Jinja Formatting Up until now, all the formatting in prompts has been done using Python f-strings But the template_format abstraction is pretty flexible, and @benderville easily extended it to support Jinja, a more advanced formatter Docs: https://
langchain.readthedocs.io/en/latest/exam
ples/prompts/prompt_management.html#Alternative-formats
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OPEN SOURCE
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LangChain Extends Prompt Formatting with Jinja Support
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LangChain v0.0.42 Release: Documentation, Formatting, and Caching Updates
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LangChain v0.0.42 Doc fixes by @YouSearchEngine team, @_abi_ Jinja formatting – @benderville Max Marginal Relevance example selector (based on paper by @xiye_nlp Caching improvements (h/t @devonbrackbill for idea) These are sneaky some awesome updates, lets
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spaCy’s Hashing Approach for Word Representations Learning
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This is almost what we do in spaCy — it's just that, instead of reserving the first 9900 rows for common words, we just use the hashing. Given that the assignment of words to shared rows is arbitrary, how can the model learn good representations?
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spaCy Trained Pipelines: Domain Flexibility and Fine-tuning Capabilities
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But for spaCy, we want to give people trained pipelines and have them be as applicable as possible across different domains. We also want people to be able to fine-tune the models on their data if it's not working ideally.
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The Hashing Trick in spaCy Models: An Underexplored Technique
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The hashing trick is an old technique from sparse linear models, well known in toolkits like Vowpal Wabbit. It's one of the unusual things I did in @spacy_io 's models that I've always felt was quite neat, and needed more experiments and write up.
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Petals Creates Free Distributed Network for Running Text-Generating AI
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Cool article from @Kyle_L_Wiggers @TechCrunch about Petals by @BigscienceW https://
techcrunch.com/2022/12/20/pet
als-is-creating-a-free-distributed-network-for-running-text-generating-ai
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Microsoft Data Science For Beginners Learning Resource
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6) DS for begineers https://
github.com/microsoft/Data
-Science-For-Beginners
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BLOOM+1 Performance on Multilingual Prompting Benchmarks
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I wonder how well BLOOM+1 would do on the multilingual prompting benchmarks proposed here:
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nbdev writeup by Carson Dial appreciated
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Many thanks to @carsondial for the writeup about nbdev 🙂