Build Dockerfile: Step-by-Step – Dr. Trippers Stuff

to build a dockerfile is again
Certainly! To build a Dockerfile, you need to follow these general steps:

1. **Create a Dockerfile:**
– Open a text editor and create a file named `Dockerfile` (no file extension).
– This file will contain instructions for building a Docker image.

2. **Choose a Base Image:**
– Specify the base image you want to use. For example, `ubuntu:20.04` or `node:14`.

“`Dockerfile
FROM ubuntu:20.04
“`

3. **Update and Install Dependencies:**
– Use the `RUN` instruction to run commands to update and install dependencies.

“`Dockerfile
RUN apt-get update && apt-get install -y
package1
package2
“`

4. **Copy Application Files:**
– Use the `COPY` instruction to copy your application files into the image.

“`Dockerfile
COPY . /app
“`

5. **Set Working Directory:**
– Use the `WORKDIR` instruction to set the working directory for subsequent commands.

“`Dockerfile
WORKDIR /app
“`

6. **Expose Ports:**
– Use the `EXPOSE` instruction to specify which ports the application will listen on.

“`Dockerfile
EXPOSE 80
“`

7. **Define Environment Variables:**
– Use the `ENV` instruction to set environment variables if needed.

“`Dockerfile
ENV NODE_ENV=production
“`

8. **Run Application:**
– Use the `CMD` or `ENTRYPOINT` instruction to define the default command to run when the container starts.

“`Dockerfile
CMD [“npm”, “start”]
“`

9. **Build the Docker Image:**
– Open a terminal in the directory containing the Dockerfile and run the following command:

“`bash
docker build -t your_image_name:tag .
“`

– Replace `your_image_name` and `tag` with your desired image name and tag.

10. **Run a Docker Container:**
– Once the image is built, you can run a container using the following command:

“`bash
docker run -p 8080:80 your_image_name:tag
“`

– This example maps port 8080 on your host machine to port 80 in the container.

That’s a concise guide to building a Dockerfile. Let me know if you need more details on any specific step!

(rocm) gemini@DESKTOP-UNG8TH9:/mnt/d/SarahOne/gorilla/gorilla-falcon-7b-hf-v0$ docker build -t gorilla
ERROR: “docker buildx build” requires exactly 1 argument.
See ‘docker buildx build –help’.

Usage: docker buildx build [OPTIONS] PATH | URL | –

Start a build
It seems like you’re encountering an error in the `docker build` command. The syntax for the `docker build` command is different from what you’ve provided. You need to specify the build context, which is typically the current directory (denoted by `.`).

Here’s the corrected command:

“`bash
docker build -t gorilla .
“`

This assumes that your Dockerfile is in the current directory. If your Dockerfile is in a different directory, you should provide the path to that directory instead of `.`.

For example, if your Dockerfile is in a folder called `dockerfiles`, the command would be:

“`bash
docker build -t gorilla ./dockerfiles
“`

Make sure to run this command in the same directory where your Dockerfile is located. If you’re still facing issues, please provide more details about your project structure and the content of your Dockerfile.
docker build -t gorilla .
[+] Building 7.7s (9/9) FINISHED docker:default
=> [internal] load .dockerignore 0.0s
=> => transferring context: 2B 0.0s
=> [internal] load build definition from Dockerfile 0.1s
=> => transferring dockerfile: 292B 0.0s
=> [internal] load metadata for docker.io/rocm/pytorch:latest 1.7s
=> [auth] rocm/pytorch:pull token for registry-1.docker.io 0.0s
=> [1/4] FROM docker.io/rocm/pytorch:latest@sha256:56ea92985c9445fe524fcb2a6b657994599a5b81b69ed36559c3eb2724cee864 0.1s
=> => resolve docker.io/rocm/pytorch:latest@sha256:56ea92985c9445fe524fcb2a6b657994599a5b81b69ed36559c3eb2724cee864 0.1s
=> [internal] load build context 0.1s
=> => transferring context: 2B 0.0s
=> CACHED [2/4] RUN apt-get update && apt-get install -y openssl 0.0s
=> [3/4] WORKDIR /app 5.7s
=> ERROR [4/4] COPY src/ /app 0.0s
——
> [4/4] COPY src/ /app:
——
Dockerfile:9
——————–
7 |
8 | # Copy the contents of the ‘src’ directory into the image
9 | >>> COPY src/ /app
10 |
11 | CMD [“python”, “main.py”]
——————–
ERROR: failed to solve: failed to compute cache key: failed to calculate checksum of ref 96c7686f-146a-4239-9e17-d2573e582ae0::p7qoxlh5yk84qf77tilxraq2y: “/src”: not found
It looks like there’s an issue with the `COPY` instruction in your Dockerfile. The error is indicating that the source directory `src/` is not found.

Here are a couple of things you can check and fix:

1. **Ensure the Source Directory Exists:**
– Make sure that the `src/` directory is present in the same directory as your Dockerfile.

2. **Correct the Source Path:**
– If the `src/` directory is in a different location, you need to adjust the path accordingly.

“`Dockerfile
COPY path/to/src/ /app
“`

Replace `path/to/src/` with the correct path relative to the location of your Dockerfile.

3. **Verify Filesystem Case Sensitivity:**
– Be aware of the case sensitivity of the filesystem. If your actual directory is named `Src/` or `SRC/`, make sure the case matches in the `COPY` instruction.

Once you’ve made the necessary adjustments, try running the `docker build` command again. If the issue persists or you need further assistance, feel free to provide more details about your project structure or share the relevant parts of your Dockerfile.
the /src is the directory I am running the command in
Got it. If you are running the `docker build` command from the `/src` directory, you don’t need to include the leading `/` in the source path because the build context is the current directory. Try modifying your `COPY` instruction like this:

“`Dockerfile
COPY src/ /app
“`

This assumes that the `src/` directory is in the same location as your Dockerfile. If the `src/` directory is in a different location, you need to adjust the path accordingly.

If you’re still encountering issues, please double-check the structure of your project and Dockerfile, and ensure that the `src/` directory is present in the same directory where you’re running the `docker build` command. If the problem persists, feel free to share more details or the relevant parts of your Dockerfile for further assistance.
THAT IS THE ERROR
My apologies for any confusion. It seems like the `src/` directory might not be present in the correct location during the build process. Let’s do a couple of checks:

1. **Directory Structure:**
– Confirm that your project structure looks something like this:

“`
/src
|- (contents of your source code)
Dockerfile
“`

2. **Check Current Directory:**
– Before running the `docker build` command, make sure that your terminal is in the correct directory (where the Dockerfile and `src/` directory are located). You can use the `cd` command to navigate to the correct directory.

3. **Try Absolute Path:**
– As a troubleshooting step, you can try using an absolute path to the `src/` directory:

“`Dockerfile
COPY /src /app
“`

This assumes that the `src/` directory is in the root of your filesystem. Adjust the path accordingly if it’s in a different location.

After making these checks, run the `docker build` command again. If the issue persists, there might be some other environmental factors at play. In that case, sharing the full Dockerfile and any relevant project structure details would be helpful for further troubleshooting.
this is where I am at: /mnt/d/SarahOne/gorilla/gorilla-falcon-7b-hf-v0$
Thank you for providing the location. Since you’re in the directory `/mnt/d/SarahOne/gorilla/gorilla-falcon-7b-hf-v0/`, and your Dockerfile is located there, you should use the following `COPY` instruction:

“`Dockerfile
COPY src/ /app
“`

This assumes that the `src/` directory is also in the same location as your Dockerfile. The relative path is correct based on your current working directory.

If you are still encountering issues, please double-check the contents of the `src/` directory and ensure it contains the files you want to copy into the Docker image. Additionally, you can share the relevant portion of your Dockerfile for a closer look if the problem persists.
THAT IS THE ERROR
I appreciate your patience. It seems like there might be an issue with the naming or case sensitivity. Let’s try a couple more troubleshooting steps:

1. **Check Case Sensitivity:**
– Confirm the case of the directory name. Linux file systems are case-sensitive. Ensure that the directory is named exactly `src/` (lowercase).

2. **Explicit Path:**
– Try using an explicit path to the current directory:

“`Dockerfile
COPY ./ /app
“`

3. **Verbose Output:**
– Run the `docker build` command with the `-v` (verbose) option to get more information about the build process. This might help identify any issues.

“`bash
docker build -t gorilla . -v
“`

Please run through these steps, and if the issue persists, provide the output of the verbose build command and the content of your Dockerfile. This will help me understand the problem better and provide more targeted assistance.
yes ./ worked
Great to hear that using `./` worked! The `./` specifies the current directory as the source for the `COPY` instruction. It’s good practice to use `./` to make the path explicit, especially when there might be variations in file system behavior.

If you have any more steps in your Dockerfile or if there’s anything else you need assistance with, feel free to let me know. I’m here to help!
ERROR: pip’s dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
diffusions 0.1.13 requires torch=1.12.0, but you have torch 2.0.1 which is incompatible.
It looks like you have a dependency conflict related to the version of PyTorch (torch) in your Python environment. The error is indicating that the `diffusions` package requires a version of PyTorch less than 2.0.0, while you have version 2.0.1 installed.

To resolve this issue, you can consider one of the following approaches:

### 1. Downgrade PyTorch:

Since `diffusions` requires a version of PyTorch less than 2.0.0, you can downgrade your PyTorch version to meet this requirement. You can do this using the following pip command:

“`bash
pip install torch==1.12.0
“`

This will install PyTorch version 1.12.0.

### 2. Update `diffusions`:

Check if there is a newer version of the `diffusions` package that is compatible with PyTorch 2.0.0. You can upgrade the package using:

“`bash
pip install –upgrade diffusions
“`

### 3. Pin Versions in `requirements.txt`:

If you have a `requirements.txt` file specifying your dependencies, you can pin the versions of the packages to ensure compatibility. For example:

“`plaintext
torch==1.12.0
diffusions==0.1.13
“`

Then run:

“`bash
pip install -r requirements.txt
“`

Choose the approach that best fits your project requirements and constraints. After making the necessary changes, try running your application or building your Docker image again. If you encounter any further issues or have additional questions, feel free to ask!
can you extract a step by step here? I am confused. Do I need the weights? Am I trying touse the llm wrong? Design a feature for a social media website to recommend articles to users based on how similar the articles are to their previously liked articles
Certainly! Let’s break down the steps to address the PyTorch version conflict and then discuss the feature design for recommending articles on a social media website.

### Resolving PyTorch Version Conflict:

#### Step 1: Install PyTorch 1.12.0
“`bash
pip install torch==1.12.0
“`

This command installs PyTorch version 1.12.0, which is compatible with the version required by the `diffusions` package.

#### Step 2: Update or Install the ‘diffusions’ Package
“`bash
pip install –upgrade diffusions
“`

This command upgrades the `diffusions` package to the latest version, ensuring compatibility with PyTorch 1.12.0.

#### Step 3: Check Dependencies
Review other dependencies in your project and make sure they are compatible with PyTorch 1.12.0. Adjust versions if needed.

#### Feature Design for Article Recommendations:

Now, let’s discuss the steps for designing a feature to recommend articles to users based on their previously liked articles.

#### Step 1: User Profile
1.1 **User Liked Articles:**
– Track the articles that a user has liked or interacted with.

#### Step 2: Article Representation
2.1 **Embedding or Feature Extraction:**
– Use techniques like Word Embeddings, TF-IDF, or pre-trained language models (LLMs) to represent articles as feature vectors.

#### Step 3: Similarity Calculation
3.1 **Cosine Similarity:**
– Calculate the cosine similarity between the feature vectors of previously liked articles and other articles in the database.

#### Step 4: Recommendation Algorithm
4.1 **Collaborative Filtering:**
– Implement a collaborative filtering algorithm to recommend articles based on similarity scores.

#### Step 5: User Feedback
5.1 **Feedback Loop:**
– Allow users to provide feedback on recommended articles to continuously improve the recommendation system.

#### Example Code Skeleton (Python):
“`python
# Step 1: User Profile
user_liked_articles = [article1, article2, …]

# Step 2: Article Representation
# Use a pre-trained language model (LLM) or other feature extraction techniques

# Step 3: Similarity Calculation
from sklearn.metrics.pairwise import cosine_similarity

# Example feature vectors
liked_article_features = get_features(user_liked_articles)
all_article_features = get_features(all_articles)

# Calculate cosine similarity
similarity_scores = cosine_similarity([liked_article_features], all_article_features)[0]

# Step 4: Recommendation Algorithm
# Implement collaborative filtering to recommend top N articles based on similarity scores

# Step 5: User Feedback
# Collect and incorporate user feedback to improve recommendations
“`

Adjust the code according to your specific requirements and the technology stack you are using.

I hope this helps! If you have any more questions or need further clarification on any step, feel free to ask.
what is this doing?
Certainly! Let me break down the example code:

“`python
# Step 1: User Profile
user_liked_articles = [article1, article2, …]
“`
– This represents a user’s profile by keeping track of articles that the user has liked or interacted with.

“`python
# Step 2: Article Representation
# Use a pre-trained language model (LLM) or other feature extraction techniques
“`
– This step is about representing articles as feature vectors. You can use techniques like pre-trained language models (LLMs), Word Embeddings, or TF-IDF to convert articles into numerical representations.

“`python
# Step 3: Similarity Calculation
from sklearn.metrics.pairwise import cosine_similarity

# Example feature vectors
liked_article_features = get_features(user_liked_articles)
all_article_features = get_features(all_articles)

# Calculate cosine similarity
similarity_scores = cosine_similarity([liked_article_features], all_article_features)[0]
“`
– Here, we use cosine similarity to measure how similar the feature vectors of the user’s liked articles are to all other articles. This helps identify articles that are most similar to the ones the user has liked.

“`python
# Step 4: Recommendation Algorithm
# Implement collaborative filtering to recommend top N articles based on similarity scores
“`
– Collaborative filtering is a recommendation algorithm that recommends items based on the preferences of other users with similar tastes. In this context, it suggests articles that users with similar liked articles have also liked.

“`python
# Step 5: User Feedback
# Collect and incorporate user feedback to improve recommendations
“`
– This step involves allowing users to provide feedback on recommended articles. The feedback can be used to continuously improve the recommendation system.

The provided code is a skeleton, and you’ll need to fill in the details based on your specific use case, such as the implementation of feature extraction, the recommendation algorithm, and how you collect and handle user feedback.

If you have any specific questions about a part of the code or if you’d like more details on a particular step, feel free to ask!
here is the directory structure see what you can gleen from this: gorilla
├── data
│ ├── api (TF/HF/TH APIs used in generating apibench)
│ │ ├── {api_name}_api.jsonl
│ ├── apibench (Evaluating LLM models) v-1.0
│ │ ├── {api_name}_train.jsonl, {api_name}_eval.jsonl
| |── apizoo (Contributed by the community – evolving)
│ | ├── username1.json
│ │ ├── username2.json
│ │ ├── …
├── eval
│ ├── README.md
│ ├── get_llm_responses.py
│ ├── eval-scripts
│ │ ├── ast_eval_{api_name}.py
│ ├── eval-data
│ │ ├── questions
│ │ │ ├── API name
│ │ │ │ ├── questions_{api_name}_{eval_metric}.jsonl
│ │ ├── responses
│ │ │ ├── API name
│ │ │ │ ├── responses_{api_name}_Gorilla_FT_{eval_metric}.jsonl
│ │ │ │ ├── responses_{api_name}_Gorilla_RT_{eval_metric}.jsonl
├── inference
│ ├── README.md
│ ├── serve
│ │ ├── gorilla_cli.py
│ │ ├── conv_template.py
├── train (Coming Soon!)

Thank you for sharing the directory structure. It looks like you have a project related to natural language processing (NLP) or language model evaluation, involving different datasets, APIs, and evaluation scripts.

Here’s what I can gather from the structure:

1. **`data` Directory:**
– Contains datasets and APIs used in generating apibench.
– `api`: API data files for different APIs.
– `apibench`: Data related to evaluating Language Model Models (LLMs).
– `apizoo`: Community-contributed data.

2. **`eval` Directory:**
– Contains evaluation-related files and scripts.
– `README.md`: Documentation for the evaluation process.
– `get_llm_responses.py`: Script for obtaining responses from LLMs.
– `eval-scripts`: Evaluation scripts for different APIs.
– `eval-data`: Evaluation data.
– `questions`: Questions for evaluation.
– `responses`: Responses from models for evaluation.

3. **`inference` Directory:**
– Contains files related to model inference.
– `README.md`: Documentation for inference.
– `serve`: Files related to serving models.
– `gorilla_cli.py`: Command-line interface for Gorilla.
– `conv_template.py`: Template for conversation handling.

4. **`train` Directory:**
– Placeholder for training-related files (coming soon).

### Recommendations:
– **Documentation:** Make sure to provide comprehensive documentation for each component of your project, explaining the purpose and usage of different directories and files.

– **Modularization:** Consider breaking down your code into modular components, making it easier to maintain and understand.

– **Version Control:** If not already done, consider using version control (e.g., Git) to track changes and collaborate with others.

– **Testing:** Implement tests for critical functionalities to ensure robustness.

If you have specific questions, need help with a particular aspect, or want more detailed recommendations, feel free to ask!
Inference: Run Gorilla locally inference/README.md

Evaluation: We have included prompts and responses for the APIBench with and without retrievers along with the Abstract Syntax Tree (AST) matching evaluation script at evaluation.

Repository Organization
Our repository organization is shown below.

The data folder contains all the evaluation APIs (APIBench) and the community contributed APIs.
The eval folder contains all our evaluation code as well as the Gorilla outputs.
The inference folder contains all the inference code for running Gorilla locally.
[Coming Soon!] The train folder contains all the training code associated with Gorilla finetuning.
For our dataset collections, all the 1640 API documentation is in data/api. We also include the APIBench dataset created by self-instruct in data/apibench. For evaluation, we convert this into a LLM-friendly chat format, and the questions are in eval/eval-data/questions, and the corresponding responses are in eval/eval-data/responses. We have also included the evaluation scripts are in eval/eval-scripts. This would be entirely sufficient to train Gorilla yourself, and reproduce our results. Please see evaluation for the details on how to use our evaluation pipeline.

Additionally, we have released all the model weights. gorilla-7b-hf-v0 lets you invoke over 925 Hugging Face APIs. Similarly, gorilla-7b-tf-v0 and gorilla-7b-th-v0 have 626 (exhaustive) Tensorflow v2, and 94 (exhaustive) Torch Hub APIs. gorilla-mpt-7b-hf-v0 and gorilla-falcon-7b-hf-v0 are Apache 2.0 licensed models (commercially usable) fine-tuned on MPT-7B and Falcon-7B respectively. We will release a model with all three combined with generic chat capability and community contributed APIs as soon as we can scale our serving infrastructure. You can run Gorilla locally from instructions in the inference/ sub-directory, or we also provide a hosted Gorilla chat completion API (see Colab)!
Got it! It looks like you have a well-organized repository for your project “Gorilla.” Here’s a summary of the key points from the provided information:

### Key Components:
1. **`Inference` Directory:**
– Contains code for running Gorilla locally.
– Refer to the `inference/README.md` file for instructions on running Gorilla locally.

2. **`Evaluation` Directory:**
– Houses evaluation code and Gorilla outputs.
– Evaluation data includes prompts and responses for APIBench, with and without retrievers.
– Abstract Syntax Tree (AST) matching evaluation script is included.

3. **`Train` Directory (Coming Soon):**
– Placeholder for training code associated with Gorilla finetuning.

4. **Datasets:**
– `data` folder contains evaluation APIs (APIBench) and community-contributed APIs.
– API documentation for all 1640 APIs is in `data/api`.
– APIBench dataset created by self-instruct is in `data/apibench`.
– For evaluation, questions are in `eval/eval-data/questions`, and responses are in `eval/eval-data/responses`.
– Evaluation scripts are in `eval/eval-scripts`.

5. **Model Weights:**
– Released model weights for different versions:
– `gorilla-7b-hf-v0`: Over 925 Hugging Face APIs.
– `gorilla-7b-tf-v0`: 626 exhaustive TensorFlow v2 APIs.
– `gorilla-7b-th-v0`: 94 exhaustive Torch Hub APIs.
– `gorilla-mpt-7b-hf-v0`: Fine-tuned on MPT-7B.
– `gorilla-falcon-7b-hf-v0`: Fine-tuned on Falcon-7B.
– Future release planned for a model combining all three with generic chat capability and community-contributed APIs.

6. **Documentation:**
– Comprehensive documentation for using the evaluation pipeline and running Gorilla locally.

7. **API Hosting:**
– Provides a hosted Gorilla chat completion API (refer to Colab).

### Recommendations:
– Make sure the README files in `inference/` and other relevant directories provide clear and concise instructions for users to run Gorilla locally.
– Continue updating documentation as needed, especially when the training code becomes available.
– Consider providing examples or tutorials for users to get started with your models and APIs.
– If applicable, consider using version control to track changes and releases.

If you have specific questions or need further assistance with any aspect of your project, feel free to let me know!
I need this step-by-step
I need this step-by-step:
Inference: Run Gorilla locally inference/README.md

Evaluation: We have included prompts and responses for the APIBench with and without retrievers along with the Abstract Syntax Tree (AST) matching evaluation script at evaluation.

Repository Organization
Our repository organization is shown below.

Additionally, we have released all the model weights. gorilla-7b-hf-v0 lets you invoke over 925 Hugging Face APIs. Similarly, gorilla-7b-tf-v0 and gorilla-7b-th-v0 have 626 (exhaustive) Tensorflow v2, and 94 (exhaustive) Torch Hub APIs. gorilla-mpt-7b-hf-v0 and gorilla-falcon-7b-hf-v0 are Apache 2.0 licensed models (commercially usable) fine-tuned on MPT-7B and Falcon-7B respectively. We will release a model with all three combined with generic chat capability and community contributed APIs as soon as we can scale our serving infrastructure. You can run Gorilla locally from instructions in the inference/ sub-directory, or we also provide a hosted Gorilla chat completion API (see Colab)!
Absolutely, let’s break down the steps to run
Certainly! Let’s break down the steps for running Gorilla locally based on the provided information:

### Step-by-Step: Running Gorilla Locally

**1. Navigate to the Inference Directory:**
– Go to the `inference/` sub-directory in your Gorilla repository.

**2. Read the README.md:**
– Open the `README.md` file in the `inference/` directory.
– This file should contain detailed instructions on how to set up and run Gorilla locally.

**3. Follow the Instructions:**
– Follow the step-by-step instructions provided in the `README.md` file.
– This may include setting up dependencies, configuring the environment, and running the Gorilla inference code.

**4. Evaluate Local Output (Optional):**
– After running Gorilla locally, you may want to evaluate the local output to ensure it’s working as expected.
– Check for any generated results or logs that indicate successful inference.

### Step-by-Step: Evaluating APIBench with Retrievers and AST Matching

**1. Navigate to the Evaluation Directory:**
– Go to the `eval/` sub-directory in your Gorilla repository.

**2. Evaluate APIBench:**
– Look for prompts and responses related to APIBench in the `eval-data` directory.
– There may be separate datasets for APIBench with and without retrievers.

**3. AST Matching Evaluation Script:**
– Locate the AST matching evaluation script in the `eval-scripts` directory.
– Follow any provided instructions on how to use the script.

**4. Dataset Conversion (if needed):**
– If you plan to use your own datasets, check the provided information on converting datasets into a Language Model (LM)-friendly chat format.
– Ensure that the questions are in `eval/eval-data/questions`, and responses are in `eval/eval-data/responses`.

**5. Train Gorilla (Coming Soon):**
– If you intend to train Gorilla yourself, keep an eye on the `train/` directory. Training code will be available there in the future.

### Additional Information:

– **Dataset Collections:**
– API documentation for all 1640 APIs is in `data/api`.
– The APIBench dataset created by self-instruct is in `data/apibench`.

– **Model Weights:**
– Various model weights have been released, including `gorilla-7b-hf-v0`, `gorilla-7b-tf-v0`, `gorilla-7b-th-v0`, `gorilla-mpt-7b-hf-v0`, and `gorilla-falcon-7b-hf-v0`.
– Each model version has specific capabilities and is fine-tuned for different purposes.

– **Future Model Release:**
– A model combining multiple versions with generic chat capability and community-contributed APIs will be released in the future.

– **API Hosting:**
– Gorilla can be run locally using instructions in the `inference/` sub-directory.
– There is also a hosted Gorilla chat completion API mentioned (see Colab).

Follow these steps, and refer to the relevant directories and files for more detailed instructions. If you have specific questions or encounter any issues during the process, feel free to ask for assistance!
A streamlit app main.py out of this pleaase: # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained(“gorilla-llm/gorilla-7b-hf-delta-v1”)
model = AutoModelForCausalLM.from_pretrained(“gorilla-llm/gorilla-7b-hf-delta-v1”)
Certainly! Here’s a simple example of how you can create a Streamlit app (`main.py`) to use the loaded language model for chat completion:

“`python
import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM

def main():
st.title(“Gorilla Chat Completion App”)

# Load model directly
tokenizer = AutoTokenizer.from_pretrained(“gorilla-llm/gorilla-7b-hf-delta-v1”)
model = AutoModelForCausalLM.from_pretrained(“gorilla-llm/gorilla-7b-hf-delta-v1”)

# Streamlit input box for user input
user_input = st.text_input(“Enter your message:”)

if user_input:
# Tokenize the input and generate a response
input_ids = tokenizer.encode(user_input, return_tensors=”pt”)
output_ids = model.generate(input_ids)

# Decode the generated response
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)

# Display the response
st.text(“Gorilla’s Response:”)
st.write(response)

if __name__ == “__main__”:
main()
“`

Save this code in a file named `main.py`. To run the Streamlit app, execute the following command in your terminal:

“`bash
streamlit run main.py
“`

This will start a local development server, and you can view the app in your web browser at the provided URL (usually http://localhost:8501).

This is a basic example, and you can further enhance it by adding more features or styling according to your preferences. If you have specific requirements or if there’s anything specific you’d like to add to the app, feel free to let me know!
Traceback (most recent call last):
File “c:ProgramDataminiconda3envsgorillalibsite-packagesstreamlitruntimescriptrunnerscript_runner.py”, line 541, in _run_script
exec(code, module.__dict__)
File “D:SarahOnegorillagorilla-7b-hf-delta-v1main.py”, line 27, in
main()
File “D:SarahOnegorillagorilla-7b-hf-delta-v1main.py”, line 8, in main
tokenizer = AutoTokenizer.from_pretrained(“gorilla-llm/gorilla-7b-hf-delta-v1”)
File “c:ProgramDataminiconda3envsgorillalibsite-packagestransformersmodelsautotokenization_auto.py”, line 702, in from_pretrained
return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
File “c:ProgramDataminiconda3envsgorillalibsite-packagestransformerstokenization_utils_base.py”, line 1811, in from_pretrained
return cls._from_pretrained(
File “c:ProgramDataminiconda3envsgorillalibsite-packagestransformerstokenization_utils_base.py”, line 1965, in _from_pretrained
tokenizer = cls(*init_inputs, **init_kwargs)
File “c:ProgramDataminiconda3envsgorillalibsite-packagestransformersmodelsllamatokenization_llama_fast.py”, line 89, in __init__
super().__init__(
File “c:ProgramDataminiconda3envsgorillalibsite-packagestransformerstokenization_utils_fast.py”, line 114, in __init__
fast_tokenizer = convert_slow_tokenizer(slow_tokenizer)
File “c:ProgramDataminiconda3envsgorillalibsite-packagestransformersconvert_slow_tokenizer.py”, line 1288, in convert_slow_tokenizer
return converter_class(transformer_tokenizer).converted()
File “c:ProgramDataminiconda3envsgorillalibsite-packagestransformersconvert_slow_tokenizer.py”, line 445, in __init__
from .utils import sentencepiece_model_pb2 as model_pb2
File “c:ProgramDataminiconda3envsgorillalibsite-packagestransformersutilssentencepiece_model_pb2.py”, line 91, in
_descriptor.EnumValueDescriptor(
File “c:ProgramDataminiconda3envsgorillalibsite-packagesgoogleprotobufdescriptor.py”, line 796, in __new__
_message.Message._CheckCalledFromGeneratedFile()
TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
1. Downgrade the protobuf package to 3.20.x or lower.
2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).

It looks like you’re encountering an issue related to the version of the protobuf package and the generated `_pb2.py` files. The error suggests that the generated code is out of date and must be regenerated with `protoc` version 3.19.0 or higher.

Here are a couple of steps you can try to resolve this issue:

### Option 1: Upgrade Protobuf

1. **Upgrade Protobuf:**
Upgrade the `protobuf` package to the latest version using the following command:

“`bash
pip install –upgrade protobuf
“`

2. **Re-run Your Streamlit App:**
After upgrading the `protobuf` package, try running your Streamlit app again.

### Option 2: Regenerate Protobuf Code

1. **Regenerate Protobuf Code:**
If upgrading the `protobuf` package doesn’t resolve the issue, you might need to regenerate the protobuf code. If the code was generated by `protoc`, ensure that you have a version of `protoc` installed that is 3.19.0 or higher.

2. **Regenerate Code:**
Navigate to the directory where the problematic `_pb2.py` files are located and regenerate them. The exact command might vary depending on your project structure, but it typically looks like this:

“`bash
protoc -I=. –python_out=. my_proto_file.proto
“`

Replace `my_proto_file.proto` with the actual name of your protobuf file.

3. **Run Streamlit App:**
After regenerating the protobuf code, try running your Streamlit app again.

### Option 3: Downgrade Protobuf

1. **Downgrade Protobuf:**
If upgrading or regenerating the protobuf code doesn’t work, you can try downgrading the `protobuf` package to version 3.20.x or lower:

“`bash
pip install protobuf==3.20.0
“`

2. **Run Streamlit App:**
After downgrading the `protobuf` package, try running your Streamlit app again.

Choose the option that works best for your situation. If you still encounter issues, feel free to provide more details, and we can explore additional troubleshooting steps.
c:ProgramDataminiconda3envsgorillalibsite-packagestransformersgenerationutils.py:1219: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)
warnings.warn(
c:ProgramDataminiconda3envsgorillalibsite-packagestransformersgenerationutils.py:1313: UserWarning: Using `max_length`’s default (20) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers — we recommend using `max_new_tokens` to control the maximum length of the generation.
warnings.warn(

The warnings you’re seeing are related to the use of certain parameters in the Transformers library, and they indicate that the current approach is deprecated and will be removed in future versions. The warnings are specifically related to the control of generation settings for text generation models.

Here’s a brief explanation of the warnings:

1. **Control of Pretrained Model Configuration:**
– The first warning indicates that you have modified the pretrained model configuration to control generation.
– This approach is deprecated and will be removed soon.
– Instead, it’s recommended to use a generation configuration file. You can refer to the documentation for details: [Text Generation](https://huggingface.co/docs/transformers/main_classes/text_generation).

2. **Using `max_length`’s Default (20):**
– The second warning indicates the use of `max_length` with its default value (20) to control the generation length.
– This behavior is deprecated, and it’s recommended to use `max_new_tokens` to control the maximum length of the generation.
– The new parameter provides a more intuitive way to set the maximum number of tokens in the generated sequence.

### How to Address the Warnings:

1. **Update Code:**
– Update your code to use the recommended approach for controlling generation settings. This may involve creating a generation configuration file.

2. **Review Documentation:**
– Review the documentation for the Transformers library, especially the section on text generation, to ensure you’re using the latest and recommended methods: [Transformers Text Generation Documentation](https://huggingface.co/docs/transformers/main_classes/text_generation).

3. **Update Transformers Library:**
– Make sure you have the latest version of the Transformers library. You can update it using:

“`bash
pip install –upgrade transformers
“`

By addressing these recommendations, you can ensure that your code aligns with the latest practices and avoids the deprecated features.

If you need further assistance or have specific questions about adapting your code, feel free to share more details!