All tags
Model: "mistral-7b"
not much happened this weekend
jamba-1.5 dream-machine-1.5 ideogram-v2 mistral-nemo-minitron-8b mistral-7b llama-3-8b nous-research cursor-ai gdm george-hotz agibot unitree eth-zurich disney uc-san-diego ai21-labs luma-labs ideogram nvidia mistral-ai meta-ai-fair distributed-ai optimizer inter-gpu-communication low-latency-training open-source humanoid-robots robotics physics-based-motion teleoperation multilingual-models long-context text-to-video text-to-image model-performance george-hotz adcock_brett aman
Nous Research announced DisTrO, a new optimizer that drastically reduces inter-GPU communication by 1000x to 10,000x enabling efficient training on slow networks, offering an alternative to GDM's DiLoCo. Cursor AI gained viral attention from an 8-year-old user and announced a new fundraise, with co-host Aman returning to their podcast. George Hotz launched tinybox for sale. In robotics, AGIBOT revealed 5 new humanoid robots with open-source plans, and Unitree showcased its G1 humanoid robot nearing mass production at $16,000. ETH Zurich and Disney developed an AI system for physics-based robot motion generation from text or images. UC San Diego released ACE, an open-source teleoperation system for controlling multiple robots. AI21 Labs unveiled Jamba 1.5, a multilingual model with 256k context length and permissive licensing. Luma Labs released Dream Machine 1.5 for improved text-to-video generation. Ideogram launched v2 of its text-to-image model with near-perfect text generation. Nvidia and Mistral released Mistral-NeMo-Minitron 8B, a small model outperforming Mistral-7B and llama-3-8b on the Open LLM leaderboard.
Nvidia Minitron: LLM Pruning and Distillation updated for Llama 3.1
llama-3-1-8b llama-3-1 jamba-1.5 claude-3 dracarys-70b dracarys-72b mistral-nemo-minitron-8b mistral-7b nvidia meta-ai-fair ai21-labs anthropic hugging-face pruning knowledge-distillation weight-pruning activation-based-pruning width-pruning kl-divergence teacher-correction prompt-optimization multilinguality long-context mixture-of-experts model-fine-tuning
Nvidia and Meta researchers updated their Llama 3 results with a paper demonstrating the effectiveness of combining weight pruning and knowledge distillation to reduce training costs by training only the largest model from scratch and deriving smaller models via pruning and distillation. The process involves teacher correction, activation-based pruning (favoring width pruning), and retraining with distillation using KL Divergence loss, resulting in better-performing models at comparable sizes. However, distillation incurs some accuracy tradeoffs. Additionally, AI21 Labs launched Jamba 1.5, a hybrid SSM-Transformer MoE model with large context windows and multilingual support. Anthropic updated Claude 3 with LaTeX rendering and prompt caching. An open-source coding-focused LLM, Dracarys, was released in 70B and 72B sizes, showing improved coding performance. The Mistral Nemo Minitron 8B model outperforms Llama 3.1 8B and Mistral 7B on the Hugging Face leaderboard, highlighting pruning and distillation benefits. Research on prompt optimization reveals the complexity of prompt search spaces and the surprising effectiveness of simple algorithms like AutoPrompt/GCG.
DataComp-LM: the best open-data 7B model/benchmark/dataset
mistral-nemo-12b gpt-4o-mini deepseek-v2-0628 mistral-7b llama-3 gemma-2 qwen-2 datacomp hugging-face openai nvidia mistral-ai deepseek dataset-design scaling-laws model-benchmarking model-performance fine-tuning multilinguality function-calling context-windows open-source-models model-optimization cost-efficiency benchmarking sam-altman guillaume-lample philschmid miramurati
DataComp team released a competitive 7B open data language model trained on only 2.5T tokens from the massive DCLM-POOL dataset of 240 trillion tokens, showing superior scaling trends compared to FineWeb. OpenAI launched GPT-4o mini, a cost-effective model with 82% MMLU and performance near GPT-4-Turbo, aimed at developers for broad applications. NVIDIA and Mistral jointly released the Mistral NeMo 12B model featuring a 128k token context window, FP8 checkpoint, multilingual support, and Apache 2.0 licensing. DeepSeek announced DeepSeek-V2-0628 as the top open-source model on the LMSYS Chatbot Arena leaderboard with strong rankings in coding, math, and hard prompts. This news highlights advances in dataset design, model efficiency, and open-source contributions in the AI community.
Gemma 2 tops /r/LocalLlama vibe check
gemma-2-9b gemma-2-27b llama-3 mistral-7b phi-3 qwen gemma llamaindex mistral-ai cohere deepseek-ai nous-research eureka-labs model-comparison local-llms multilinguality model-efficiency fine-tuning ai-education ai-teaching-assistants andrej-karpathy
Gemma 2 (9B, 27B) is highlighted as a top-performing local LLM, praised for its speed, multilingual capabilities, and efficiency on consumer GPUs like the 2080ti. It outperforms models like Llama 3 and Mistral 7B in various tasks, including non-English text processing and reasoning. The community discussion on /r/LocalLlama reflects strong preference for Gemma 2, with 18 mentions, compared to 10 mentions for Llama 3 and 9 mentions for Mistral. Other models like Phi 3 and Qwen also received mentions but are considered surpassed by Gemma 2. Additionally, Andrej Karpathy announced the launch of Eureka Labs, an AI+Education startup aiming to create an AI-native school with AI Teaching Assistants, starting with the LLM101n course to teach AI training fundamentals. This initiative is seen as a significant development in AI education.
Microsoft AgentInstruct + Orca 3
mistral-7b orca-2.5 microsoft-research apple tencent hugging-face synthetic-data fine-tuning instruction-following transformers model-performance hallucination-detection dataset-quality flashattention mixture-of-experts philschmid sama bindureddy rohanpaul_ai zachtratar dair_ai
Microsoft Research released AgentInstruct, the third paper in its Orca series, introducing a generative teaching pipeline that produces 25.8 million synthetic instructions to fine-tune mistral-7b, achieving significant performance gains: +40% AGIEval, +19% MMLU, +54% GSM8K, +38% BBH, +45% AlpacaEval, and a 31.34% reduction in hallucinations. This synthetic data approach follows the success of FineWeb and Apple's Rephrasing research in improving dataset quality. Additionally, Tencent claims to have generated 1 billion diverse personas for synthetic data. On AI Twitter, notable discussions included a shooting incident at a Trump rally and recent ML research highlights such as FlashAttention-3, RankRAG, and Mixture of A Million Experts.
Evals: The Next Generation
gpt-4 gpt-5 gpt-3.5 phi-3 mistral-7b llama-3 scale-ai mistral-ai reka-ai openai moderna sanctuary-ai microsoft mit meta-ai-fair benchmarking data-contamination multimodality fine-tuning ai-regulation ai-safety ai-weapons neural-networks model-architecture model-training model-performance robotics activation-functions long-context sam-altman jim-fan
Scale AI highlighted issues with data contamination in benchmarks like MMLU and GSM8K, proposing a new benchmark where Mistral overfits and Phi-3 performs well. Reka released the VibeEval benchmark for multimodal models addressing multiple choice benchmark limitations. Sam Altman of OpenAI discussed GPT-4 as "dumb" and hinted at GPT-5 with AI agents as a major breakthrough. Researchers jailbroke GPT-3.5 via fine-tuning. Global calls emerged to ban AI-powered weapons, with US officials urging human control over nuclear arms. Ukraine launched an AI consular avatar, while Moderna partnered with OpenAI for medical AI advancements. Sanctuary AI and Microsoft collaborate on AI for general-purpose robots. MIT introduced Kolmogorov-Arnold networks with improved neural network efficiency. Meta AI is training Llama 3 models with over 400 billion parameters, featuring multimodality and longer context.
OpenAI's Instruction Hierarchy for the LLM OS
phi-3-mini openelm claude-3-opus gpt-4-turbo gpt-3.5-turbo llama-3-70b rho-1 mistral-7b llama-3-8b llama-3 openai microsoft apple deepseek mistral-ai llamaindex wendys prompt-injection alignment benchmarking instruction-following context-windows model-training model-deployment inference performance-optimization ai-application career-advice drive-thru-ai
OpenAI published a paper introducing the concept of privilege levels for LLMs to address prompt injection vulnerabilities, improving defenses by 20-30%. Microsoft released the lightweight Phi-3-mini model with 4K and 128K context lengths. Apple open-sourced the OpenELM language model family with an open training and inference framework. An instruction accuracy benchmark compared 12 models, with Claude 3 Opus, GPT-4 Turbo, and Llama 3 70B performing best. The Rho-1 method enables training state-of-the-art models using only 3% of tokens, boosting models like Mistral. Wendy's deployed AI-powered drive-thru ordering, and a study found Gen Z workers prefer generative AI for career advice. Tutorials on deploying Llama 3 models on AWS EC2 highlight hardware requirements and inference server use.
Llama-3-70b is GPT-4-level Open Model
llama-3-70b llama-3-8b llama-3 llama-2-70b mistral-7b grok-3 stable-diffusion-3 vasa-1 meta-ai-fair groq nvidia amazon microsoft benchmarking model-performance fine-tuning function-calling arithmetic image-generation video-generation energy-usage gpu-demand political-bias ai-safety scaling context-windows tokenization elon-musk
Meta has released Llama 3, their most capable open large language model with 8B and 70B parameter versions supporting 8K context length and outperforming previous models including Llama 2 and Mistral 7B. Groq serves the Llama 3 70B model at 500-800 tokens/second, making it the fastest GPT-4-level token source. Discussions highlight AI scaling challenges with Elon Musk stating that training Grok 3 will require 100,000 Nvidia H100 GPUs, and AWS planning to acquire 20,000 B200 GPUs for a 27 trillion parameter model. Microsoft unveiled VASA-1 for lifelike talking face generation, while Stable Diffusion 3 and its extensions received mixed impressions. Concerns about AI energy usage and political bias in AI were also discussed.
Cohere Command R+, Anthropic Claude Tool Use, OpenAI Finetuning
c4ai-command-r-plus claude-3 gpt-3.5-turbo gemini mistral-7b gemma-2 claude-3-5 llama-3 vicuna cohere anthropic openai microsoft stability-ai opera-software meta-ai-fair google-deepmind mistral-ai tool-use multilingual-models rag fine-tuning quantum-computing audio-generation local-inference context-windows model-size-analysis model-comparison
Cohere launched Command R+, a 104B dense model with 128k context length focusing on RAG, tool-use, and multilingual capabilities across 10 key languages. It supports Multi-Step Tool use and offers open weights for research. Anthropic introduced tool use in beta for Claude, supporting over 250 tools with new cookbooks for practical applications. OpenAI enhanced its fine-tuning API with new upgrades and case studies from Indeed, SK Telecom, and Harvey, promoting DIY fine-tuning and custom model training. Microsoft achieved a quantum computing breakthrough with an 800x error rate improvement and the most usable qubits to date. Stability AI released Stable Audio 2.0, improving audio generation quality and control. The Opera browser added local inference support for large language models like Meta's Llama, Google's Gemma, and Vicuna. Discussions on Reddit highlighted Gemini's large context window, analysis of GPT-3.5-Turbo model size, and a battle simulation between Claude 3 and ChatGPT using local 7B models like Mistral and Gemma.
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.
Claude 3 is officially America's Next Top Model
claude-3-opus claude-3-sonnet claude-3-haiku gpt-4o-mini mistral-7b qwen-72b anthropic mistral-ai huggingface openrouter stable-diffusion automatic1111 comfyui fine-tuning model-merging alignment ai-ethics benchmarking model-performance long-context cost-efficiency model-evaluation mark_riedl ethanjperez stuhlmueller ylecun aravsrinivas
Claude 3 Opus outperforms GPT4T and Mistral Large in blind Elo rankings, with Claude 3 Haiku marking a new cost-performance frontier. Fine-tuning techniques like QLoRA on Mistral 7B and evolutionary model merging on HuggingFace models are highlighted. Public opinion shows strong opposition to ASI development. Research supervision opportunities in AI alignment are announced. The Stable Diffusion 3 (SD3) release raises workflow concerns for tools like ComfyUI and automatic1111. Opus shows a 5% performance dip on OpenRouter compared to the Anthropic API. A new benchmark stresses LLM recall at long contexts, with Mistral 7B struggling and Qwen 72b performing well.
not much happened today
llama-2-70b llama-2-7b mistral-7b qwen-1.5 llava microsoft mistral-ai ollama fine-tuning synthetic-data retrieval-augmented-generation embeddings hardware-optimization performance-benchmarks model-memory multimodality
The Reddit community /r/LocalLlama discusses fine-tuning and training LLMs, including tutorials and questions on training models with specific data like dictionaries and synthetic datasets with 25B+ tokens. Users explore retrieval-augmented generation (RAG) challenges with models like mistral-7b and embedding generation for EEG brain activity. Discussions include hardware optimization for running llama-2-70b locally under budget constraints, and performance benchmarks for qwen-1.5 models. There is interest in extending LLM capabilities, such as converting llama-2-7b into a vision-capable model like llava and improving model memory for longer context retention.
Welcome Interconnects and OpenRouter
mistral-large miqu mixtral gpt-4 mistral-7b mistral-ai openai perplexity-ai llamaindex qwen langchain model-comparison model-optimization quantization role-playing story-writing code-clarity ai-assisted-decompilation asynchronous-processing quantum-computing encoder-based-diffusion open-source hardware-experimentation rag-systems nathan-lambert alex-atallah
Discord communities analyzed 22 guilds, 349 channels, and 12885 messages revealing active discussions on model comparisons and optimizations involving Mistral AI, Miqu, and GGUF quantized models. Highlights include comparing Mistral Large with GPT-4, focusing on cost-effectiveness and performance, and exploring quantization techniques like GPTQ and QLORA to reduce VRAM usage. Advanced applications such as role-playing, story-writing, code clarity, and AI-assisted decompilation were emphasized, alongside development of tools like an asynchronous summarization script for Mistral 7b. The intersection of quantum computing and AI was discussed, including DARPA-funded projects and encoder-based diffusion techniques for image processing. Community efforts featured new Spanish LLM announcements, hardware experimentation, and open-source initiatives, with platforms like Perplexity AI and LlamaIndex noted for innovation and integration. Speculation about Mistral AI's open-source commitment and tools like R2R for rapid RAG deployment highlighted collaborative spirit.
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.
Sora pushes SOTA
gemini-1.5 sora h20-gpt mistral-7b llama-13b mistralcasualml mixtral-instruct yi-models openai google-deepmind nvidia mistral-ai h2oai multimodality gpu-power-management long-context model-merging fine-tuning retrieval-augmented-generation role-play-model-optimization cross-language-integration training-loss synthetic-data-generation coding-support
Discord communities analyzed over 20 guilds, 312 channels, and 10550 messages reveal intense discussions on AI developments. Key highlights include the Dungeon Master AI assistant for Dungeons and Dragons using models like H20 GPT, GPU power supply debates involving 3090 and 3060 GPUs, and excitement around Google's Gemini 1.5 with its 1 million token context window and OpenAI's Sora model. Challenges with large world models (LWM) multimodality, GPT-assisted coding, and role-play model optimization with Yi models and Mixtral Instruct were discussed. Technical issues like model merging errors with MistralCasualML, fine-tuning scripts like AutoFineTune, and cross-language engineering via JSPyBridge were also prominent. NVIDIA's Chat with RTX feature leveraging retrieval-augmented generation (RAG) on 30+ series GPUs was compared to LMStudio's support for Mistral 7b and Llama 13b models. The community is cautiously optimistic about these frontier models' applications in media and coding.
AI gets Memory
miqumaid-v2-70b mixtral-8x7b-qlora mistral-7b phi-2 medalpaca aya openai langchain thebloke cohere unsloth-ai mistral-ai microsoft rag memory-modeling context-windows open-source finetuning sequential-fine-tuning direct-preference-optimization rlhf ppo javascript-python-integration hardware-optimization gpu-overclocking quantization model-training large-context multilinguality joanne-jang
AI Discords analysis covered 20 guilds, 312 channels, and 6901 messages. The report highlights the divergence of RAG style operations for context and memory, with implementations like MemGPT rolling out in ChatGPT and LangChain. The TheBloke Discord discussed open-source large language models such as the Large World Model with contexts up to 1 million tokens, and the Cohere aya model supporting 101 languages. Roleplay-focused models like MiquMaid-v2-70B were noted for performance improvements with enhanced hardware. Finetuning techniques like Sequential Fine-Tuning (SFT) and Direct Preference Optimization (DPO) were explained, with tools like Unsloth AI's apply_chat_template preferred over Alpaca. Integration of JavaScript and Python via JSPyBridge in the SillyTavern project was also discussed. Training challenges with Mixtral 8x7b qlora versus Mistral 7b were noted. The LM Studio Discord focused on hardware limitations affecting large model loading, medical LLMs like medAlpaca, and hardware discussions around GPU upgrades and overclocking. Anticipation for IQ3_XSS 1.5 bit quantization support in LM Studio was expressed.
The Dissection of Smaug (72B)
smaug-72b qwen-1.0 qwen-1.5 gpt-4 mistral-7b miqumaid wizardlm_evol_instruct_v2_196k openhermes-2.5 abacus-ai hugging-face nous-research laion thebloke lm-studio intel nvidia elevenlabs fine-tuning model-merging quantization web-ui model-conversion hardware-setup privacy image-generation optical-character-recognition prompt-engineering bindureddy
Abacus AI launched Smaug 72B, a large finetune of Qwen 1.0, which remains unchallenged on the Hugging Face Open LLM Leaderboard despite skepticism from Nous Research. LAION introduced a local voice assistant model named Bud-E with a notable demo. The TheBloke Discord community discussed model performance trade-offs between large models like GPT-4 and smaller quantized models, fine-tuning techniques using datasets like WizardLM_evol_instruct_V2_196k and OpenHermes-2.5, and challenges in web UI development and model merging involving Mistral-7b and MiquMaid. The LM Studio Discord highlighted issues with model conversion from PyTorch to gguf, hardware setups involving Intel Xeon CPUs and Nvidia P40 GPUs, privacy concerns, and limitations in image generation and web UI availability.
Qwen 1.5 Released
qwen-1.5 mistral-7b sparsetral-16x7b-v2 bagel-7b-v0.4 deepseek-math-7b-instruct deepseek qwen mistral-ai hugging-face meta-ai-fair quantization token-context multilinguality retrieval-augmented-generation agent-planning code-generation sparse-moe model-merging fine-tuning direct-preference-optimization character-generation ascii-art kanji-generation vr retinal-resolution light-field-passthrough frozen-networks normalization-layers
Chinese AI models Yi, Deepseek, and Qwen are gaining attention for strong performance, with Qwen 1.5 offering up to 32k token context and compatibility with Hugging Face transformers and quantized models. The TheBloke Discord discussed topics like quantization of a 70B LLM, the introduction of the Sparse MoE model Sparsetral based on Mistral, debates on merging vs fine-tuning, and Direct Preference Optimization (DPO) for character generation. The Nous Research AI Discord covered challenges in Japanese Kanji generation, AI scams on social media, and Meta's VR headset prototypes showcased at SIGGRAPH 2023. Discussions also included fine-tuning frozen networks and new models like bagel-7b-v0.4, DeepSeek-Math-7b-instruct, and Sparsetral-16x7B-v2.
CodeLLama 70B beats GPT4 on HumanEval
codellama miqu mistral-medium llama-2-70b aphrodite-engine mixtral flatdolphinmaid noromaid rpcal chatml mistral-7b activation-beacon eagle-7b rwkv-v5 openhermes2.5 nous-hermes-2-mixtral-8x7b-dpo imp-v1-3b bakllava moondream qwen-vl meta-ai-fair ollama nous-research mistral-ai hugging-face ai-ethics alignment gpu-optimization direct-prompt-optimization fine-tuning cuda-programming optimizer-technology quantization multimodality context-length dense-retrieval retrieval-augmented-generation multilinguality model-performance open-source code-generation classification vision
Meta AI surprised the community with the release of CodeLlama, an open-source model now available on platforms like Ollama and MLX for local use. The Miqu model sparked debate over its origins, possibly linked to Mistral Medium or a fine-tuned Llama-2-70b, alongside discussions on AI ethics and alignment risks. The Aphrodite engine showed strong performance on A6000 GPUs with specific configurations. Role-playing AI models such as Mixtral and Flatdolphinmaid faced challenges with repetitiveness, while Noromaid and Rpcal performed better, with ChatML and DPO recommended for improved responses. Learning resources like fast.ai's course were highlighted for ML/DL beginners, and fine-tuning techniques with optimizers like Paged 8bit lion and adafactor were discussed.
At Nous Research AI, the Activation Beacon project introduced a method for unlimited context length in LLMs using "global state" tokens, potentially transforming retrieval-augmented models. The Eagle-7B model, based on RWKV-v5, outperformed Mistral in benchmarks with efficiency and multilingual capabilities. OpenHermes2.5 was recommended for consumer hardware due to its quantization methods. Multimodal and domain-specific models like IMP v1-3b, Bakllava, Moondream, and Qwen-vl were explored for classification and vision-language tasks. The community emphasized centralizing AI resources for collaborative research.
RWKV "Eagle" v5: Your move, Mamba
rwkv-v5 mistral-7b miqu-1-70b mistral-medium llama-2 mistral-instruct-v0.2 mistral-tuna llama-2-13b kunoichi-dpo-v2-7b gpt-4 eleutherai mistral-ai hugging-face llamaindex nous-research rwkv lmsys fine-tuning multilinguality rotary-position-embedding model-optimization model-performance quantization speed-optimization prompt-engineering model-benchmarking reinforcement-learning andrej-karpathy
RWKV v5 Eagle was released with better-than-mistral-7b evaluation results, trading some English performance for multilingual capabilities. The mysterious miqu-1-70b model sparked debate about its origins, possibly a leak or distillation of Mistral Medium or a fine-tuned Llama 2. Discussions highlighted fine-tuning techniques, including the effectiveness of 1,000 high-quality prompts over larger mixed-quality datasets, and tools like Deepspeed, Axolotl, and QLoRA. The Nous Research AI community emphasized the impact of Rotary Position Embedding (RoPE) theta settings on LLM extrapolation, improving models like Mistral Instruct v0.2. Speed improvements in Mistral Tuna kernels reduced token processing costs, enhancing efficiency. The launch of Eagle 7B with 7.52B parameters showcased strong multilingual performance, surpassing other 7B class models.
Google Solves Text to Video
mistral-7b llava google-research amazon-science huggingface mistral-ai together-ai text-to-video inpainting space-time-diffusion code-evaluation fine-tuning inference gpu-rentals multimodality api model-integration learning-rates
Google Research introduced Lumiere, a text-to-video model featuring advanced inpainting capabilities using a Space-Time diffusion process, surpassing previous models like Pika and Runway. Manveer from UseScholar.org compiled a comprehensive list of code evaluation benchmarks beyond HumanEval, including datasets from Amazon Science, Hugging Face, and others. Discord communities such as TheBloke discussed topics including running Mistral-7B via API, GPU rentals, and multimodal model integration with LLava. Nous Research AI highlighted learning rate strategies for LLM fine-tuning, issues with inference, and benchmarks like HumanEval and MBPP. RestGPT gained attention for controlling applications via RESTful APIs, showcasing LLM application capabilities.
Nightshade poisons AI art... kinda?
mistral-7b falcon-7b mistral-ai hugging-face mixture-of-experts gpu-parallelism quantization fine-tuning model-merging ai-detection role-playing benchmarking
Over the weekend of 1/19-20/2024, discussions in TheBloke Discord covered key topics including Mixture of Experts (MoE) model efficiency, GPU parallelism, and quantization strategies. Users debated the effectiveness of AI detection tools like GPTZero and explored fine-tuning challenges with models such as Mistral 7B and Falcon 7B. Community interest was strong in developing simpler, community-powered quantization services and understanding model merging techniques. Ethical considerations around AI applications like AI girlfriend sites were also discussed.
1/17/2024: Help crowdsource function calling datasets
mistral-7b dolphin-2.7-mixtral-8x7b mega-dolphin dolphin-2.6-mistral-7b-dpo llama-cpp lm-studio mistral-ai microsoft hugging-face apple function-calling quantization model-performance gpu-optimization model-selection closed-source memory-optimization linux-server api-fees headless-mode yagilb heyitsyorkie
LM Studio updated its FAQ clarifying its closed-source status and perpetual freeness for personal use with no data collection. The new beta release includes fixes and hints at upcoming 2-bit quantization support. For gaming, models like Dolphin 2.7 Mixtral 8x7B, MegaDolphin, and Dolphin 2.6 Mistral 7B DPO with Q4_K_M quantization were recommended. Discussions highlighted that single powerful GPUs outperform multi-GPU setups due to bottlenecks, with older GPUs like Tesla P40 being cost-effective. Microsoft's AutoGen Studio was introduced but has issues and requires API fees for open-source models. Linux users are advised to use llama.cpp over LM Studio due to lack of headless mode. Additional tools like LLMFarm for iOS and various Hugging Face repositories were also mentioned. "LM Studio must be running to use the local inference server as there is no headless mode available" and "matching model size to GPU memory is key for performance" were notable points.
12/31/2023: Happy New Year
mistral-7b mixtral lm-studio mistral-ai hugging-face amd fine-tuning hardware-optimization vram emotional-intelligence model-deployment integration gpu-optimization software-updates
LM Studio community discussions highlight variations and optimizations in Dolphin and Mistral 7b models, focusing on hardware-software configurations and GPU vRAM impact on processing speed. Challenges with Mixtral model deployment on local machines and workarounds for downloading models from HuggingFace in restricted regions were addressed. Users explored enhancing AI's emotional intelligence and personalities through extended prompts, referencing research on emotional stimuli in large language models. The community also discussed hardware setups for budget AI compute servers, integration issues with ChromaDB and Autogen, and shared positive feedback on LM Studio's usability and UI. Celebrations for the New Year added a social touch to the guild interactions.
12/23/2023: NeurIPS Best Papers of 2023
gpt-4 palm2 hermes-2.5 mistral-7b nous-research hugging-face apple context-length malware-security video-content music-content linear-layers api-access large-language-models embedding vector-databases model-merging model-interpretability striped-hyena-architecture quantization rmsnorm attention-mechanisms
The Latent Space Pod released a 3-hour recap of the best NeurIPS 2023 papers. The Nous Research AI Discord community discussed optimizing AI performance with shorter context lengths, malware security concerns linked to HuggingFace, and shared insights on video and music content. Technical discussions included the DYAD research paper proposing a faster alternative to linear layers, Apple's ML Ferret machine learning tool, and accessing PALM2 via API. The community also explored Large Language Models focusing on specialized models, data scaling, embedding/vector databases, model merging, and interpretability, with mentions of Hermes 2.5, GPT-4, and Mistral. Additionally, there were conversations on the Striped Hyena Architecture, quantization challenges, and fixes related to RMSNorm and the "Attention is All You Need" paper.
12/13/2023 SOLAR10.7B upstages Mistral7B?
solar-10.7b llama-2 mistral-7b phi-2 gpt-4 gemini upstage nous-research openai mistral-ai microsoft depth-up-scaling pretraining synthetic-data gpu-training api-usage model-integration agi asi chat-models vision model-performance fine-tuning
Upstage released the SOLAR-10.7B model, which uses a novel Depth Up-Scaling technique built on the llama-2 architecture and integrates mistral-7b weights, followed by continued pre-training. The Nous community finds it promising but not exceptional. Additionally, weights for the phi-2 base model were released, trained on 1.4 trillion tokens including synthetic texts created by GPT-3 and filtered by GPT-4, using 96 A100 GPUs over 14 days. On OpenAI's Discord, users discussed challenges with various GPT models, including incoherent outputs, API usage limitations, and issues with GPT-4 Vision API. Conversations also covered understanding AGI and ASI, concerns about OpenAI's partnership with Axel Springer, and pricing changes for GPT Plus. Discussions included the Gemini chat model integrated into Bard and comparisons with GPT-4 performance.
12/10/2023: not much happened today
mixtral-8x7b-32kseqlen mistral-7b stablelm-zephyr-3b openhermes-2.5-neural-chat-v3-3-slerp gpt-3.5 gpt-4 nous-research openai mistral-ai hugging-face ollama lm-studio fine-tuning mixture-of-experts model-benchmarking inference-optimization model-evaluation open-source decentralized-ai gpu-optimization community-engagement andrej-karpathy yann-lecun richard-blythman gabriel-syme pradeep1148 cyborg_1552
Nous Research AI Discord community discussed attending NeurIPS and organizing future AI events in Australia. Highlights include interest in open-source and decentralized AI projects, with Richard Blythman seeking co-founders. Users shared projects like Photo GPT AI and introduced StableLM Zephyr 3B. The Mixtral model, based on Mistral, sparked debate on performance and GPU requirements, with comparisons to GPT-3.5 and potential competitiveness with GPT-4 after fine-tuning. Tools like Tensorboard, Wandb, and Llamahub were noted for fine-tuning and evaluation. Discussions covered Mixture of Experts (MoE) architectures, fine-tuning with limited data, and inference optimization strategies for ChatGPT. Memes and community interactions referenced AI figures like Andrej Karpathy and Yann LeCun. The community also shared resources such as GitHub links and YouTube videos related to these models and tools.