Open Router Models and its Strengths

Mixtral 8x7B

• Ability to follow instructions
• Complete requests
• Generate creative text formats
• Fine-tuned to be a helpful assistant
• Sparse Mixture of Experts architecture contributes to high performance across different benchmarks

Dolphin 2.5 Mixtral 8x7B

• Proficiency in coding
• Uncensored nature
• Reliable choice for tasks that require accurate and detailed language generation

Mistral-Medium

• Proficiency in reasoning, code, JSON, and chat applications
• Large context window
• Closed-source, flagship status

Mistral-Small

• Supports multiple languages including English, French, Italian, German, and Spanish
• Coding capabilities
• Versatile choice for multilingual and technical applications

Mistral-Tiny

• Cost-effectiveness
• Suitable for applications that require efficient and budget-friendly language processing solutions

Open Hermes 2.5

• Creative and engaging writing
• Some inconsistencies in instruction adherence and character consistency
• Excels in real-time interactions
• Proficiency in a variety of language tasks

Mistral 7B Instruct

• Processing and generating responses based on specific instructions
• Useful in tasks that require a high level of precision and adherence to guidelines

Psyfighter v2 13B

• Ability to provide long, detailed responses
• Improved prose and logic capabilities
• Designed to offer high-quality outputs, especially in story mode
• Valuable tool for various natural language processing tasks

Code Llama 34B Instruct

• Generate text for code synthesis and understanding
• Particularly useful in the context of Python

Phind Code Llama 34B

• Designed for general code synthesis and understanding
• Specifically for Python
• An auto-regressive language model that uses an optimized architecture

Goliath 120B

• Excels in roleplaying
• Best LLM for this purpose
• Outperforms smaller models in prose, understanding, and handling complex scenarios

PPLX 70B

• Highly regarded for its translation capabilities
• Cross-language understanding
• Instruction following
• Reading between the lines
• Handling complex scenarios
• Humor comprehension

PPLX 7B

• Exceptional processing speed and efficiency
• Generates answers within seconds
• Produces an average of 140 tokens per second

Nous Hermes 70B

• Provide long and detailed responses
• Lower hallucination rates
• Absence of OpenAI censorship mechanisms in its training data

Airoboros L 2 70B

• Particularly adept at handling detailed coding tasks with specific criteria
• Create applications based on detailed requirements
• Write multi-threaded servers
• Generate optimal responses to instructions utilizing a set of provided tools

Synthia 70B

• Known for providing correct answers to a high percentage of multiple-choice questions
• Ability to follow instructions and acknowledge all data input

Mythalion 13B

• Available in various quantization methods
• Each with different trade-offs between model size and quality

Yi 34B

• Large bilingual (English/Chinese) language model
• Contains 34 billion parameters
• Trained on a dataset that supports a 4K sequence length
• Can be expanded to 32K during inference

Yi 6B

• Might excel in applications where quick and accurate language processing is required but with limited computational resources

Noromaid 20B

• Performance in creating lewd stories and roleplay scenarios

Llama 2-70B Instruct v2

• Performance has been compared to other LLMs, such as Llama2-70B
• Achieves competitive results

Llama 2-13B

• Strength lies in its ability to handle a high number of requests per second
• Minimize latency
• Provide cost-effective solutions for NLP tasks

Google Palm 2 & Google Palm 2 32k

• Model architecture and objective have also been updated to achieve overall better performance
• Faster inference
• Fewer parameters to serve
• Lower serving cost

Mistral Open Orca 7B

• Performance has been compared to other LLMs, such as Llama2-70B
• Achieves competitive results

Neural Chat 7B

• Ability to conduct natural, flowing conversations
• Suitable for chatbots and virtual assistant applications

MythoMist

• Strength lies in its experimental nature
• Active benchmarking process
• Tailored to specific user goals

OpenChat

• Fine-tuned with C-RLFT
• Achieved the highest average performance among all 13B open models on three standard benchmarks

Zephyr 7B

• Excels in performance and efficiency
• Scores closely correlate with human ratings of model outputs
• Outperforms larger models on benchmarks like MT-Bench and AlpacaEval

Nous Capybara 34B

• Key strengths include fast performance
• Suitable for real-time applications and large-scale language processing tasks

RWKV v.5

• Ability to handle a wide range of languages
• Performance comparable to transformer models
• Efficient use of resources such as VRAM during both training and inference