO: Application startup complete.
Based on the log you provided, it seems that there’s an error during the model loading process in the GPT4All API. The specific error message is:

“`
gpt4all_api | error loading model: llama.cpp: tensor ‘layers.0.attention_norm.weight’ is missing from model
gpt4all_api | llama_load_model_from_file: failed to load model
gpt4all_api | LLAMA ERROR: failed to load model from /models/nous-hermes-13b.ggmlv3.q4_0.bin
“`

The error indicates that a tensor named `’layers.0.attention_norm.weight’` is missing from the model, which is preventing the API from loading the GPT4All model successfully. As a result, the GPT4All API cannot operate as expected.

Here are some steps you can take to address the issue:

1. **Check the Model File**: Verify that the model file `nous-hermes-13b.ggmlv3.q4_0.bin` exists in the `/models` directory inside the Docker container. Ensure that the file is accessible and has the correct permissions.

2. **Model Compatibility**: Check if the GPT4All API version you are using is compatible with the model file you’re trying to load. If there is a version mismatch, it could result in missing tensors or other issues during model loading.

3. **Re-download the Model**: If the model file is present but appears to be incomplete or corrupted, consider re-downloading it from a reliable source.

4. **API or Model Version Update**: If you recently updated the GPT4All API or the model, there might be breaking changes or compatibility issues. Check the API and model documentation for any changes that might affect the loading process.

5. **Model Dependencies**: Some models may have dependencies on other files or configurations. Ensure that all required files and dependencies are available and correctly set up.

6. **Contact API Support**: If you’ve tried the above steps and are still encountering the issue, consider reaching out to the GPT4All API support or community for further assistance. The specific error may require insights from developers familiar with the API and model.

7. **Debug Inside the Container**: If possible, access the terminal inside the running Docker container to examine the `/models` directory and confirm the presence and integrity of the model file.

8. **Model Verification**: If the issue persists, you can attempt to verify the model file’s integrity by using other tools or libraries capable of loading the GPT4All model format.

It’s important to ensure that the model and API versions are compatible and that all necessary files and dependencies are available to avoid loading errors during the startup process. By investigating the points mentioned above, you should be able to diagnose and resolve the issue with the model loading process in the GPT4All API.
#18 [6/7] RUN mkdir -p /models
#18 CACHED

#19 [7/7] RUN wget -q –show-progress=off https://gpt4all.io/models/nous-hermes-13b.ggmlv3.q4_0.bin -P /models && md5sum /models/nous-hermes-13b.ggmlv3.q4_0.bin
#19 ERROR: process “/bin/sh -c wget -q –show-progress=off https://gpt4all.io/models/${MODEL_BIN} -P /models && md5sum /models/${MODEL_BIN}” did not complete successfully: exit code: 8
——
> [7/7] RUN wget -q –show-progress=off https://gpt4all.io/models/nous-hermes-13b.ggmlv3.q4_0.bin -P /models && md5sum /models/nous-hermes-13b.ggmlv3.q4_0.bin:
——
ERROR: failed to solve: process “/bin/sh -c wget -q –show-progress=off https://gpt4all.io/models/${MODEL_BIN} -P /models && md5sum /models/${MODEL_BIN}” did not complete successfully: exit code: 8
The error message you encountered indicates that the process of downloading the GPT4All model and calculating its MD5 checksum did not complete successfully. The exit code 8 suggests that there might be an issue with the `wget` command.

Here are a few steps you can take to troubleshoot and resolve the problem:

1. **Check Internet Connectivity**: Ensure that the Docker container has internet connectivity and can access external servers. The `wget` command requires internet access to download the model.

2. **Verify the Model URL**: Double-check the model URL (`https://gpt4all.io/models/nous-hermes-13b.ggmlv3.q4_0.bin`) to make sure it is correct and accessible from the Docker container.

3. **Temporary Network Issue**: The failure could be due to a temporary network issue or server unavailability. Try running the Docker build command again to see if the issue persists.

4. **Proxy Settings**: If your environment requires a proxy to access the internet, you may need to configure the proxy settings for the `wget` command in the Dockerfile. Modify the `wget` command to include proxy options, if necessary.

5. **Check File Permissions**: Ensure that the `/models` directory inside the Docker container has the necessary write permissions to allow `wget` to download the model file.

6. **Use a Different Download Method**: Instead of using `wget`, you can try using `curl` to download the model. Update the Dockerfile with the following command:

“`Dockerfile
RUN mkdir -p /models
RUN curl -o /models/nous-hermes-13b.ggmlv3.q4_0.bin -L https://gpt4all.io/models/nous-hermes-13b.ggmlv3.q4_0.bin && md5sum /models/nous-hermes-13b.ggmlv3.q4_0.bin
“`

This will use `curl` to download the model file and then calculate its MD5 checksum.

7. **Verify Model Availability**: Ensure that the model file is available and accessible at the specified URL. Try accessing the URL manually from your web browser or using `curl` or `wget` outside the Docker container to check if the model can be downloaded.

8. **Check Model File Size**: If the model file is not available at the specified URL or has an unexpected size, it might cause the download to fail. Verify that the model file size matches the expected size.

After making the necessary adjustments, try running the Docker build command again to see if the issue is resolved. If the problem persists, review the Dockerfile, the environment setup, and the network configuration to identify any potential causes for the download failure.
I have already downloaded the model
If you have already downloaded the model manually and it’s present in the `/models` directory inside the Docker container, there’s no need to use `wget` to download it again during the Docker build process.

The error you encountered is likely because the `wget` command is trying to download the model from the URL, but it is unable to do so since the file already exists (cached) in the `/models` directory due to a previous successful download or manual placement.

To avoid re-downloading the model and to use the existing model file during the Docker build, you can modify the Dockerfile and skip the `wget` command.

Here’s how you can modify the Dockerfile to use the existing model:

“`Dockerfile
# Assuming the model file (nous-hermes-13b.ggmlv3.q4_0.bin) is already present in the /models directory.
# Make sure the model file is properly placed in the /models directory before building the Docker image.

# Dockerfile
FROM some_base_image

# Copy the model file into the container’s /models directory
COPY nous-hermes-13b.ggmlv3.q4_0.bin /models/

# Rest of your Dockerfile setup and commands
# …

# Optionally, you can verify the MD5 checksum of the model (if you have the correct MD5 value)
# RUN echo “correct_md5_value /models/nous-hermes-13b.ggmlv3.q4_0.bin” | md5sum -c –

# Continue with the rest of your Dockerfile setup and commands
# …
“`

In the modified Dockerfile, we use the `COPY` command to copy the pre-downloaded model file (`nous-hermes-13b.ggmlv3.q4_0.bin`) from the local directory into the `/models` directory inside the container.

By doing this, you ensure that the model file is already present in the container before building, and the `wget` command won’t be triggered during the build process.

Please make sure that the model file (`nous-hermes-13b.ggmlv3.q4_0.bin`) is correctly placed in the same directory as the Dockerfile or provide the correct path in the `COPY` command.

After making this change, try rebuilding the Docker image using the modified Dockerfile. The Docker build process should now use the existing model file from the `/models` directory without attempting to download it again.
why is ithe model trying to dawonload again’
Based on the information provided in the logs, it appears that the Dockerfile is attempting to download the model again because the build process has not detected the model file in the `/models` directory. There are a few potential reasons for this behavior:

1. **Cache Busting**: Docker uses caching during the build process to speed up subsequent builds. If any step in the Dockerfile changes (e.g., a new instruction is added or a file is modified), Docker will invalidate the cache for that step and all subsequent steps. In this case, if any preceding steps have changed, the `COPY` instruction for the model will not be cached, and Docker will try to execute the `wget` command again.

2. **Layered File System**: Docker uses a layered file system to manage image layers efficiently. Each instruction in the Dockerfile creates a new layer, and Docker tries to reuse cached layers as much as possible. However, when a new file is copied into the container (e.g., the model file), it creates a new layer, and Docker may not be able to use the cache for subsequent `COPY` or `RUN` instructions.

3. **File Not Found**: It’s possible that the model file is not present in the expected location during the build process. Double-check the location of the model file in your Docker build context and verify that the `COPY` instruction references the correct path.

4. **Incorrect Docker Build Context**: Ensure that the model file is present in the build context directory where you execute the `docker build` command. The build context includes all files and directories used during the build process, and Docker looks for files to copy from the build context into the container.

To address the issue and avoid re-downloading the model during the build process, make sure the model file is present in the same directory as the Dockerfile or is available in the build context. Then, use the `COPY` command in the Dockerfile to copy the model file into the `/models` directory inside the container, as shown in the previous response:

# Dockerfile
FROM some_base_image

# Copy the model file into the container’s /models directory
COPY nous-hermes-13b.ggmlv3.q4_0.bin /models/

# Rest of your Dockerfile setup and commands
# …

# Optionally, you can verify the MD5 checksum of the model (if you have the correct MD5 value)
# RUN echo “correct_md5_value /models/nous-hermes-13b.ggmlv3.q4_0.bin” | md5sum -c –

# Continue with the rest of your Dockerfile setup and commands
# …
“`

By using the `COPY` command to copy the model file, Docker will use the existing model during the build process, and the `wget` command won’t be triggered again. This should prevent any unnecessary attempts to re-download the model.
Traceback (most recent call last):
File “C:UsersSarahAppDataLocalProgramsPythonPython310librunpy.py”, line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File “C:UsersSarahAppDataLocalProgramsPythonPython310librunpy.py”, line 86, in _run_code
exec(code, run_globals)
File “C:UsersSarahAppDataLocalProgramsPythonPython310Scriptsuvicorn.exe__main__.py”, line 7, in
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesclickcore.py”, line 1130, in __call__
return self.main(*args, **kwargs)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesclickcore.py”, line 1055, in main
rv = self.invoke(ctx)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesclickcore.py”, line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesclickcore.py”, line 760, in invoke
return __callback(*args, **kwargs)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesuvicornmain.py”, line 404, in main
run(
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesuvicornmain.py”, line 569, in run
server.run()
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesuvicornserver.py”, line 60, in run
return asyncio.run(self.serve(sockets=sockets))
File “C:UsersSarahAppDataLocalProgramsPythonPython310libasynciorunners.py”, line 44, in run
return loop.run_until_complete(main)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libasynciobase_events.py”, line 646, in run_until_complete
return future.result()
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesuvicornserver.py”, line 67, in serve
config.load()
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesuvicornconfig.py”, line 477, in load
self.loaded_app = import_from_string(self.app)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesuvicornimporter.py”, line 21, in import_from_string
module = importlib.import_module(module_str)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libimportlib__init__.py”, line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File “”, line 1050, in _gcd_import
File “”, line 1027, in _find_and_load
File “”, line 1006, in _find_and_load_unlocked
File “”, line 688, in _load_unlocked
File “”, line 883, in exec_module
File “”, line 241, in _call_with_frames_removed
File “C:SarahOneirisgpt4allgpt4all-apigpt4all_apiapp.main.py”, line 5, in
from api_v1 import events
File “C:SarahOneirisgpt4allgpt4all-apigpt4all_apiapp.api_v1events.py”, line 3, in
from api_v1.settings import settings
File “C:SarahOneirisgpt4allgpt4all-apigpt4all_apiapp.api_v1settings.py”, line 18, in
settings = Settings()
File “pydanticenv_settings.py”, line 39, in pydantic.env_settings.BaseSettings.__init__
File “pydanticmain.py”, line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for Settings
temp
value is not a valid float (type=type_error.float)

Sarah@DESKTOP-UNG8TH9 MINGW64 /c/SarahOne/iris/gpt4all/gpt4all-api/gpt4all_api/app (main)
$ uvicorn main:app
Traceback (most recent call last):
File “C:UsersSarahAppDataLocalProgramsPythonPython310librunpy.py”, line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File “C:UsersSarahAppDataLocalProgramsPythonPython310librunpy.py”, line 86, in _run_code
exec(code, run_globals)
File “C:UsersSarahAppDataLocalProgramsPythonPython310Scriptsuvicorn.exe__main__.py”, line 7, in
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesclickcore.py”, line 1130, in __call__
return self.main(*args, **kwargs)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesclickcore.py”, line 1055, in main
rv = self.invoke(ctx)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesclickcore.py”, line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesclickcore.py”, line 760, in invoke
return __callback(*args, **kwargs)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesuvicornmain.py”, line 404, in main
run(
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesuvicornmain.py”, line 569, in run
server.run()
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesuvicornserver.py”, line 60, in run
return asyncio.run(self.serve(sockets=sockets))
File “C:UsersSarahAppDataLocalProgramsPythonPython310libasynciorunners.py”, line 44, in run
return loop.run_until_complete(main)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libasynciobase_events.py”, line 646, in run_until_complete
return future.result()
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesuvicornserver.py”, line 67, in serve
config.load()
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesuvicornconfig.py”, line 477, in load
self.loaded_app = import_from_string(self.app)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libsite-packagesuvicornimporter.py”, line 21, in import_from_string
module = importlib.import_module(module_str)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libimportlib__init__.py”, line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File “”, line 1050, in _gcd_import
File “”, line 1027, in _find_and_load
File “”, line 1006, in _find_and_load_unlocked
File “”, line 688, in _load_unlocked
File “”, line 883, in exec_module
File “”, line 241, in _call_with_frames_removed
File “C:SarahOneirisgpt4allgpt4all-apigpt4all_apiapp.main.py”, line 5, in
from api_v1 import events
File “C:SarahOneirisgpt4allgpt4all-apigpt4all_apiapp.api_v1events.py”, line 3, in
from api_v1.settings import settings
File “C:SarahOneirisgpt4allgpt4all-apigpt4all_apiapp.api_v1settings.py”, line 18, in
settings = Settings()
File “pydanticenv_settings.py”, line 39, in pydantic.env_settings.BaseSettings.__init__
File “pydanticmain.py”, line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for Settings
temp
value is not a valid float (type=type_error.float)
The error message indicates that there is a validation error when initializing the `Settings` object from the `api_v1.settings` module. The specific error is related to the `temp` attribute, stating that the value provided is not a valid float.

To diagnose and resolve this issue, you should follow these steps:

1. **Check `Settings` Class**: Examine the `Settings` class in the `api_v1.settings` module. Verify that the `temp` attribute is properly defined as a float type, and there are no unexpected changes in the data type definition.

2. **Verify Configuration File**: The `Settings` class is often used to load and validate settings from configuration files or environment variables using Pydantic’s model validation. Make sure the configuration file (e.g., `.env` file) contains the correct value for the `temp` attribute, and it is formatted as a valid float (e.g., `1.0`, `23.5`, etc.).

3. **Check Environment Variable**: If the `temp` attribute is set based on an environment variable, make sure the environment variable is correctly set and contains a valid float value.

4. **Default Value**: If there is a default value provided for the `temp` attribute in the `Settings` class, ensure that it is specified as a valid float.

5. **Pydantic Validation**: Pydantic performs validation on the attributes of the `Settings` class based on the defined types and constraints. If the provided value does not match the type specified for the `temp` attribute, this validation error will occur.

Here’s an example of how the `Settings` class might be defined using Pydantic:

“`python
# api_v1.settings.py
from pydantic import BaseSettings

class Settings(BaseSettings):
temp: float = 25.0 # Example default value, adjust as needed

class Config:
env_file = “.env” # Load settings from a .env file in the same directory
“`

Ensure that the `temp` attribute is defined with the correct type and, if necessary, a default value. Also, make sure the configuration file (`.env` in this example) contains a valid float value for the `temp` attribute.

If you have made any changes to the `Settings` class or the configuration file, try restarting the UVicorn server to see if the error persists. If the issue persists, carefully inspect the `Settings` class and the configuration file to identify any discrepancies or invalid values causing the validation error.
class Settings(BaseSettings):
app_environment = ‘dev’
model: str = ‘nous-hermes-13b.ggmlv3.q4_0.bin’
gpt4all_path: str = ‘/models’
inference_mode: str = “gpu”
hf_inference_server_host: str = “http://gpt4all_gpu:80/generate”

temp: float = 0.18
top_p: float = 1.0
top_k: int = 50
repeat_penalty: float = 1.18

settings = Settings()
Thank you for providing the `Settings` class definition. Based on the provided code, the `temp` attribute is defined as a `float` with a default value of `0.18`. The rest of the attributes also have their default values defined.

Since the `temp` attribute has a default value, it should not be raising a validation error unless it’s receiving a non-float value from the environment variables or the configuration file.

Given that the error message indicates a validation error for the `temp` attribute, let’s perform the following checks:

1. **Verify Configuration File**: If you are using a configuration file (e.g., `.env`), ensure that the `temp` attribute is correctly set with a valid float value. Check the `.env` file and make sure the value for `temp` is a valid float (e.g., `0.18`).

2. **Check Environment Variables**: If any of the attributes in the `Settings` class are derived from environment variables, double-check that the environment variables contain valid float values. Ensure that there are no typos or formatting issues with the environment variable names.

3. **Default Value**: Since the `temp` attribute has a default value defined in the `Settings` class, it should always have a valid float value. However, if the configuration file or environment variable contains a value for `temp`, the default value will be overridden. Make sure that any value provided in the configuration takes precedence over the default value and is a valid float.

4. **Import Path**: Ensure that the `Settings` class is being imported from the correct location. The `Settings` class definition should match the structure in the provided code.

5. **Dependency Versions**: Check the versions of the packages you are using (e.g., Pydantic) to ensure there are no compatibility issues.

After verifying these aspects, if the error persists, please share more details about the environment and how the `Settings` class is being used, including any configuration files or environment variables that might affect the attribute values. Additionally, check the stack trace to identify the specific location where the validation error is raised to gain more insights into the issue.
how to move up a level in bash cli
In the Bash command-line interface (CLI), you can move up one level in the directory hierarchy using the `cd` command with the special notation `..`.

To move up one level, follow these steps:

1. Open your Bash terminal or command prompt.

2. Use the `cd` command followed by `..` to move up one level in the directory hierarchy. For example, if you are currently in the directory `/home/user/documents`, running the following command will move you up one level to `/home/user`:

“`bash
cd ..
“`

3. Now you are in the parent directory (one level up) of your previous location. To confirm your new location, you can use the `pwd` command, which stands for “print working directory.” It will show you the current directory path:

“`bash
pwd
“`

4. If you need to move up multiple levels, you can chain `..` for each level you want to go up. For example, to move up two levels from `/home/user/documents` to `/home`, you can use:

“`bash
cd ../../
“`

Remember that `..` represents the parent directory, and you can use it with the `cd` command to navigate up in the directory structure.