Reflection 70B (Fully Tested) : This Opensource LLM beats Claude 3.5 Sonnet & GPT-4O?

Summary

In this video, the host explores the newly released 'Reflection 70b' model, a fine-tuned Llama 3.1 AI that claims superiority over Claude 3.5 and other open-source models. Utilizing 'reflection tuning,' the model is designed to self-evaluate and correct its reasoning. Despite impressive benchmark results, the video tests its practicality through 13 questions, revealing both its strengths and limitations. While it performs well in certain tasks, the model's high token consumption and inference costs raise concerns about its cost-effectiveness, suggesting it may not yet surpass existing models like Claude in terms of overall value.

Takeaways

• 🐫 **New Model Introduction**: A new fine-tuned model called Reflection 70b has emerged, claiming to be superior to Claude 3.5 and other open-source models.
• 🔍 **Reflection Tuning Technique**: Reflection Tuning is a novel technique that enables LLMs to self-evaluate and correct their reasoning process before providing answers.
• 📊 **Benchmark Domination**: Reflection 70b has reportedly outperformed all models in various benchmarks, although the reliability of these benchmarks is questioned.
• 🚩 **Practical Testing**: The video creator tests Reflection 70b with 13 questions to evaluate its performance in real-world scenarios.
• 💡 **Correctness in Answers**: The model answers a variety of questions correctly, including capital cities, mathematical problems, and logical reasoning.
• 🚫 **Prime Number Failure**: Reflection 70b incorrectly identifies a prime number, indicating it may struggle with certain types of mathematical reasoning.
• 💻 **Coding Question Performance**: The model fails to generate correct code for creating an HTML page with a confetti effect but succeeds in generating a Python program for leap years.
• 📈 **SVG and Landing Page Shortcomings**: It fails to produce accurate SVG code for a butterfly and a sleek landing page, suggesting limitations in creative or design-related tasks.
• 💰 **Cost Concerns**: The model's high token generation raises concerns about inference costs, making it potentially less cost-effective than other models.
• 📉 **Comparison with Other Models**: Despite good performance, Reflection 70b is not on par with larger models like Claude GPT-4, and its higher costs may not justify the modest improvements.

Q & A

What is the new fine-tuned model discussed in the video?

The new fine-tuned model discussed in the video is called 'Reflection 70b'.

What technique was used to train the Reflection 70b model?

The Reflection 70b model was trained using a technique called 'reflection tuning'.

How does reflection tuning work?

Reflection tuning involves the LLM first thinking about how it should answer a question, then reflecting on the answer to consider its correctness, making adjustments if necessary, before producing the final output.

What is the potential drawback of reflection tuning mentioned in the video?

The potential drawback of reflection tuning is that it might generate two to three times more tokens than a general LLM, which significantly increases its inference cost.

How did the video test the Reflection 70b model's capabilities?

The video tested the Reflection 70b model by posing it 13 different questions, ranging from general knowledge to coding-related queries.

What was the outcome of the Reflection 70b model's test on prime number recognition?

The Reflection 70b model failed to correctly identify whether the number 337 is a prime number.

How did the model perform on the HTML and CSS coding question?

The model failed to create an HTML page with a button that explodes confetti when clicked, as the provided code did not work.

Was the Python program for printing leap years successful?

Yes, the Python program for printing the next X leap years based on user input worked correctly.

What was the result of the SVG code generation for a butterfly?

The SVG code generated for a butterfly did not produce a correct representation, resulting in a fail.

How did the Reflection 70b model compare to other models in terms of cost-effectiveness?

The Reflection 70b model was not cost-effective due to its high token consumption for simple answers, making it more expensive for similar results compared to other models like Claude.

What was the final verdict on the Reflection 70b model after testing?

While the Reflection 70b model showed good performance in certain tasks, it was deemed not as effective overall due to its high costs and limitations, and was not on par with models like Claude GPT-40.