Preliminary testing on my agent benchmark (based on https://
github.com/aymeric-rouche
r/benchmark_agents
…): Llama3-70B-Instruct is on par with GPT4! cc @lvwerra
LLMS
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Llama3-70B-Instruct matches GPT4 in agent benchmark test
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Setting Up Query Engine with LLM Integration
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& Setting up a query engine The query engine takes a query string & use it to fetch relevant context and then sends them both as a prompt to the LLM to generate a final natural language response. Here's how you set it up:
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Cost analysis of training 8B and 70B language models on A100
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Napkin math here is 1 A100 hour atm is ~$1 on cloud providers, so roughly 1.3M hours for 8B (see model card) would mean $1.3M. And $6.4M for 70B. Keeping in mind that this is just the approx cost to hit go and wait and assuming a perfect run. And that it takes quite a bit more in
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Synthetic Text Models Unsuitable for Accuracy-Critical Applications
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A synthetic text extruding machine is not well-matched to any application where the accuracy of the content matters. This is clearly one such application. >>
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Llama3 Autotraining Now Available on NVIDIA DGX Cloud
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You can already autotrain (no code needed) Llama3 on @nvidia DGX cloud thanks to our new integration
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Open-Source AI Models Released Rapidly in Spring 2024
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DBRX, Mixtral 8x22B, Llama-3 all released within weeks. The open-source AI scene shows no sign of slowing down.
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Llama3 Now Available on Major Cloud AI Platforms
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Not everyone is aware but the fact that llama3 has been added to HF on day one means that right away, you can easily run it on @aws Sagemaker, @microsoft AzureML, @googlecloud Vertex and the @huggingface hosted solutions (autotrain, spaces, serverless and dedicated endpoints).
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Llama 3 Now Available on Perplexity Labs and API
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We're excited to announce that Llama 3 is available on Perplexity Labs and our API. Kudos to the team @AIatMeta for all of the hard work they put into this release. We can't wait to see what you build with it. Try it free at http://
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LLM Security: Don’t Include Sensitive Data in Prompts
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The examples are interesting but many of them illustrate scenarios that I would never consider implementing – if you don't want information to be available to your users, your first priority should be not to include that information in an LLM prompt in the first place!
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CyberSecEval2: Exploring Prompt Injection Attack Examples
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CyberSecEval2 includes an interesting collection of example prompt injection attacks – it's JSON on GitHub which means you can browse them in Datasette Lite like this: https://
lite.datasette.io/?json=https://
github.com/meta-llama/PurpleLlama/blob/main/CybersecurityBenchmarks/datasets/prompt_injection/prompt_injection.json#/data/prompt_injection?_filter_column_1=&_filter_op_1=notlike&_filter_value_1=secret+key&_filter_column=&_filter_op=exact&_filter_value=&_sort=rowid&_facet=injection_variant&_facet=injection_type&_facet=risk_category
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