Summary of Best LLM Evaluation Models

Deeep Seek V2

• Challenging for small context length prompts
• Issues with multi-chain conversations may arise

Claude 3.5 Sonnet

• Faster compared to Deep Seek V2
• Less chatty
• Quality metrics and responses are promising

Opus 3

• Comparable performance to Claude 3.5 Sonnet

LangChain

• Helpful in transforming data to model-friendly format
• Provides a pandas/SQL plugin

GPT-4

• Effective for organization-specific QA systems
• Requires conscious phrasing of queries for more accurate results

Mistral Model

• Considered better for tool use and reasoning
• Gemma outperforms in JSON-related tasks

Starling LM 7B

• Prompts quick, ready-to-use answers
• Maintains domain-specific language in rewrites

ReAct Agent on LangChain

• Utilizes Mistral Instruct
• Performs well in tasks like weather lookup and reasoning

Yi Model

• Good for code generation tasks

Meta-Llama-3-8B-Instruct.Q5_K_M.gguf

• High quality output for complex tasks

Aya 23 8B

• Excellent performance for general use cases

Languages and Frameworks Mentioned:

• Deeep Seek V2
• Claude 3.5 Sonnet
• Opus 3
• LangChain
• GPT-4
• Mistral Model
• Starling LM 7B
• ReAct Agent on LangChain
• Yi Model
• Meta-Llama-3-8B-Instruct.Q5_K_M.gguf
• Aya 23 8B

These summarizations are based on Reddit comments and feedback from users utilizing various LLM models for different tasks and datasets.