Dolphin-2.8-7B: The Best Conversational Coding Focused 32K Mistral 7B V0.2 Model Finetune?

Ai Flux
2 Apr 202412:15

TLDRThe video discusses the release of the mistol 7B version o.2 AI model and its interesting trade-offs with a large context window. Eric Hartford's fine-tuned modification, dolphin 2.8, is introduced, showcasing its capabilities in conversational and coding skills. The model's uncensored nature and performance on benchmarks are also highlighted, with examples demonstrating its understanding of webg and the mandal BR set. The video encourages viewers to explore the model's potential and share their experiences.

Takeaways

  • 🚀 Introduction of Mistol 7B version 0.2 with a 32k token context window, sparking initial comparisons to the original Mistol 7B.
  • 🌟 Eric Hartford's announcement of a fine-tuned and modified version of Mistol 7B, named Dolphin 2.8.
  • 🎉 Dolphin 2.8 is fully licensed with Apache 2.0, allowing commercial use, which is a notable aspect in the AI model landscape.
  • 💡 Training of Dolphin 2.8 was sponsored by Cruso Cloud, with GPUs provided for the process.
  • 📈 Dolphin 2.8's training involved interesting datasets, including cognitive computations, code feedback, and filtered instruction data.
  • 🔍 The model has been fine-tuned to a 16k sequence length, taking around 3 days with 10 L4s from Cruso Cloud.
  • 🌐 Dolphin 2.8 demonstrates a variety of skills, including instruction, conversational, and coding abilities.
  • 🔗 The model is uncensored, with data filtered to remove certain alignment and bias, placing responsibility on the user for its ethical application.
  • 📊 Benchmark scores for Dolphin 2.8 are competitive, ranking alongside other capable models like Neural Hermes 2.5 and Platypus.
  • 🛠️ The model's performance in coding tasks and understanding complex concepts like webg and the Mandelbrot set is notable, showcasing its potential for problem-solving.
  • 🔮 The potential for future improvements in the model is highlighted, with the expectation that capabilities will continue to grow.

Q & A

  • What is the main topic of the video script?

    -The main topic of the video script is the discussion of the new AI model, Dolphin 2.8, which is based on the Mistol 7B version o.2 and its capabilities, improvements, and potential applications.

  • Who developed the Dolphin 2.8 model?

    -The Dolphin 2.8 model was developed by Eric Hartford, who is well-known and highly regarded in the AI community.

  • What are the key features of Mistol 7B version o.2?

    -Mistol 7B version o.2 has a 32,000 token context window and is trained on a new base model. It also has a full weight fine-tuned to around a 16k sequence length.

  • How does the new model, Dolphin 2.8, differ from the original Mistol 7B?

    -Dolphin 2.8 introduces a variety of instruction, conversational, and coding skills. It is also fully licensed with Apache 2.0, allowing for commercial use, which is a novel aspect compared to many other models.

  • What are some of the unique datasets used to train Dolphin 2.8?

    -Dolphin 2.8 was trained on several unique datasets including cognitive computations, code feedback filtered instruction, and another distinct code feedback dataset. These datasets involve records of people giving feedback on code reviews and comments on GitHub.

  • What is the performance of Dolphin 2.8 on benchmarks?

    -Dolphin 2.8 scores around 68.6 on average across Arc H, SWAG, MLU, and other benchmarks, placing it in an interesting spot among 7 billion parameter models.

  • How does the video script describe the conversational capabilities of Dolphin 2.8?

    -The script describes Dolphin 2.8 as having improved conversational skills due to its training with natural language context about coding. This is an interesting attribute observed by many researchers where enhancing coding abilities also improves conversational abilities.

  • What is the stance on censorship in Dolphin 2.8?

    -Dolphin 2.8 is entirely uncensored, with datasets filtered to remove certain alignment and bias. The model is designed to be highly compliant with any requests, including unethical ones, and users are advised to implement their own alignment layer before using it as a service.

  • What is the significance of the 32k context window in Mistol 7B version o.2?

    -The 32k context window enables a lot of cool things but does not always equate to better performance. It allows the model to handle very large contexts, which can be beneficial for certain tasks, but it also requires more computational resources and may not always lead to improvements in benchmark scores.

  • What is the script's view on the potential applications of Dolphin 2.8?

    -The script suggests that Dolphin 2.8 could be used for a variety of tasks, particularly those related to coding and conversation. It also implies that the model could be used for more complex and innovative projects, pushing the boundaries of what AI can achieve in these areas.

  • How does the video script conclude regarding the use of Dolphin 2.8?

    -The script concludes by encouraging viewers to experiment with Dolphin 2.8 and share their experiences. It suggests that while the performance of Mistol 7B version o.2 may be underwhelming if not used properly, Dolphin 2.8 shows promise in providing more coding performance and specificity.

Outlines

00:00

🚀 Introduction to Mistol 7B v0.2 and Dolphin 2.8

The paragraph discusses the release and initial reactions to the Mistol 7B version 0.2, which features a 32k token context window. It highlights the excitement around the potential advancements over the original Mistol 7B model. Eric Hartford's contribution is noted, with his Dolphin 2.8 model being introduced as a fine-tuned version of Mistol 7B v0.2. The model's training, sponsored by Cruso Cloud, and its licensing under Apache 2.0 are also mentioned. The paragraph sets the stage for a deeper exploration of the capabilities and applications of the new model.

05:00

🌐 Training and Features of Dolphin 2.8

This paragraph delves into the specifics of Dolphin 2.8's training process and its unique features. It mentions the datasets used, including cognitive computations and code feedback datasets, emphasizing the model's coding and conversational skills. The model's uncensored nature and the responsibility placed on the user for its ethical use are also discussed. Benchmark scores are briefly touched upon, providing a performance overview of the model in comparison to other 7B models.

10:01

📊 Evaluation and Testing of Dolphin 2.8

The paragraph focuses on the practical testing and evaluation of Dolphin 2.8. It describes the model's performance in conversational and coding tasks, as well as its understanding of web technologies like WebGL. The author's personal projects and experiences with the model are shared, offering insights into its capabilities and limitations. The paragraph also discusses the model's approach to problem-solving and its potential for learning from simpler, naive solutions.

🤖 Uncensored Nature and Future Exploration

The final paragraph addresses the uncensored nature of Dolphin 2.8 and its potential for misuse, while emphasizing the user's responsibility in utilizing the model ethically. It also expresses excitement for future releases and improvements in AI technology, inviting the audience to share their thoughts on the model's usefulness and potential applications. The author concludes with a call to action for the viewers to engage with the content and look forward to upcoming videos.

Mindmap

Keywords

💡Mistol 7B version o.2

Mistol 7B version o.2 is an updated version of a large language model with a 32,000 token context window. This version is seen as an improvement over the original 7B model released a year prior. The script discusses the interesting trade-offs that come with a large context window, such as enabling cool features, but not always improving performance. It is used as a base for further fine-tuning and modification by researchers and developers.

💡Dolphin 2.8

Dolphin 2.8 is a fine-tuned and modified version of the Mistol 7B version o.2 model, developed by Eric Hartford. It is noted for its fully licensed nature under Apache 2.0, meaning it can be used commercially. The model is trained with GPUs provided by Cruso Cloud and has a variety of instruction, conversational, and coding skills. It is also described as being uncensored, meaning it does not have extra layers to prevent certain types of outputs.

💡Cruso Cloud

Cruso Cloud is a provider of GPU services mentioned in the script. It sponsored the training of the Dolphin 2.8 model and is noted for having an interesting approach to cloud computing. The speaker has used their services before and finds them to be of high quality.

💡Hugging Face

Hugging Face is a company and platform that provides tools and services for developers working with AI and machine learning models. In the script, it is noted for recent enhancements, including the capability to deploy AI models on Cloudflare Edge, which is considered a groundbreaking development for deploying AI on GPUs.

💡Code Feedback Data Sets

Code Feedback Data Sets are collections of data that include records of people giving feedback on code reviews and comments on platforms like GitHub. These data sets are used to fine-tune AI models to better understand and engage in coding-related conversations and tasks.

💡Benchmarks

Benchmarks are standardized tests or criteria used to evaluate the performance of a model or system. In the context of the script, benchmarks are used to compare the performance of different AI models, particularly those with 7 billion parameters.

💡WebGL

WebGL, or Web Graphics Library, is a JavaScript API for rendering interactive 3D and 2D graphics within any compatible web browser without the use of plug-ins. It is used by developers to create visually rich applications and experiences on the web.

💡Mandelbrot Set

The Mandelbrot Set is a mathematical set of complex numbers that is famously known for its intricate and infinitely complex fractal boundary. It is generated by a simple iterative algorithm and has become an iconic image in the field of computer graphics and mathematics.

💡Uncensored Model

An uncensored model refers to an AI model that has not been modified to restrict or filter its outputs based on certain criteria or to prevent it from generating inappropriate content. Such a model is expected to be used responsibly by the developers or users, who are advised to implement their own alignment layers before using the model.

💡Performance Profiling

Performance profiling is the process of measuring and analyzing the performance of a system or model, typically to identify bottlenecks, optimize resources, and improve efficiency. In the context of AI models, it involves evaluating how well the model handles tasks, processes information, and generates outputs.

💡Naive Approach

A naive approach refers to a simple, straightforward, or unpretentious method or solution to a problem. It often lacks complexity and may not account for all possible variables or scenarios. In the context of the script, a naive approach is seen as beneficial because it is easier to understand and learn from, with less context to read through.

Highlights

Introduction of the mistol 7B version o.2 and its comparison with the original version released a year ago.

The 32k token context window in the new version enables many cool features, but it doesn't always mean better performance.

Eric Hartford's convincing fine tune and modification of the model, naming it dolphin 2.8.

Dolphin 2.8 is fully licensed with Apache 2.0, allowing commercial use.

Training of the model was sponsored by cruso Cloud, with GPUs provided for the process.

Hugging face's recent enhancements, including the capability to deploy to Cloudflare Edge, is noted as groundbreaking.

The model is based on mistol 7B version o.2 and has a 32k context length, fine-tuned to a 16k sequence length.

Dolphin 2.8 has a variety of instruction, conversational, and coding skills.

The data sets used for training include cognitive computations, code feedback filtered instruction, and distinct code feedback data sets.

The model is uncensored, with data sets filtered to remove certain alignment and bias.

Users are advised to implement their own alignment layer before exposing the model as a service.

Benchmark scores for the model are around 68.6 average, placing it in an interesting spot among other 7B models.

The model's performance in a non-coding conversation shows it can understand and respond without explicit instruction prompts.

In a coding-related task, the model demonstrates understanding of webg and provides a correct structure for a complex 3D rendering task.

The model's naive approach to problem-solving can be beneficial for learning and understanding.

The model's uncensored nature is highlighted, emphasizing user responsibility for its use.

The video ends with a call to action for viewers to share their thoughts on using the model and its potential applications.

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