Learn more here: https://
cerebras.ai/press-release/
cerebras-demonstrates-trillion-parameter-model-training-on-a-single-cs-3-system
… https://
cerebras.ai/blog/introduci
ng-gigagpt-gpt-3-sized-models-in-565-lines-of-code
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CODE
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Cerebras Trains Trillion-Parameter Models on Single CS-3
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Cerebras-GPT vs Megatron: Simplifying GPU Training Architecture
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Nvidia is very proud of Megatron – it lets you train across thousands of GPUs! But it's 20K lines of code to manage the cluster. Cerebras-GPT is 500 lines of code. One block of memory. One logical accelerator. No distributed computing. Everyone who's used it calls it magic.
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Linear Scaling AI Model Training Across Multiple Systems
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After the single box ran, we scaled it to 2 and 16 systems with linear scaling. There were no model & code changes required.
Megatron
DeepSpeed
Sharding The model lives on a single block of memory. No need to break it up. The whole thing trains like one giant GPU. -
DuckDB 1.0: High-Speed Analytics Engine for AI Workflows
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#DuckDB Redefining Analytics for #AI 100x faster Parquet queries vs. Pandas Process billions of rows in seconds In-process #SQL <10MB RAM for setup Integrated #Python/#R for #ML workflows Version 1.0 stability + @MotherDuckHQ backing #DataAnalytics #TechTrends
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Llama 3.3 70B Now Available on SambaNova Cloud
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🚨 Llama 3.3 70B, now available on SambaNova Cloud! @AIatMeta
— SambaNova (@SambaNovaAI) 11 décembre 2024
✅ Context length of 64K tokens
✅ Perfect for building Agents with other models
✅ Need higher rate limits? Just ask us
Try it out 👇Llama 3.3 70B, now available on SambaNova Cloud! @AIatMeta Context length of 64K tokens Perfect for building Agents with other models Need higher rate limits? Just ask us Try it out
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ContextCite: Tracing AI Sources and Detecting Hallucinations
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If the AI generates an inaccurate fact, users can trace the error back to its original source & understand the model’s reasoning. If the AI hallucinates an answer, ContextCite can indicate that the information didn’t come from any real source at all.
— MIT CSAIL (@MIT_CSAIL) 11 décembre 2024
The tool could be valuable… pic.twitter.com/D8ViuL2cjXIf the AI generates an inaccurate fact, users can trace the error back to its original source & understand the model’s reasoning. If the AI hallucinates an answer, ContextCite can indicate that the information didn’t come from any real source at all. The tool could be valuable
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MIT ContextCite tool improves chatbot answer reliability verification
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How can we really know if a chatbot is giving a reliable answer? MIT CSAIL’s "ContextCite" tool can ID the parts of external context used to generate any particular statement from a language model, improving trust by helping users easily verify the statement:
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Disable Agent Messages in FlowiseAI ChatEmbed Configuration
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You can turn it off via showAgentMessages – https://
github.com/FlowiseAI/Flow
iseChatEmbed?tab=readme-ov-file#configuration
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IBM Watsonx Integration Guide for Flowise Platform
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To use IBM Watsonx within Flowise:
— FlowiseAI (@FlowiseAI) 11 décembre 2024
1. Create an account on Watsonx
2. Create a project, and select the model
3. Create API Key on IBM Cloud Console
4. Grab the URL, Project ID, and Model Name
Step by step guide: https://t.co/Q6VOqxzcv7
Thanks @_Eduard26 for contributions! pic.twitter.com/U8IsrHLm7kTo use IBM Watsonx within Flowise: 1. Create an account on Watsonx
2. Create a project, and select the model
3. Create API Key on IBM Cloud Console
4. Grab the URL, Project ID, and Model Name Step by step guide: https://
docs.flowiseai.com/integrations/l
angchain/chat-models/ibm-watsonx
… Thanks @_Eduard26 for contributions!