All tags
Model: "mixtral-8x7b"
How Carlini Uses AI
gemma-2-2b gpt-3.5-turbo-0613 mixtral-8x7b gen-3-alpha segment-anything-model-2 stable-fast-3d groq intel deepmind box figure-ai openai google meta-ai-fair nvidia stability-ai runway benchmarking adversarial-attacks large-language-models text-generation multimodality robotics emotion-detection structured-data-extraction real-time-processing teleoperation 3d-generation text-to-video nicholas-carlini chris-dixon rasbt
Groq's shareholders' net worth rises while others fall, with Intel's CEO expressing concern. Nicholas Carlini of DeepMind gains recognition and criticism for his extensive AI writings, including an 80,000-word treatise on AI use and a benchmark for large language models. Chris Dixon comments on AI Winter skepticism, emphasizing long-term impact. Box introduces an AI API for extracting structured data from documents, highlighting potential and risks of LLM-driven solutions. Recent AI developments include Figure AI launching the advanced humanoid robot Figure 02, OpenAI rolling out Advanced Voice Mode for ChatGPT with emotion detection, Google open-sourcing Gemma 2 2B model matching GPT-3.5-Turbo-0613 performance, Meta AI Fair releasing Segment Anything Model 2 (SAM 2) for real-time object tracking, NVIDIA showcasing Project GR00T for humanoid teleoperation with Apple Vision Pro, Stability AI launching Stable Fast 3D for rapid 3D asset generation, and Runway unveiling Gen-3 Alpha for AI text-to-video generation.
Rombach et al: FLUX.1 [pro|dev|schnell], $31m seed for Black Forest Labs
gemma-2-2b gpt-3.5-turbo-0613 mixtral-8x7b flux-1 stability-ai google-deepmind nvidia text-to-image text-to-video model-benchmarking open-weight-models model-distillation safety-classifiers sparse-autoencoders ai-coding-tools rohanpaul_ai fchollet bindureddy clementdelangue ylecun svpino
Stability AI co-founder Rombach launched FLUX.1, a new text-to-image model with three variants: pro (API only), dev (open-weight, non-commercial), and schnell (Apache 2.0). FLUX.1 outperforms Midjourney and Ideogram based on Black Forest Labs' ELO score and plans to expand into text-to-video. Google DeepMind released Gemma-2 2B, a 2 billion parameter open-source model that outperforms larger models like GPT-3.5-Turbo-0613 and Mixtral-8x7b on Chatbot Arena, optimized with NVIDIA TensorRT-LLM. The release includes safety classifiers (ShieldGemma) and sparse autoencoder analysis (Gemma Scope). Discussions highlight benchmarking discrepancies and US government support for open-weight AI models. Critiques of AI coding tools' productivity gains were also noted.
Not much happened today
jamba-v0.1 command-r gpt-3.5-turbo openchat-3.5-0106 mixtral-8x7b mistral-7b midnight-miqu-70b-v1.0.q5_k_s cohere lightblue openai mistral-ai nvidia amd hugging-face ollama rag mixture-of-experts model-architecture model-analysis debate-persuasion hardware-performance gpu-inference cpu-comparison local-llm stable-diffusion ai-art-bias
RAGFlow open sourced, a deep document understanding RAG engine with 16.3k context length and natural language instruction support. Jamba v0.1, a 52B parameter MoE model by Lightblue, released but with mixed user feedback. Command-R from Cohere available on Ollama library. Analysis of GPT-3.5-Turbo architecture reveals about 7 billion parameters and embedding size of 4096, comparable to OpenChat-3.5-0106 and Mixtral-8x7B. AI chatbots, including GPT-4, outperform humans in debates on persuasion. Mistral-7B made amusing mistakes on a math riddle. Hardware highlights include a discounted HGX H100 640GB machine with 8 H100 GPUs bought for $58k, and CPU comparisons between Epyc 9374F and Threadripper 1950X for LLM inference. GPU recommendations for local LLMs focus on VRAM and inference speed, with users testing 4090 GPU and Midnight-miqu-70b-v1.0.q5_k_s model. Stable Diffusion influences gaming habits and AI art evaluation shows bias favoring human-labeled art.
Mistral Large disappoints
mistral-large mistral-small mixtral-8x7b gpt-4-turbo dreamgen-opus-v1 mistral-ai openai hugging-face benchmarking model-merging fine-tuning reinforcement-learning model-training tokenization model-optimization ai-assisted-decompilation performance cost-efficiency deception roleplay deep-speed dpo timotheeee1 cogbuji plasmator jsarnecki maldevide spottyluck mrjackspade
Mistral announced Mistral Large, a new language model achieving 81.2% accuracy on MMLU, trailing GPT-4 Turbo by about 5 percentage points on benchmarks. The community reception has been mixed, with skepticism about open sourcing and claims that Mistral Small outperforms the open Mixtral 8x7B. Discussions in the TheBloke Discord highlighted performance and cost-efficiency comparisons between Mistral Large and GPT-4 Turbo, technical challenges with DeepSpeed and DPOTrainer for training, advances in AI deception for roleplay characters using DreamGen Opus V1, and complexities in model merging using linear interpolation and PEFT methods. Enthusiasm for AI-assisted decompilation was also expressed, emphasizing the use of open-source projects for training data.
Karpathy emerges from stealth?
mistral-7b mixtral-8x7b zephyr-7b gpt-4 llama-2 intel mistral-ai audiogen thebloke tokenization quantization model-optimization fine-tuning model-merging computational-efficiency memory-optimization retrieval-augmented-generation multi-model-learning meta-reasoning dataset-sharing open-source ethical-ai community-collaboration andrej-karpathy
Andrej Karpathy released a comprehensive 2-hour tutorial on tokenization, detailing techniques up to GPT-4's tokenizer and noting the complexity of Llama 2 tokenization with SentencePiece. Discussions in AI Discord communities covered model optimization and efficiency, focusing on quantization of models like Mistral 7B and Zephyr-7B to reduce memory usage for consumer GPUs, including Intel's new weight-only quantization algorithm. Efforts to improve computational efficiency included selective augmentation reducing costs by 57.76% and memory token usage versus kNN for Transformers. Challenges in hardware compatibility and software issues were shared, alongside fine-tuning techniques such as LoRA and model merging. Innovative applications of LLMs in retrieval-augmented generation (RAG), multi-model learning, and meta-reasoning were explored. The community emphasized dataset sharing, open-source releases like SDXL VAE encoded datasets and Audiogen AI codecs, and ethical AI use with censorship and guardrails. Collaboration and resource sharing remain strong in these AI communities.
1/16/2024: TIES-Merging
mixtral-8x7b nous-hermes-2 frankendpo-4x7b-bf16 thebloke hugging-face nous-research togethercompute oak-ridge-national-laboratory vast-ai runpod mixture-of-experts random-gate-routing quantization gptq exl2-quants reinforcement-learning-from-human-feedback supercomputing trillion-parameter-models ghost-attention model-fine-tuning reward-models sanjiwatsuki superking__ mrdragonfox _dampf kaltcit rombodawg technotech
TheBloke's Discord community actively discusses Mixture of Experts (MoE) models, focusing on random gate routing layers for training and the challenges of immediate model use. There is a robust debate on quantization methods, comparing GPTQ and EXL2 quants, with EXL2 noted for faster execution on specialized hardware. A new model, Nous Hermes 2, based on Mixtral 8x7B and trained with RLHF, claims benchmark superiority but shows some inconsistencies. The Frontier supercomputer at Oak Ridge National Laboratory is highlighted for training a trillion-parameter LLM with 14TB RAM, sparking discussions on open-sourcing government-funded AI research. Additionally, the application of ghost attention in the academicat model is explored, with mixed reactions from the community. "Random gate layer is good for training but not for immediate use," and "EXL2 might offer faster execution on specialized hardware," are key insights shared.
1/4/2024: Jeff Bezos backs Perplexity's $520m Series B.
wizardcoder-33b-v1.1 mobilellama-1.4b-base shearedllama tinyllama mixtral-8x7b perplexity anthropic google nous-research mistral-ai hugging-face document-recall rnn-memory synthetic-data benchmarking multi-gpu-support context-length model-architecture sliding-window-attention model-parallelism gpu-optimization jeff-bezos
Perplexity announced their Series B funding round with notable investor Jeff Bezos, who previously invested in Google 25 years ago. Anthropic is raising $750 million, projecting at least $850 million in annualized revenue next year and implementing "brutal" changes to their Terms of Service. Discussions in Nous Research AI Discord cover topics such as document recall limits from gigabytes of data, RNN memory and compute trade-offs, synthetic datasets, and benchmarking of models like WizardCoder-33B-V1.1, MobileLLaMA-1.4B-Base, ShearedLLaMA, and TinyLLaMA. Other highlights include UnsLOTH optimizations for multi-GPU systems, AI rap voice models, context-extending code, and architectural innovations like applying Detectron/ViT backbones to LLMs, sliding window attention in Mistral, and parallelizing Mixtral 8x7b with FSDP and HF Accelerate.
12/12/2023: Towards LangChain 0.1
mixtral-8x7b phi-2 gpt-3 chatgpt gpt-4 langchain mistral-ai anthropic openai microsoft mixture-of-experts information-leakage prompt-engineering oauth2 logo-generation education-ai gaming-ai api-access model-maintainability scalability
The Langchain rearchitecture has been completed, splitting the repo for better maintainability and scalability, while remaining backwards compatible. Mistral launched a new Discord community, and Anthropic is rumored to be raising another $3 billion. On the OpenAI Discord, discussions covered information leakage in AI training, mixture of experts (MoE) models like mixtral 8x7b, advanced prompt engineering techniques, and issues with ChatGPT performance and API access. Users also explored AI applications in logo generation, education, and gaming, and shared solutions for Oauth2 authentication problems. A new small language model named Phi-2 was mentioned from Microsoft.
12/11/2023: Mixtral beats GPT3.5 and Llama2-70B
mixtral-8x7b gpt-4 gpt-3.5-turbo llama-3 openhermes-2.5 llava-v1.5-13b-gptq mistral-ai openai huggingface sparse-mixture-of-experts fine-tuning quantization gpu-hardware transformers model-deployment open-source coding-datasets
Mistral AI announced the Mixtral 8x7B model featuring a Sparse Mixture of Experts (SMoE) architecture, sparking discussions on its potential to rival GPT-4. The community debated GPU hardware options for training and fine-tuning transformer models, including RTX 4070s, A4500, RTX 3090s with nvlink, and A100 GPUs. Interest was expressed in fine-tuning Mixtral and generating quantized versions, alongside curating high-quality coding datasets. Resources shared include a YouTube video on open-source model deployment, an Arxiv paper, GitHub repositories, and a blog post on Mixture-of-Experts. Discussions also touched on potential open-source releases of GPT-3.5 Turbo and llama-3, and running OpenHermes 2.5 on Mac M3 Pro with VRAM considerations.