Add tinyllama model agent

[Dhivya-Bharathy]

User description

This agent uses the TinyLlama-1.1B model to generate code or responses based on user input prompts. It demonstrates a minimal setup for building an AI assistant using Hugging Face Transformers with a lightweight language model.

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PR Type

Documentation

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Description

• Added five new Jupyter notebooks in the examples/cookbooks directory, each demonstrating practical AI agent use cases with detailed instructions and code examples.

• Introduced a notebook for using the TinyLlama-1.1B model as a simple AI agent, including setup, response generation, and usage demonstration.

• Added a comprehensive code analysis agent notebook, featuring structured reporting with Pydantic schemas and example analysis workflows.

• Provided a predictive maintenance workflow notebook showcasing multi-agent orchestration for sensor data analysis, anomaly detection, and maintenance scheduling.

• Included a beginner-friendly notebook for the Qwen2.5-0.5B-Instruct model, guiding users through chat-based generation tasks.

• Added a Gemma 2B instruction agent notebook, covering model setup, prompt configuration, inference, and model saving for instruction following and code generation.

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Changes walkthrough 📝

Relevant files

Documentation

TinyLlama_1_1B_model_SimpleAIAgent.ipynb Add TinyLlama-1.1B model agent demo notebook with usage example examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb Added a new Jupyter notebook demonstrating how to use the TinyLlama-1.1B model as a simple AI agent. Includes installation instructions for required packages (transformers, accelerate, torch). Shows how to load the TinyLlama model and tokenizer from Hugging Face. Defines a Python function (generate_response) for generating responses from the model. Provides an example prompt and prints the model's output. Contains markdown explanations and a Colab badge for easy access. +2762/-0 Code_Analysis_Agent.ipynb Add notebook for AI-powered code analysis agent with structured reporting examples/cookbooks/Code_Analysis_Agent.ipynb Added a new Jupyter notebook demonstrating how to build an AI agent for code analysis and quality assessment. Introduced dependencies installation, API key setup, and code ingestion using gitingest. Defined a comprehensive Pydantic schema (CodeAnalysisReport) for structured code analysis output. Provided example agent and task setup, a main function to run analysis on a codebase, and sample output display. Included example usage with a mock analysis result and agent info display. +459/-0  Predictive_Maintenance_Multi_Agent_Workflow.ipynb Add predictive maintenance workflow notebook with multi-agent orchestration examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb Added a new Jupyter notebook demonstrating a predictive maintenance workflow using multiple AI agents. Included helper functions for simulating sensor data, performance analysis, anomaly detection, failure prediction, and maintenance scheduling. Defined agents and tasks for a multi-step workflow using the praisonaiagents library. Provided example workflow execution and sample output for maintenance planning. +401/-0  Qwen2_5_InstructionAgent.ipynb Add Qwen2.5 instruction agent notebook for simple chat generation examples/cookbooks/Qwen2_5_InstructionAgent.ipynb Added a beginner-friendly notebook for using the Qwen2.5-0.5B-Instruct model for chat-based generation. Included installation steps, Hugging Face authentication, model loading, and prompt preparation. Demonstrated how to generate a response using a chat template and print the output. Provided clear sectioning and example output for user guidance. +420/-0  Gemma2B_Instruction_Agent.ipynb Add Gemma 2B instruction agent notebook with data prep and inference examples/cookbooks/Gemma2B_Instruction_Agent.ipynb Added a notebook for using Google's gemma-2b-it model for instruction following and code generation. Included dependency installation, model/tokenizer setup, and Hugging Face authentication. Demonstrated prompt configuration, dataset tokenization, inference, and model saving. Provided example outputs and step-by-step explanations for users. +605/-0

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Summary by CodeRabbit

• New Features
  • Added a "Code Analysis Agent" example notebook demonstrating AI-driven code quality assessment, including detailed metrics and recommendations.
  • Introduced a "Gemma 2B Instruction Agent" example notebook showing how to use and fine-tune the Gemma 2B language model for instruction-based tasks.
  • Added a "Predictive Maintenance Multi-Agent Workflow" example notebook showcasing a multi-agent system for predictive maintenance using simulated sensor data and AI agents.
  • Introduced a "Qwen2.5 Instruction Agent" example notebook providing a step-by-step guide for chat-based interactions with the Qwen2.5-0.5B-Instruct model.

[coderabbitai[bot]]

Walkthrough

One notebook's "Open in Colab" badge URL is corrected to match the filename. Four new Jupyter notebooks are added demonstrating various AI agents and workflows: Gemma 2B instruction agent, predictive maintenance multi-agent workflow, Qwen2.5 instruction agent, and TinyLlama simple AI agent. These cover setup, model loading, inference, multi-agent orchestration, and saving models.

Changes

File(s)	Change Summary
examples/cookbooks/Code_Analysis_Agent.ipynb	Updates "Open in Colab" badge URL to match notebook filename casing; no other changes.
examples/cookbooks/Gemma2B_Instruction_Agent.ipynb	Adds notebook demonstrating data prep, inference, and saving with Gemma 2B instruction model.
examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb	Adds notebook for multi-agent predictive maintenance workflow using PraisonAIAgents framework.
examples/cookbooks/Qwen2_5_InstructionAgent.ipynb	Adds notebook for chat-based generation with Qwen2.5-0.5B-Instruct model.
examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb	Adds notebook demonstrating a simple AI agent using TinyLlama 1.1B model with generation function.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant Notebook
    participant AI_Agent
    participant Model/Workflow

    User->>Notebook: Run notebook cells
    Notebook->>Model/Workflow: Setup (install, import, authenticate)
    Notebook->>AI_Agent: Define/configure agent(s) and tasks
    Notebook->>Model/Workflow: Provide input (code, prompt, data)
    Model/Workflow->>AI_Agent: Analyze/process/generate output
    AI_Agent->>Notebook: Return results
    Notebook->>User: Display structured output/results

Possibly related PRs

• MervinPraison/PraisonAI#600: Both PRs modify the same Code_Analysis_Agent notebook; this PR updates the badge URL while the other adds the notebook content, making them directly related.

Poem

🐇✨
A badge corrected, links aligned,
New agents born, their skills combined.
Gemma chats and Qwen replies,
TinyLlama's wisdom flies.
Maintenance agents watch and learn,
In notebooks fresh, the rabbits turn—
Hopping through code, new paths discern! 🥕📚

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[qodo-code-review[bot]]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪

🧪 No relevant tests

🔒 Security concerns Sensitive information exposure: Multiple notebooks (Gemma2B_Instruction_Agent.ipynb, Qwen2_5_InstructionAgent.ipynb, Predictive_Maintenance_Multi_Agent_Workflow.ipynb, Code_Analysis_Agent.ipynb) contain code that requires users to directly input API keys or tokens as plaintext in the code. This is a security risk as these credentials could be accidentally committed to version control or shared. Better approaches would include using environment variables, secure credential stores, or Jupyter notebook secrets management.

⚡ Recommended focus areas for review Hardcoded Token The notebook contains a hardcoded placeholder for a Hugging Face token that users need to replace. Consider using a more secure approach for token management. "login(\"Enter your token here\")\n", "\n", Token Authentication The notebook uses a direct token input approach that requires users to manually enter their Hugging Face token. Consider implementing a more secure token handling method. "login(token=\"Enter your huggingface token\")\n" ] API Key Exposure The notebook contains a line where users need to enter their OpenAI API key directly in the code, which is not a secure practice for credential management. "os.environ['OPENAI_API_KEY'] = 'enter your api key'" ],

[qodo-code-review[bot]]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
Security	Secure API key handling Hardcoding API keys directly in the notebook is a security risk. Use a more secure approach that doesn't expose the key in the code, such as loading from environment variables or using a secure input method. examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb [66] -os.environ['OPENAI_API_KEY'] = 'enter your api key' +# Option 1: Prompt for API key input +import getpass +os.environ['OPENAI_API_KEY'] = getpass.getpass("Enter your OpenAI API key: ") +# Option 2: Load from .env file (requires python-dotenv package) +# from dotenv import load_dotenv +# load_dotenv() # loads API key from .env file + [ ] Apply / Chat Suggestion importance[1-10]: 7 __ Why: Valid security concern about hardcoded API keys. The suggestion provides practical alternatives and accurately identifies the security risk, though it's an error handling/security suggestion which caps the score.	Medium
	Remove hardcoded API key Hardcoding API keys directly in notebooks is a security risk. Consider using environment variables or a secure configuration file that's not committed to version control instead. examples/cookbooks/Code_Analysis_Agent.ipynb [67] -os.environ['OPENAI_API_KEY'] = 'your_api_key_here' +# Option 1: Load from environment variable +import os +# os.environ['OPENAI_API_KEY'] = 'your_api_key_here' # Don't hardcode keys +# Option 2: Use a .env file with python-dotenv +# from dotenv import load_dotenv +# load_dotenv() # loads variables from .env file + [ ] Apply / Chat Suggestion importance[1-10]: 6 __ Why: While this is a security best practice, the code uses 'your_api_key_here' as a placeholder in a tutorial context, making it less critical than actual hardcoded keys.	Low
General	Fix repository reference The Colab link points to a personal fork rather than the official repository. This could cause confusion for users and lead them to an outdated or unofficial version of the notebook. examples/cookbooks/Code_Analysis_Agent.ipynb [17] -[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Code_Analysis_Agent.ipynb) +[](https://colab.research.google.com/github/PraisonAI/PraisonAI/blob/main/examples/cookbooks/Code_Analysis_Agent.ipynb) [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 7 __ Why: The Colab link points to a personal fork instead of the official PraisonAI/PraisonAI repository, which could confuse users and lead them to outdated versions.	Medium
Possible issue	Add error handling The function doesn't handle potential CUDA out-of-memory errors that could occur when generating responses with large models. Add error handling to gracefully fall back to CPU if needed and provide feedback to the user. examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb [305-308] def generate_response(prompt, max_length=256): - inputs = tokenizer(prompt, return_tensors="pt").to(model.device) - outputs = model.generate(**inputs, max_new_tokens=max_length) - return tokenizer.decode(outputs[0], skip_special_tokens=True) + try: + inputs = tokenizer(prompt, return_tensors="pt").to(model.device) + outputs = model.generate(**inputs, max_new_tokens=max_length) + return tokenizer.decode(outputs[0], skip_special_tokens=True) + except RuntimeError as e: + if "CUDA out of memory" in str(e): + print("Warning: GPU memory exceeded. Falling back to CPU.") + inputs = tokenizer(prompt, return_tensors="pt") + model_cpu = model.to("cpu") + outputs = model_cpu.generate(**inputs, max_new_tokens=max_length) + model.to(model.device) # Move model back to original device + return tokenizer.decode(outputs[0], skip_special_tokens=True) + else: + raise e [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 6 __ Why: This suggestion adds useful error handling for CUDA out-of-memory scenarios which is a common issue when working with large language models. The fallback to CPU processing provides graceful degradation, though as an error handling improvement it receives a moderate score.	Low
	Fix authentication method The login function is called with a placeholder token value that requires user modification. This will cause authentication failures when users run the notebook without changing it. Instead, use the more secure login() without parameters to prompt for token input. examples/cookbooks/Gemma2B_Instruction_Agent.ipynb [356] -login(token="Enter your token here") +login() # Will prompt for token input when needed [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 6 __ Why: The suggestion correctly identifies that using a placeholder token will cause authentication failures, but there's a minor discrepancy in the existing_code format that doesn't exactly match the actual code structure.	Low
[ ] Update

[codecov[bot]]

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 16.43%. Comparing base (60fd485) to head (70057d9). Report is 77 commits behind head on main.

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #608   +/-   ##
=======================================
  Coverage   16.43%   16.43%           
=======================================
  Files          24       24           
  Lines        2160     2160           
  Branches      302      302           
=======================================
  Hits          355      355           
  Misses       1789     1789           
  Partials       16       16           

Flag	Coverage Δ
quick-validation	0.00% <ø> (ø)
unit-tests	16.43% <ø> (ø)

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