Hummerbot AI - Model Selection

Overview

Hummerbot AI supports multiple AI providers and models, allowing users to select the most appropriate model for their specific needs. The platform offers both online and offline models with different capabilities and use cases.

Supported AI Providers

OpenAI

OpenAI models are cloud-based and offer state-of-the-art capabilities across various domains.

GPT-4o

• Description: Latest flagship GPT model optimized for reasoning, speed, and cost. Supports text, vision, and code.
• Strengths: Reasoning, multimodal input, complex problem-solving, natural conversation
• Best For: Complex reasoning tasks, multimodal inputs, high-quality conversations

GPT-4 Turbo

• Description: More affordable and faster variant of GPT-4, with nearly the same quality.
• Strengths: Cost efficiency, long context, balanced performance
• Best For: Applications requiring long context windows with high quality

GPT-3.5 Turbo

• Description: Fast and cost-efficient model for everyday tasks. Great for chat and lightweight applications.
• Strengths: Conversational tasks, summarization, lightweight coding, lower latency
• Best For: Everyday chat applications, quick summarization, lightweight coding tasks

text-davinci-003

• Description: Most capable GPT-3 model. Can do any task the other models can do, often with higher quality, longer output and better instruction-following.
• Strengths: Complex intent, cause and effect, creative generation, summarization
• Best For: Complex reasoning tasks, creative generation, detailed summarization

code-davinci-002

• Description: Most capable Codex model. Particularly good at translating natural language to code.
• Strengths: Code generation, code completion, code understanding
• Best For: Code generation tasks, natural language to code translation

Anthropic

Anthropic models are known for their safety and helpfulness.

Claude 3 Opus

• Description: Anthropic's most powerful model, great for reasoning, creativity, and nuanced tasks.
• Strengths: Deep reasoning, detailed answers, long context handling, safe alignment
• Best For: Complex reasoning, creative tasks, long-form content generation

Claude 3 Sonnet

• Description: Balanced Claude model with speed and quality trade-off, good for production apps.
• Strengths: Everyday reasoning, content generation, balanced performance
• Best For: General purpose applications requiring good balance of speed and quality

Claude 3 Haiku

• Description: Fastest Claude model, designed for real-time tasks and lower cost workloads.
• Strengths: Speed, lightweight reasoning, low-latency apps
• Best For: Real-time applications, quick responses, cost-sensitive applications

Google

Google's Gemini models offer strong multimodal capabilities.

Gemini 1.5 Pro

• Description: Google's most powerful multimodal model with advanced reasoning and long context window.
• Strengths: Multimodal understanding, long context, strong reasoning, research tasks
• Best For: Multimodal tasks, research applications, long context processing

Gemini 1.5 Flash

• Description: Lightweight, faster Gemini variant optimized for real-time apps and cost efficiency.
• Strengths: Low latency, cost-effective, scalable applications
• Best For: Real-time applications, cost-sensitive deployments

Mistral

Mistral models offer open-source alternatives with strong performance.

Mixtral 8x7B

• Description: Open-weight Mixture of Experts model. High efficiency and quality at scale.
• Strengths: Open-source, coding, reasoning, multilingual, cost-efficient inference
• Best For: Applications requiring open-source models, multilingual tasks

Mistral 7B

• Description: Small but powerful open-source model optimized for speed and versatility.
• Strengths: Efficient deployment, lightweight tasks, multilingual
• Best For: Lightweight applications, efficient deployments

Meta

Meta's LLaMA models provide open-source alternatives.

LLaMA 3 70B

• Description: Meta's largest open-weight LLaMA model, competitive with proprietary LLMs.
• Strengths: Reasoning, code generation, research, versatile open-source applications
• Best For: Research applications, complex reasoning tasks

LLaMA 3 8B

• Description: Smaller LLaMA model designed for efficiency while keeping strong quality.
• Strengths: Deployment flexibility, local hosting, multilingual tasks
• Best For: Local deployments, efficient applications

Ollama (Offline Models)

Ollama enables local model hosting with privacy and control.

Local Models

• Description: Self-hosted models that run locally on your machine
• Strengths: Privacy, control, no API costs, customizable
• Best For: Privacy-sensitive applications, offline usage, experimentation

Model Selection Guidelines

For Creative Tasks

• GPT-4o: Best for complex creative tasks requiring multimodal input
• Claude 3 Opus: Excellent for creative writing and nuanced tasks
• GPT-3.5 Turbo: Good for quick creative tasks with lower cost

For Technical Tasks

• code-davinci-002: Best for code generation from natural language
• GPT-4o: Strong for complex technical reasoning
• LLaMA 3 70B: Good open-source option for technical tasks

For Conversational AI

• GPT-4o: Best for complex conversations with multimodal input
• Claude 3 Sonnet: Good balance for everyday conversations
• GPT-3.5 Turbo: Fast and cost-effective for simple conversations

For Multimodal Tasks

• GPT-4o: Best for text and image input
• Gemini 1.5 Pro: Strong alternative with excellent multimodal capabilities

For Cost-Effective Solutions

• GPT-3.5 Turbo: Most cost-effective for simple tasks
• Claude 3 Haiku: Fast and cost-effective for quick responses
• Mistral 7B: Open-source option for cost-sensitive applications

For Privacy-Sensitive Applications

• Ollama Models: Self-hosted for complete privacy control
• LLaMA 3 8B: Good open-source option for local deployment

Model Parameters

Temperature

• Controls randomness in responses
• Lower values (0.1-0.3): More deterministic, focused responses
• Higher values (0.7-1.0): More creative, diverse responses

Maximum Length

• Controls the maximum number of tokens in the response
• Higher values allow for longer, more detailed responses
• Lower values provide more concise responses

Top-P

• Controls diversity through nucleus sampling
• Lower values (0.1-0.5): More focused, predictable responses
• Higher values (0.8-1.0): More diverse, creative responses

Switching Between Models

1. Open the Model Selector dropdown in the AI interface
2. Browse models by category (Online/Offline) and provider
3. Hover over a model to see detailed information
4. Select the desired model to begin using it
5. Adjust parameters as needed for your specific use case