Llama 3.1 405B is here! (Tested)
TLDRThe video reviews the capabilities of the newly released Llama 3.1 405B model. The presenter tests its reasoning, code generation, and math problem-solving skills, finding it very impressive. The model's performance is compared to other models like GPT-4 and Cloud 3.5, showing significant improvements in reasoning, code generation, and long-context tasks. It also supports multi-step tool usage and multimodal capabilities. The video concludes with the presenter testing the model on various tasks and sharing the results, highlighting the model's advanced reasoning capabilities.
Takeaways
- 😲 Llama 3.1 with versions of 7B, 70B, and 405B has been released, showcasing advanced reasoning capabilities.
- 🔍 The model's performance on benchmarks is impressive, with the 70B version being particularly strong, and the 405B version outperforming other models like GPT-3.5 and Neotron.
- 🌟 The 405B version is considered one of the largest and most capable open large models available today, with its capabilities almost at the level of GPT-40.
- 📈 The model has a 128k context window, which is a significant increase from previous versions, allowing for better handling of long context retrieval tasks.
- 🛠️ It features multi-step tool usage, which is beneficial for developing agentic workflows and complex problem-solving.
- 📝 The proficiency exam results show that the 5B model's performance is comparable to GPT-3.5 and 2.5 Sunet, while the 70B model performs significantly better.
- 💻 Code generation results are strong, with the 405B version being very close to GPT-3.5 in terms of performance for general-purpose models.
- 👀 The model now supports multimodal capabilities, including vision and video recognition, through a five-stage compositional training approach.
- ⚙️ The model has been quantized from 16-bit to 8-bit FB8, which helps reduce compute requirements and improve throughput and latency.
- 🔢 A 5B model was trained on up to 16,000 H100 GPUs, indicating the scale of hardware needed for training such large models and hinting at future requirements for even larger models.
- 📉 The model has shown some issues with numerical reasoning, particularly with numbers ending in '.11', which could be due to pattern recognition or biases in the training data.
Q & A
What is the main focus of the video?
-The video focuses on testing the reasoning capabilities of the new Llama 3.1 405B model released by Meta and comparing its performance with other models.
What example is used to test the reasoning capabilities of the Llama 3.1 model?
-The example used involves determining which of five candles was blown out first, based on their lengths after being blown out.
What are the versions of the Llama 3.1 model mentioned in the video?
-The versions mentioned are 8 billion, 70 billion, and 405 billion.
How does the Llama 3.1 model compare with other models according to the video?
-The Llama 3.1 model shows impressive performance and is compared with models like GPT-4 Omni and Cloud 3.5 Sun, with the Llama 3.1 405B being described as the biggest and most capable open large model available today.
What are some of the key features of the Llama 3.1 model?
-Key features include a 128k context window, multi-step tool usage, and support for vision and video recognition capabilities.
What improvements does the Llama 3.1 model have over previous versions?
-The Llama 3.1 model has improved reasoning capabilities, better performance on benchmarks, and supports a longer context window of 128k tokens.
What benchmarks are used to evaluate the Llama 3.1 model?
-The benchmarks include MLU and proficiency exams, with comparisons to other models like GPT-3.5 Turbo and Neotron.
How is the performance of the Llama 3.1 405B model in code generation tasks?
-The Llama 3.1 405B model performs well in code generation tasks, providing detailed and accurate Python functions along with explanations and usage examples.
What is a notable improvement in the Llama 3.1 model's response to subjective knowledge tasks?
-The Llama 3.1 model responds to subjective knowledge tasks by acknowledging the subjectivity and providing an overview of popular trends, such as in the example of sushi recommendations.
What hardware was used to train the Llama 3.1 405B model?
-The Llama 3.1 405B model was trained on up to 16,000 H100 GPUs.
What was the result of testing the Llama 3.1 model with the candle problem compared to GPT-4?
-The Llama 3.1 model correctly identified the first candle blown out as candle three and explained the reasoning, whereas GPT-4 chose the wrong candle, demonstrating Llama 3.1's advanced reasoning capabilities.
Outlines
🌟 Impressive Reasoning Capabilities of Lama 3.1
The paragraph discusses the impressive reasoning abilities of Lama 3.1, demonstrated through its ability to solve a candle-burning puzzle. It explains that Lama 3.1 correctly identified the longest candle as the one blown out first due to its shortest burn time. The release of Lama 3.1 includes 8 billion, 70 billion, and 405 billion parameter versions, highlighting significant performance improvements and capabilities. It compares benchmarks with previous versions and other models, noting its strong performance and 128k context window for handling long documents. The model's multi-step tool usage and reasoning abilities are also emphasized.
🚀 Enhanced Performance of 70B Model
This paragraph highlights the significant performance improvements of the 70 billion parameter model, surpassing GPT-3.5 turbo and Nvidia's Neotron for 340 billion on various benchmarks. It underscores the model's code generation abilities, comparing Lama 3.1's 405 billion parameters favorably with other models like Cloud 3.5 Sun. The paragraph also mentions Lama 3.1's support for multi-modal capabilities, including vision and video recognition, achieved through a five-stage compositional training approach. The quantization from 16-bit to 8-bit for improved compute efficiency is also noted.
💻 Advanced Code Generation and Math Problem Solving
The paragraph demonstrates the model's advanced code generation capabilities, including detailed explanations and example usage. It highlights the model's step-by-step approach to solving complex math problems, similar to the Gemma model, although some inaccuracies remain. The importance of quantization for reducing compute requirements without sacrificing performance is emphasized, showcasing the model's ability to handle complex workflows efficiently.
🧠 Enhanced Information Extraction and Prompt Handling
This paragraph explores the model's ability to extract information and handle prompt injections. It successfully extracts model names from abstracts but struggles with recognizing variants like Chinese llama. The model performs well in avoiding hallucinations by not inventing model names when none are present. However, it shows vulnerability to prompt injection attacks, following secondary instructions instead of sticking to the original ones. Despite this, the model demonstrates significant reasoning capabilities, correctly solving the candle puzzle and outperforming GPT-4 in this specific task.
Mindmap
Keywords
💡Llama 3.1
💡reasoning capabilities
💡context window
💡benchmarks
💡multi-step tool usage
💡quantization
💡fireworks inference endpoints
💡code generation
💡proficiency exams
💡human eval
Highlights
Llama 3.1 405B model demonstrates advanced reasoning capabilities.
The model correctly identifies 'tree' as the answer to a reasoning test.
Meta has released Llama 3.1 with versions including 8 billion, 70 billion, and 405 billion parameters.
The model shows improvements on benchmarks compared to previous checkpoints.
Llama 3.1 405B outperforms other models like Gemma 2 and GPT 4 in certain benchmarks.
The model supports a 128k context window, enhancing long context retrieval and reasoning.
Llama 3.1 focuses on tool usage capabilities, including multi-step tool usage.
The model shows strong performance on proficiency exams, comparable to GPT 4 and other advanced models.
Code generation results of Llama 3.1 405B are close to those of GPT 4 and Cloud 3.5.
The model supports multimodal capabilities through a five-stage compositional training approach.
Llama 3.1 405B has been quantized from 16 bit to 8 bit, reducing compute requirements.
The model's training involved straining on up to 16,000 H100 GPUs.
The model provides detailed responses to subjective knowledge tasks, such as describing the best sushi.
Llama 3.1 generates detailed Python functions for code generation tasks.
The model demonstrates step-by-step problem-solving in mathematical tasks.
Llama 3.1 handles information extraction tasks, identifying model names from abstracts.
The model resists prompt injection attacks, sticking to the original instructions.
Llama 3.1 correctly solves the candle puzzle, showcasing complex reasoning capabilities.