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Topic: "context-window"
SOTA Video Gen: Veo 2 and Kling 2 are GA for developers
veo-2 gemini gpt-4.1 gpt-4o gpt-4.5-preview gpt-4.1-mini gpt-4.1-nano google openai video-generation api coding instruction-following context-window performance benchmarks model-deprecation kevinweil stevenheidel aidan_clark_
Google's Veo 2 video generation model is now available in the Gemini API with a cost of 35 cents per second of generated video, marking a significant step in accessible video generation. Meanwhile, China's Kling 2 model launched with pricing around $2 for a 10-second clip and a minimum subscription of $700 per month for 3 months, generating excitement despite some skill challenges. OpenAI announced the GPT-4.1 family release, including GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano, highlighting improvements in coding, instruction following, and a 1 million token context window. The GPT-4.1 models are 26% cheaper than GPT-4o and will replace the GPT-4.5 Preview API version by July 14. Performance benchmarks show GPT-4.1 achieving 54-55% on SWE-bench verified and a 60% improvement over GPT-4o in some internal tests, though some critiques note it underperforms compared to other models like OpenRouter and DeepSeekV3 in coding tasks. The release is API-only, with a prompting guide provided for developers.
Gemma 3 beats DeepSeek V3 in Elo, 2.0 Flash beats GPT4o with Native Image Gen
gemma-3 gemini-1.5-pro gemini-2 o1-preview o3-mini-high deepseek-v3 claude-3.7-sonnet qwen-2.5-max google-deepmind openai multimodality multilinguality context-window quantization image-generation model-benchmarking model-performance vision reach_vb _philschmid danielhanchen lmarena_ai osanseviero
Google DeepMind launched the Gemma 3 family of models featuring a 128k context window, multimodal input (image and video), and multilingual support for 140+ languages. The Gemma 3-27B model ranks among the top open models on LMArena benchmarks, outperforming several competitors and matching Gemini-1.5-Pro on benchmarks. Additionally, Gemini 2 introduced Flash Native Image Generation with advanced image editing capabilities, a feature teased by OpenAI but not launched. The updates highlight significant advances in context length, multimodality, and model efficiency via quantization.
not much happened today
claude-3.7-sonnet claude-3.7 deepseek-r1 o3-mini deepseek-v3 gemini-2.0-pro gpt-4o qwen2.5-coder-32b-instruct anthropic perplexity-ai amazon google-cloud deepseek_ai coding reasoning model-benchmarking agentic-workflows context-window model-performance open-source moe model-training communication-libraries fp8 nvlink rdma cli-tools skirano omarsar0 reach_vb artificialanlys terryyuezhuo _akhaliq _philschmid catherineols goodside danielhanchen
Claude 3.7 Sonnet demonstrates exceptional coding and reasoning capabilities, outperforming models like DeepSeek R1, O3-mini, and GPT-4o on benchmarks such as SciCode and LiveCodeBench. It is available on platforms including Perplexity Pro, Anthropic, Amazon Bedrock, and Google Cloud, with pricing at $3/$15 per million tokens. Key features include a 64k token thinking mode, 200k context window, and the CLI-based coding assistant Claude Code. Meanwhile, DeepSeek released DeepEP, an open-source communication library optimized for MoE model training and inference with support for NVLink, RDMA, and FP8. These updates highlight advancements in coding AI and efficient model training infrastructure.
Mixtral 8x22B Instruct sparks efficiency memes
mixtral-8x22b llama-2-7b olmo-7b mistral-ai hugging-face google microsoft intel softbank nvidia multilinguality math code-generation context-window model-performance model-release retrieval-augmented-generation deepfake ai-investment ai-chip hybrid-architecture training-data guillaume-lample osanseviero _philschmid svpino
Mistral released an instruct-tuned version of their Mixtral 8x22B model, notable for using only 39B active parameters during inference, outperforming larger models and supporting 5 languages with 64k context window and math/code capabilities. The model is available on Hugging Face under an Apache 2.0 license for local use. Google plans to invest over $100 billion in AI, with other giants like Microsoft, Intel, and SoftBank also making large investments. The UK criminalized non-consensual deepfake porn, raising enforcement debates. A former Nvidia employee claims Nvidia's AI chip lead is unmatchable this decade. AI companions could become a $1 billion market. AI has surpassed humans on several basic tasks but lags on complex ones. Zyphra introduced Zamba, a novel 7B parameter hybrid model outperforming LLaMA-2 7B and OLMo-7B with less training data, trained on 128 H100 GPUs over 30 days. GroundX API advances retrieval-augmented generation accuracy.
1/9/2024: Nous Research lands $5m for Open Source AI
qlora phi-3 mixtral ollama nous-research openai rabbit-tech context-window fine-tuning synthetic-data activation-beacon transformer-architecture seed-financing real-time-voice-agents trillion-parameter-models kenakafrosty _stilic_ teknium
Nous Research announced a $5.2 million seed financing focused on Nous-Forge, aiming to embed transformer architecture into chips for powerful servers supporting real-time voice agents and trillion parameter models. Rabbit R1 launched a demo at CES with mixed reactions. OpenAI shipped the GPT store and briefly leaked an upcoming personalization feature. A new paper on Activation Beacon proposes a solution to extend LLMs' context window significantly, with code to be released on GitHub. Discussions also covered QLORA, fine-tuning, synthetic data, and custom architectures for LLMs.
1/8/2024: The Four Wars of the AI Stack
mixtral mistral nous-research openai mistral-ai hugging-face context-window distributed-models long-context hierarchical-embeddings agentic-rag fine-tuning synthetic-data oil-and-gas embedding-datasets mixture-of-experts model-comparison
The Nous Research AI Discord discussions highlighted several key topics including the use of DINO, CLIP, and CNNs in the Obsidian Project. A research paper on distributed models like DistAttention and DistKV-LLM was shared to address cloud-based LLM service challenges. Another paper titled 'Self-Extend LLM Context Window Without Tuning' argued that existing LLMs can handle long contexts inherently. The community also discussed AI models like Mixtral, favored for its 32k context window, and compared it with Mistral and Marcoroni. Other topics included hierarchical embeddings, agentic retrieval-augmented generation (RAG), synthetic data for fine-tuning, and the application of LLMs in the oil & gas industry. The launch of the AgentSearch-V1 dataset with one billion embedding vectors was also announced. The discussions covered mixture-of-experts (MoE) implementations and the performance of smaller models.
12/29/2023: TinyLlama on the way
tinyllama-1.1b openai hugging-face gpu-optimization model-deployment discord-bots embedding-models inference-server hardware-compatibility model-performance beta-testing autogen context-window
The Nous/Axolotl community is pretraining a 1.1B model on 3 trillion tokens, showing promising results on HellaSwag for a small 1B model. The LM Studio Discord discussions cover extensive GPU-related issues, Discord bot integration with the OpenAI API, and hardware limitations affecting model usage. Community members also discuss server hosting for embeddings and LLMs, propose updates for Discord channels to improve model development collaboration, and address a gibberish problem in beta releases. The Autogen tool's installation and operational challenges are also clarified by users.
12/15/2023: Mixtral-Instruct beats Gemini Pro (and matches GPT3.5)
mixtral gemini-pro gpt-3.5 gpt-4.5 gpt-4 chatgpt lmsys openai deepseek cloudflare huggingface performance context-window prompt-engineering privacy local-gpu cloud-gpu code-generation model-comparison model-usage api-errors karpathy
Thanks to a karpathy shoutout, lmsys now has enough data to rank mixtral and gemini pro. The discussion highlights the impressive performance of these state-of-the-art open-source models that can run on laptops. In the openai Discord, users compared AI tools like perplexity and chatgpt's browsing tool, favoring Perplexity for its superior data gathering, pricing, and usage limits. Interest was shown in AI's ability to convert large code files with deepseek coder recommended. Debates on privacy implications for AI advancement and challenges of running LLMs on local and cloud GPUs were prominent. Users reported issues with chatgpt including performance problems, loss of access to custom GPTs, and unauthorized access. Discussions also covered prompt engineering for large context windows and speculations about gpt-4.5 and gpt-4 future developments.