Is Phind AI's Code LLama FineTune BETTER Than GPT 4 Code Interpreter?!
TLDRThe video discusses a claim by a team from Find, an AI search engine and programmer, that they've fine-tuned the Codex Lama 34b model to surpass GPT-4 in human evaluation for coding tasks. They utilized a specialized dataset and training methods, running the model on four RTX 3090s to achieve competitive performance. The video also touches on the potential advancements in GPT-4 since its March release and the implications of such developments in the AI coding field.
Takeaways
- 🚀 A group has claimed to beat GPT-4 in human eval using a fine-tuned version of Codex Lama 34b.
- 💡 The individual who managed the feat used four RTX 3090s to achieve performance close to GPT-4's speed.
- 🌟 The team behind this achievement is associated with a product called Find, an AI search engine and programming aid.
- 📈 They focused on fine-tuning Codex Lama 34b with a proprietary dataset aimed at programming questions and solutions.
- 🔍 The fine-tuned model achieved scores of 67.6 and 69.5 on human eval, compared to GPT-4's reported 67.
- 🛠️ The dataset used for fine-tuning contained 80,000 high-quality programming solution and instruction pairs.
- 💻 The models were trained over two epochs with 160,000 examples, without using language models like Laura.
- 🛠️ Tools like Deep Speed 3 and Flash Extension 2 were used for efficient training within a short timeframe.
- 💰 The hardware utilized was 32 A100 80GB GPUs, indicating a significant investment in computational resources.
- 📊 The sequence length for the models was 4096 tokens, and the models were tested with random substrings for native fine-tuning.
- 📈 The team behind Find has released their models for public use, allowing others to verify and scrutinize their claims.
Q & A
What is the main claim made by the group behind the product 'Find'?
-The group behind 'Find' claims that they have managed to beat GPT-4 in coding with a fine-tuned version of the Codex Lamaze 34b model.
What is the significance of the Codex Lamaze 34b model in this context?
-The Codex Lamaze 34b model is significant because it is the model that the group behind 'Find' used to achieve better performance in human eval compared to GPT-4, by fine-tuning it with their proprietary dataset.
What is the 'Find' product mentioned in the script?
-'Find' is an AI search engine and fair programmer focused on programming. The group behind 'Find' is the one claiming to have beaten GPT-4 in coding with the fine-tuned Codex Lamaze 34b model.
How did the group fine-tune the Codex Lamaze 34b model?
-The group fine-tuned the Codex Lamaze 34b model using their internal fine dataset, which they claim is a better representation of what programmers actually do and how they interact with various models.
What type of questions and solutions did the group focus on during the fine-tuning process?
-The group focused on programming questions and solutions, particularly instruction answer pairs, which is similar to Meta's approach with their Codex Lamaze instruct model.
What is the significance of the hardware used in this process?
-The significance of the hardware is that the group used four RTX 3090s and 32 A100 80GB GPUs to run the fine-tuned Codex Lamaze 34b model, achieving performance close to that of GPT-4 in OpenAI's interface.
What tools did the group use to train the models in three hours?
-The group used Deep Speed 0.3 and Flash Extension 2 to train the models in three hours, which are advanced tools for efficient and fast model training.
What is the importance of not using Laura in the models' training?
-Not using Laura in the models' training is important because it indicates that the models were fine-tuned natively, focusing on the specific dataset and tasks relevant to the 'Find' product.
How did the group handle the sequence length and evaluation examples during the fine-tuning process?
-The group handled the sequence length of 4096 tokens and for each evaluation example, they randomly sampled three substrings of 50 characters, which is part of their native fine-tuning approach.
What are the concerns regarding the quantization and perplexity used in the models?
-The concerns are that the specific quantization levels (likely between 4-bit and 6-bit) and the use of perplexity as a tool might have affected the performance and comparability of the models, especially when comparing to GPT-4's performance.
What is the controversy surrounding the human eval scores and the potential leak of data from human eval into GPT-4?
-The controversy is that there is a suspicion that data from human eval might have leaked into GPT-4, potentially improving its coding abilities since the original March release. This has sparked debates about the fairness and accuracy of the comparison between GPT-4 and the fine-tuned Codex Lamaze 34b model.
Outlines
🚀 Fine-Tuning Code Lama 34b for Enhanced Performance
The first paragraph discusses a claim that a fine-tuned version of the Code Lama 34b model was able to outperform GPT-4 in human evaluations. The individual responsible for running the model used four RTX 3090s to achieve impressive performance, despite not initially aiming to surpass GPT-4. The group behind this achievement is a team from a product called Find, an AI search engine and programmer. They assert that their specialized fine-tuned model, using an internal data set of programming interactions, has achieved scores of 67.6 and 69.5 in human evaluations, bettering GPT-4's reported 67 score from an official technical report in March. The paragraph delves into the specifics of their fine-tuning process, focusing on programming questions and solutions, and contrasts it with Meta's approach with the Code Lama Instruct model. It also highlights the hardware used and the training methodologies, including the use of deep-speed and Flash extension tools, and the avoidance of Laura for model training.
💡 Insights into Find Team's Model and GPT-4's Evolution
The second paragraph provides further insights into the Find team's model and its comparison with GPT-4. It discusses the possibility of GPT-4's coding abilities improving since March, especially considering the model's capability to run code with a code interpreter, a feature not available in March. The paragraph questions the official 67.6 score for the Find team's model, suggesting it might be based on unofficial sources and potentially influenced by quantization and perplexity considerations. It also addresses the issue of RLHF (Reinforcement Learning from Human Feedback) and the suspicion of data leakage from human evaluations into GPT-4, which could have impacted its performance. The paragraph concludes by emphasizing the significance of the Find team's achievement, highlighting the potential for smaller entities to develop highly capable coding models outside of large tech corporations.
Mindmap
Keywords
💡Code Lama 34b
💡Human Eval
💡Find
💡Fine-tuning
💡GPUs
💡Deep Speed
💡Quantization
💡RLHF
💡Code Interpreter
💡API
💡Performance Metrics
Highlights
A friend successfully ran the Codex Lama 34b model across four RTX 3090s, achieving impressive performance.
The group behind this achievement is a team from a product called Find, an AI search engine and programmer.
Find's core focus is programming, which aligns with their claims of fine-tuning Codex Lama 34b for better performance.
Find claims to have achieved 67.6 and 69.5 passes on human eval, better than GPT-4's reported 67 in March.
Find's data set features instruction answer pairs, differing from Meta's training approach with Codex Lama Instruct.
The models were fine-tuned over two epochs with 160,000 examples, without using the language model Laura.
Find utilized deep speed zero three and Flash extension 2 for efficient model training.
The hardware used for training consisted of 32 A100 80 GB GPUs.
Find's models were trained on a sequence length of 4096 tokens.
For each evaluation example, three 50-character substrings were randomly sampled as part of the native fine-tuning process.
Find has released both of the models for public testing, allowing scrutiny of their claims.
There are concerns about the quantization levels used in Find's models, with speculation that 4-bit or 6-bit quantizations were employed.
The source of the 67.6 number claimed by Find is questioned, with a link to an official technical report from OpenAI in March.
GPT-4's coding abilities may have improved since March due to the integration of RLHF and the ability to run code with a code interpreter.
The achievement by Find suggests that smaller companies and individuals can now compete with tech giants in developing advanced coding models.
The video encourages viewers to explore the potential of running their own projects on fast GPUs at affordable prices.