Inexpensive token generation and agentic workflows for large language models (LLMs) open up intriguing new possibilities for training LLMs on synthetic data. Pretraining an LLM on its own directly generated responses to prompts doesn't help. But if an agentic workflow implemented
LLMS
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Managing Special Tokens in AI Models: Guidance Gaps
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Has anyone seen published guidance about how to avoid tokens (including special things like [INST] or ) messing things up, for any of the available models? I've not seen good documentation on this myself yet
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LangSmith Achieves 30% Accuracy Improvement Without Prompt Engineering
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How Dosu used LangSmith to achieve a 30% accuracy improvement with no prompt engineering One of the goals of LangSmith is to help teams set up a data flywheel. By capturing LLM outputs alongside user feedback, we can help developers automatically use that feedback to improve
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Daily Content on Python, Data Science, and Machine Learning
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That's a wrap! If you are interested in any of these below topics: – Python – Data Science – Machine Learning – Data Analysis – LLMs – MLOps Find me → @Sumanth_077 I'm sharing daily content over here.
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Jamba-Instruct Features Largest 256K Context Window in Size Class
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(4/4) With a 256K context window, Jamba-Instruct boasts the largest context window in its size class.
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Jamba-Instruct Performance on Long-Context Question Answering Benchmarks
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(3/4) Jamba-Instruct results on long-context QA benchmarks, conducted using the same method outlined in section 5.2.2 of our Jamba base model whitepaper.
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Jamba-Instruct Outperforms Competitors on Performance Benchmarks
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(2/4) Jamba-Instruct outperforms or rivals other instruction-tuned competitors across common performance benchmarks.
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AI21 Labs Releases Jamba-Instruct Enterprise Model
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We just released Jamba-Instruct! Built from our groundbreaking SSM-Transformer Jamba architecture, Jamba-Instruct brings the same technological innovation to the enterprise via an aligned model. With leading quality benchmarks, a 256K context window, and the most competitive
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Mistral Large and Mixtral 8x22B Performance on GSM1K Dataset
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Nice paper. Very good performance by Mistral Large and Mixtral 8x22B on the new GSM1K dataset! Results from Table D (plotted in Figure below) are also a good reminder that models are very sensitive to prompt. In general, it is also good to use sampling & majority voting to
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Host Your Project on Hugging Face Spaces with Free LLM
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Looks awesome Host in a HF Space and we’ll provide the LLM!!