Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and al

Llama 2: The New Open LLM SOTA (ft. Nathan Lambert, Matt Bornstein, Anton Troynikov, Russell Kaplan, Whole Mars Catalog et al.)

Wed Jul 19 2023

AILama 2Open SourceStartupsData QualityDramaRegulatory ChallengesBenchmarkingAI DevelopmentPrivacy

Description

A comprehensive summary of the episode discussing the release of Lama 2 in the AI landscape, its impact on innovation, licensing debates, data quality considerations, drama surrounding Lama 2, Meta's commitment to open source and startups, insights from chapters, working with LAMA2 and hardware configurations, benchmarking and potential applications of LAMA2, AI development and future possibilities, the release of LAMA and its implications, and preparing for potential negative scenarios.

Insights

Model competition is crucial

Choosing transparent models and addressing OpenAI's non-transparency with dataset information are key factors in model selection.

Open source models provide stability

Building a business on open source models avoids random deprecation and provides stability for startups.

Data quality is crucial for pre-training

Removing errors and duplicates from training data, using higher quality data, and considering overfitting are important for improving performance.

Lama 2 faces drama and regulatory challenges

Guillaume Lampao's exclusion from the paper, Meta's risk in releasing Lama 2, and the need for transparency about training data are concerns.

Lama's impact on startups and the ecosystem

Lama enables startups to build defensible businesses, reduces latency and cost, and drives innovation in the startup ecosystem.

Working with LAMA2 and benchmarking

Fine-tuning preference models, working with LAMA2 in specific domains, and benchmarking with real production data are important considerations.

AI development and future possibilities

Infusing programming knowledge into AI, optimizing open source models for hardware acceleration, and privacy concerns drive AI development.

Release of LAMA and its implications

Open-sourcing LAMA allows for testing capabilities, fine-tuning for specific applications, and enabling new research opportunities.

Preparing for potential negative scenarios

Understanding and developing on top of models, improving safety and interpretability, and the rapid progress of language model capabilities are important for preparing for potential negative scenarios.

Meta's commitment to transparency and research

Meta's release of LAMA showcases their commitment to transparency, research, and supporting the startup ecosystem.

Chapters

Release of Lama 2 in the AI Landscape
'Llama 2' Release and its Impact
Recent Projects and Collaborations
Open Source Availability and Licensing Debate
Data Quality and Pre-training Considerations
Drama and Concerns Surrounding Lama 2
Meta's Commitment to Open Source and Startups
Insights from Chapters
Working with LAMA2 and Hardware Configurations
Benchmarking and Potential Applications of LAMA2
AI Development and Future Possibilities
Release of LAMA and its Implications
Preparing for Potential Negative Scenarios

Summary

Transcript

Release of Lama 2 in the AI Landscape

00:00 - 07:14

Emergency pod discussing the release of Lama 2 in the AI landscape
Guests include Nathan Lambert from Huggingface, Matt Bornstein of A16Z, Anton from Chroma, and Russell Kaplan from ScaleAI
Nathan Lambert shares his in-depth paper review and summary of Lama 2
Huggingface collaborated on releasing the model on their platform
Lama 2 is a clear step in the direction of open source retrieval augmented generation
The paper includes details on the stack used to build and run Lama 2
The specific data set used is not clearly explained, but it seems to be a combination of open source preference data and additional fine-tuned data sets
Two reward models were trained for making the model helpful and safe
The new paper is less transparent about training details compared to the first Lama paper
There are ongoing lawsuits related to the training data used in Lama

'Llama 2' Release and its Impact

06:53 - 13:21

'Llama 2' has been released, allowing for commercial use and redistribution.
The release of 'Llama 2' is expected to lead to a surge in innovation.
The quality benchmarks for 'Llama 2' look promising, but it will take time to fully assess its trustworthiness.
It is now possible to download 'Llama 2' without going through the official waiting list.
'Llama 2' can be run on personal computers or accessed via API.
Andreessen Horowitz has a version of 'Llama 2' available on Replicate, although there may be charges associated with it.
The model seems very good for creative tasks and provides fast responses similar to GPT-3.5.
The involvement of the team at Andreessen Horowitz has been a collaborative effort.

Recent Projects and Collaborations

12:58 - 19:45

The team is doing a great job with recent projects and collaborations
They decided to start doing real work to support the ecosystem, especially open source
They released a playground and an AI starter kit for Wama
Their code may or may not be production ready, but they will release more in the future
The companion chatbot is impressive with its features
Alessio has been compiling show notes for the podcast
There is a larger pre-training corpus for llama - model
Supervised fine-tuning was interesting, but there are concerns about limitations on certain questions or actions
There are safety considerations before releasing models to the public
More work needs to be done on training these models efficiently
The new llama model has significantly increased data size and context length compared to previous versions

Open Source Availability and Licensing Debate

19:16 - 26:17

A two-line change can make LAMA forget about the context it was trained on.
The rope scaling trick has been verified by Microsoft and applies to previous versions of LAMA as well.
Open source availability will lead to faster advancements in research and optimization.
Commercial license may unlock more features or optimizations.
There is a debate about the definition of open source and its application in this context.
The Lama license requires an extra license from Meta for companies with over 700 million monthly active users.
The usage policy prohibits training other models with Lama, protecting against distilling the model.
Enforcing these clauses is challenging due to the nature of language models and their use cases.
Huggingface has its own license that may be appropriate for LAMA2.
Commercial use of language models is expected to increase, leading to discussions around licensing.
Cross-query attention makes inference on bigger models faster.
Code and map reasoning are not emphasized in the paper but are important use cases for chat GPT.
The early Jeff details confirm the capabilities insinuated by anthropic and open AI.
Chinchilla Optimal may no longer be considered optimal due to the amount of pre-training data used.

Data Quality and Pre-training Considerations

25:50 - 32:36

The amount of pre-training data here goes far beyond Chinchilla Optimal.
Data quality is changing the game in pre-training.
Removing errors and duplicates from the training data is crucial.
Advancements in cleaning methods allow for better data quality.
Using more higher quality data tends to improve performance.
There is a trend of using more and better data in machine learning.
Overfitting becomes a concern when increasing the tokens to prompt ratio.
Chinchilla was optimized for pre-training compute budget, not inference budget.
Moving from research to production requires different objectives and considerations.
The AI arms race drives the need to ship models quickly, even if they are not perfect yet.
Improvements can still be made with more computational resources and time.
We are still in the early stages of AI development where rapid progress is possible.

Drama and Concerns Surrounding Lama 2

32:10 - 38:38

Guillaume Lampao, one of the co-founders of Miss Dral, worked on Lama 2 but was left out of the paper
There may be drama among researchers at Meta
Alex is surprised that Meta released Lama 2 despite their strong language translation capabilities
The open source models for multilingual translation are not very strong
The human eval score for coding abilities in Lama 2 is fairly low
Human eval is not a good measure for coding chatbots and new benchmarks are needed
Fine-tuning may improve code performance in Lama 2 models
The secrecy around these models makes it difficult to make informed decisions as a consumer
Meta took a big risk by releasing Lama 2 and faces regulatory challenges
Transparency about training data would be a competitive advantage for model selection
Meta released Lama as open source with commercial licensing and received support from industry leaders

Meta's Commitment to Open Source and Startups

38:12 - 45:05

Meta released an open-source language model called LAMA, which can be used commercially.
Meta also released an industry leader's suffering, emphasizing the need for open source.
The release of LAMA showcases Meta's commitment to transparency and research.
Meta aims to use LAMA for internal research and development purposes rather than making money directly from it.
The impact of LAMA on the startup ecosystem is significant, enabling startups to build defensible businesses by using open source models.
Startups can triage their AI workloads by using off-the-shelf models for simple queries and fine-tuning for more complex tasks.
Commercially usable high-quality language models like LAMA are crucial for startups to reduce latency and cost.
Hugging Face has launched an inference endpoint for LAMA, providing easy access to the model.
AI becoming a black box is inevitable, but model competition may drive transparency as a competitive factor.

Insights from Chapters

44:49 - 51:52

Model competition is a key factor in choosing transparent models.
OpenAI's non-transparency with dataset information is a problem that needs more transparency.
Building a business on open source models provides stability and avoids random deprecation.
The estimated cost for LAMA2 is primarily for data collection, not GPUs.
Safety was separated and collected separately to gather additional metadata.
Multi-turn tasks require more time and cost for preference data collection.
Code data is harder to obtain, resulting in lower throughput for preference labels.
There is a shift towards using preference data over pre-training datasets.
OpenAI compared human vendors to their models in supervised fine-tuning data.
The right limit for annotations in fine-tuning data needs further research.
Creating diverse sets of tasks requires paying money to ensure variety.

Working with LAMA2 and Hardware Configurations

51:28 - 58:11

Supervised fine tuning data is hard to create but vendors provide high-quality data
Anthropics new base models are good enough at responding to instructions without supervised fine tuning
Investing in preference models and getting RLE JF model is more valuable than training another model
There is a need for preference models for code questions in LAMA
LAMA StackOverflow could be a potential project using LAMA as the base with additional preference models
The ecosystem for working with LAMA has improved, making it easier to work with and fine-tune
Hugging Face and other libraries have added support for LAMA2
There is a need for clear instructions on running LAMA2 on specific hardware configurations like Mac with GPU integration
Llama.cpp project can accelerate LAMA1 on M2 hardware, which may be updated for the new models

Benchmarking and Potential Applications of LAMA2

57:44 - 1:04:34

Excitement about open source models and the potential for chroma retrieval
Evaluating retrieval augmented generation use cases with benchmarks like PSYQ data sets
The need for benchmarks that reflect real production data
The availability of open assistant data set for benchmarking purposes
Interest in benchmarking models with 8 attention heads
The emergence of Tool Forma and its potential applications with LAMA2
Scale AI's plans to fine-tune LAMA2 on domain-specific data sets
The request for a Chat GPT code interpreter built on top of LAMA2
The importance of acquiring preference data from developers for training foundation models
Scale AI's focus on crowd-sourcing expertise in specific domains

AI Development and Future Possibilities

1:04:10 - 1:11:23

OpenAI's growth has been driven by professionals who have passed exams or are licensed in their respective fields.
Infusing programming knowledge into AI is a way to contribute to the development of AI-powered society.
By the time AI is strong enough to simulate human beings, people's data will already be incorporated, offering a form of immortality.
Optimizing open source models for hardware acceleration on laptops and smartphones opens up new use cases.
Smaller models that can run on phones are more interesting when combined with retrieval or tool usage.
Language models can be used for brainstorming and generating names for things.
Evaluation prompts and coding questions can be generated using language models.
The dynamics between OpenAI, Microsoft, and other partners are complex and involve considerations of ownership and compute capabilities.
Privacy concerns drive some companies to prefer running inference on their own hardware rather than relying on external APIs.
Paranoia around data privacy influences decisions about self-hosting open source language models.

Release of LAMA and its Implications

1:10:54 - 1:17:40

The model was not trained using Azure Compute, but rather on the company's supercluster and internal production cluster.
Open-sourcing the model can generate goodwill and adoption for Meta, as well as spur more open source initiatives from other companies.
The community will need to test the model's capabilities and fine-tune it for specific applications.
If the model is sufficiently capable, it could enable new uses and research opportunities by allowing access to its internal state.
This release provides an opportunity for academic research to proceed without reliance on OpenAI APIs.
There will likely be an explosion of domain-specific fine-tunes and use case-specific applications.
Fine-tuning models exclusively for specific tools can greatly improve their utility in real-world applications.
There is optimism about the potential of agents built on fine-tuned models, although they are currently considered toys.
The release of Llama is not expected to significantly impact AI doom scenarios or improve them.

Preparing for Potential Negative Scenarios

1:17:11 - 1:19:53

Having a model that we can understand and develop on top of will help us prepare better for potential negative scenarios.
Working with the core internals of the models improves the safety story and interpretability.
The language model capabilities are progressing rapidly, becoming more common and commodified.
In the future, it may be common for every computer to run large language models natively.
Thanking everyone for joining and sharing their thoughts.
Encouragement to play with Loma2.