Enhanced model response handling: adjusted max_length dynamically and added lower bound for max_new_tokens to ensure optimal output quality. Improved logging of device usage for better debugging.
Refactor response generation to remove sentence truncation, increase max tokens, and adjust model parameters for more comprehensive multi-agent dialogue. Updated readme to reflect improvements.
Added explicit int8 quantization configuration using BitsAndBytes for DeepSeek-V3. Improved error handling and streamlined model loading for better compatibility.
Addressed quantization issue by enforcing fp16 precision for DeepSeek-V3 model loading. Updated error handling and improved compatibility for Multi-Agent XAI Demo.
Removed Hugging Face pipeline dependency and implemented direct model loading for DeepSeek-V3 using AutoTokenizer and AutoModelForCausalLM. Improved fallback robustness and error handling for model operations.
Enhanced error handling and fallback mechanism for DeepSeek-V3. Added detailed error messages, graceful termination, and support for unsupported quantization configurations.
Added fallback mechanism to switch from pipeline to direct model loading for compatibility. Ensured robust handling for environments without pipeline support in the Transformers library.
Switched to using DeepSeek-V3 for both Engineer and Analyst pipelines for text generation. Updated references to ensure compatibility and performance with the model.
Replaced GPT-Neo with Microsoft PHI-4 model using the pipeline method for improved loading and performance. Updated Engineer and Analyst roles to utilize PHI-4 for text generation, maintaining streamlined conversation flow and summarization.
Refined Multi-Agent XAI Demo by hiding explicit prompt references, improving final plan clarity, and enhancing conversational flow with contextual variations.
Enhanced Multi-Agent XAI Demo with hidden prompts, streamlined final plan summarization, and improved response diversity. Adjusted repetition penalties and conversation formatting for clarity.
Refactored Multi-Agent XAI Demo to hide prompts, limit response length, and enable a natural conversational flow between the Engineer and Analyst. Finalized with a cohesive plan summarizing the dialogue.
Revised Multi-Agent XAI Demo to simulate conversational interaction between Engineer and Analyst. Adjusted prompt design to enable natural dialogue flow based on user input and prior responses. Improved final plan summarization to balance technical and analytical perspectives.
Enhanced Multi-Agent XAI Demo by improving response formatting and final plan synthesis. Ensured concise, actionable bullet points for Engineer and Analyst responses, with a cohesive final summary for clarity.
Refactored Multi-Agent XAI Demo to improve output formatting, remove redundant content, and fix issues with tokenizer compatibility. Added fallback for AutoTokenizer import and ensured cohesive and actionable final plan generation.
Added a fallback mechanism for the AutoTokenizer import to handle compatibility issues with older versions of the transformers library. This ensures continued functionality by defaulting to GPT2Tokenizer when AutoTokenizer is unavailable. Streamlined model loading and response generation for robustness in the demo application.
Enhance prompt diversity and response clarity, refine model roles for better differentiation, and address redundancy in outputs to improve multi-agent collaboration.
Replaced large models with smaller, faster-loading models (EleutherAI GPT-Neo-125M for Engineer and Microsoft DialoGPT-small for Analyst) to improve load times and maintain request-handling efficiency.
Updated code to use lightweight models (gpt-neo-125M and DialoGPT-small) to avoid timeouts and ensure compatibility with HuggingFace free tier. Optimized generation parameters for better performance
Updated multi-agent system to use smaller, faster models (EleutherAI GPT-Neo-1.3B and Microsoft DialoGPT-medium) for efficient processing and implemented final plan summarization.
Updated multi-agent system to use Databricks Dolly-v2-12b for Engineer and HuggingFace Zephyr-7b-alpha for Analyst. Improved conversation flow with concise responses, optimized model parameters, and enhanced final plan summarization.
Refined response generation to limit iterations, reduce hallucinations, and enhance summarization for actionable insights in the multi-agent system demo
Refactored multi-agent system to enforce concise responses, limit verbose outputs, improve real-time conversation streaming, and enhance final plan summarization for better user experience in the demo.
Enhance XAI transparency and user experience: Added real-time conversation display during model interactions, limited model exchanges to 3 rounds, improved response quality and clarity, and refined summary generation. Integrated spinner feedback for better UI responsiveness.
Enhance real-time conversation display for Engineer and Analyst, limit exchanges to 3 rounds, and improve XAI explanations for better user transparency.
Addressed identified issues in multi-agent system: Improved iterative model conversation, added clear XAI explanations, enhanced summarization logic, and integrated attention masks and padding tokens to ensure reliable model behavior.
