[AINews] Cerebras Inference: Faster, Better, AND Cheaper • ButtondownTwitterTwitter

The section discusses the latest developments in AI, particularly focusing on the Cerebras Inference service. It compares the performance and pricing of different AI models and providers. Notable mentions include Gemini 1.5 models from Google, open-source models like CogVideoX-5B and Rene 1.3B, and discussions on distributed training and AI ethics. The section also covers AI applications, infrastructure, and insights shared on AI Twitter Recap. Additionally, the AI Reddit Recap includes highlights from /r/LocalLlama about open-source text-to-video AI models.

The Gemini 1.5 Flash 8B model by Google, with 8 billion parameters, shows state-of-the-art performance and efficiency, outperforming larger models like Llama 2 70B. Google's use of standard transformers for Gemini sparked discussions about model performance and potential advancements. Other topics include OpenAI's upcoming 'Strawberry' AI, Google's Gemini 1.5 updates, and advancements in AI image generation and manipulation with models like Flux AI. Additionally, scientific breakthroughs like the discovery of DNA damage repair protein and discussions on AI ethics and societal impact are highlighted.

Model Merging: A member proposed merging the differences between UltraChat and Mistral into Mistral-Yarn as a potential strategy, despite skepticism from others.### Hermes 3 and Llama 3.1 Comparison: A comparison highlighted Hermes 3's competitive performance over Llama 3.1, with benchmark links provided.### Finetuning with Synthetic Data: Discussion on using synthetic data in finetuning models, focusing on Hermes 3 and 'strawberry models.'### OpenRouter Updates: OpenRouter API degradation, Llama 3.1 405B BF16 endpoint, Hyperbolic's BF16 Llama 3.1 405B Base, LMSys leaderboard relevance questioned, and OpenRouter DeepSeek caching in progress.### Eleuther Discussions: Free Llama 3.1 405B API, TRL.X status inquiry, model training data sources, reversing Monte Carlo tree search, and computer vision project feedback request.### LlamaIndex Queries: Supported OpenAI models, library updates, Pydantic v2 issue, GraphRAG authentication error solution, and building NL to SQL chatbot with LlamaIndex tools.### Torchtune Considerations: QLoRA & FSDP1 compatibility, torch.compile vs. Liger kernels, model-wide vs. per-layer compilation performance, impact of activation checkpointing, and balancing speed with compilation granularity.### OpenAI Contemplations: GPT-4 challenges, Mini model vs. GPT-4 comparison, SearchGPT vs. Perplexity strengths, AI sentience debate, and concerns on Orion model access.### Interconnects Updates: Google's new Gemini models, API usability discussions, SnailBot for link notifications, Open Source Data Availability predictions, and compiler nuances in Mojo Discord.### Latent Space Discoveries: Google's Gemini models, Anthropic's coding powerhouse, Artifacts mobile app launch, Cartesia's Sonic AI, and Cerebras' fast inference solution.### OpenInterpreter Developments: New instruction format, Daily Bots launch, Bland's AI phone agent, Jupyter Book metadata guide, and ongoing OpenInterpreter development.### OpenAccess AI Collective Insights: Axlotl on Apple Silicon, IBM's Power Scheduler, QLora FSDP parameters debate, and token behavior impact on model training.### DSPy Achievements: Success at MEDIQA challenge, potential 'ImageNet moment' at NeurIPS HackerCup, and increased adoption due to notable wins.

DSPy is not only effective for NLP but also for other domains like code generation, as demonstrated in a recent talk covering its use in the NeurIPS HackerCup challenge. For those interested in DSPy, a recent talk by @kristahopsalong on Weights & Biases provides a great starting point for exploring DSPy for coding, including its optimizers and a hands-on demo using the 2023 HackerCup dataset. Additionally, there was a user inquiry about changing the OpenAI base URL and model to a different LLM like OpenRouter API, with attempts to modify parameters in the code snippets.

mrdragonfox made a statement in the Unsloth AI community collaboration channel. The conversation seems to revolve around unusual behavior observed by the user.

Perplexity AI General

The section provides information on various topics related to Llama 3.1 8B performance expectations, model size, tagging conversations with topics, and explaining RAG systems in a ChatGPT interface. It also includes details on a Flex-Attention visualization tool and a Tiny ASIC for 1-bit LLMs. Additionally, the section mentions Nous Research AI discussions on research papers and interesting links. Lastly, updates from OpenRouter announcements, Eleuther general and research discussions, and LlamaIndex blog and general channels are highlighted.

Google announced the release of three experimental Gemini models: Gemini 1.5 Flash-8B, Gemini 1.5 Pro, and Gemini 1.5 Flash. These models offer improved capabilities for coding, complex prompts, and have been significantly enhanced. The API's usability was discussed with frustration over limitations, including the evaluation of the 8B model on RewardBench. Additionally, a user questioned the Q* hypothesis relevance and its link to Monte Carlo Tree Search, expressing skepticism about OpenAI's release strategy.

DSPy's "ImageNet Moment": DSPy's "ImageNet" moment is attributed to @BoWang87 Lab's success at the MEDIQA challenge, where a DSPy-based solution won two Clinical NLP competitions with significant margins of 12.8% and 19.6%. This success led to a significant increase in DSPy's adoption, similar to how CNNs became popular after excelling on ImageNet.

NeurIPS HackerCup Challenge: DSPy's Next "ImageNet Moment"?: The NeurIPS 2024 HackerCup challenge is seen as a potential "ImageNet moment" for DSPy, similar to how convolutional neural networks gained prominence after excelling on ImageNet. The challenge provides an opportunity for DSPy to showcase its capabilities and potentially gain even greater adoption.

DSPy for Coding: Getting Started: For those interested in DSPy, @kristahopsalong recently gave a talk on Weights & Biases about getting started with DSPy for coding, covering its optimizers and a hands-on demo using the 2023 HackerCup dataset. The talk provides a great starting point for anyone interested in learning more about DSPy and its applications in coding.

DSPy Talk on Weights & Biases: The talk by @kristahopsalong on Weights & Biases covered the latest information on DSPy's optimizers and included a hands-on demo of code generation using DSPy. The talk is available on YouTube at https://www.youtube.com/watch?v=yhYeDGxnuGY.

DSPy for Code Generation: DSPy is being used for code generation, with a recent talk covering its use in the NeurIPS HackerCup challenge. This suggests that DSPy is not only effective for NLP but also for other domains like code generation.

• A member inquired about benchmarking Llama 3.1 using a custom API and inference pipeline.
• They seek guidance on getting started with benchmarking and optimizing their system.
• Another member explores solutions to integrate a custom model handler for function-calling features.