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Scientific insights: the significance of refund information

Amazing guide! Was trying to format llama-3-8B for a while and this provides so much depth to help me understand how to go about it.

Aha caught the Silicon valley reference: Hot dog...not hot dog!!! Great explanation btw! Thanks

I've diversified our crypto holdings by adding $Gala and $LDO Both are representing 1.95% of the total portfolio.

There is exact same video with same words and dialogue by a different guy... I thought this will be different..

this guy looks like AI)

Great explanation connor. Loved it.

I'm trying to find a tutorial on how to run wordcraft? Can you tell me how?

wish be helpful if you speak slower😂

🎯 Key points for quick navigation: 00:00 *🧠 DSPy combines a new syntax with optimization for better AI control.* 02:26 *🛠️ DSPy offers clean and structured input/output fields for consistent syntax in LLM programs.* 04:47 *🔄 In DSPy, you can define the forward pass of LLM programs with control flow elements like loops and conditionals.* 12:26 *📏 DSPy Assertions allow defining rules and suggestions for how LLM models should behave.* 14:03 *🚀 DSPy optimizes instructions and examples in LLM programs for better performance.* 16:19 *🔍 In DSPy, inductive biases and depth control are inspired by PyTorch for designing LLM programs.* 17:43 *🔑 DSPy allows optimizing intermediate steps of LLM programs by focusing on final output instead of each layer's supervision.* 19:35 *🛠️ DSPy Compiler automates data labeling and example generation to improve LLM program optimization.* 21:01 *🧠 New language models are being developed frequently, requiring programs to keep up for optimal performance.* 21:41 *🔄 DSP optimizes initial task descriptions into more detailed signatures to improve overall task performance.* 23:17 *📚 Using generative models allows for creating training data without large human-labeled datasets, changing the landscape of data collection.* 30:07 *📏 Metrics are essential in evaluating the quality of synthetic examples, with options like exact match and F1 score.* 33:33 *🧩 DPy programs involve multiple components optimizing LLMs for complex tasks, offering impressive overall system behavior.* 37:18 *📊 DPy facilitates setting up and configuring models using available datasets, demonstrating the paradigm shift towards minimal training examples.* 41:13 *🧠 Adding explanations and intermediate reasoning helps improve model performance.* 42:38 *📊 DSPy retrievers expect a list of strings as input for querying.* 43:07 *🏗️ Writing a RAG program involves initializing components, retrieving, generating answers, and adding reasoning to prompts.* 44:32 *💬 Running inference in DSPy involves passing input questions to the model.* 48:16 *🔄 Multi-hop search in DSPy involves looping through context, generating queries, retrieving passages, and deduplicating contexts.* 49:25 *⚙️ DSPy allows for supervision on intermediate hops in multi-hop search, providing depth to queries.* 51:28 *🎯 DSPy saves time by adding Chain of Thought prompting without needing to manually write out rationales.* Made with HARPA AI

All that zooming in and out is super distracting and nausea inducing. Please never do another video with that. Choose a zoom level and keep it throughout the video.

😊😊

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Hey Connor, is there any place to learn Go based on what AlphaGo does? Can it teach?

Very nice, thank you. Only the zooming and movement is kind of dizzyish and way to fast sometimes so I can't follow anything as you can't read or know where you are going. I think with the large pointer is enough to drive attention to it. Anyway, great vid and thanks again for the intro to DSPy.

What we can do to have bigger answers? I want it to generate code, but after executing it gives me 4 lines of code Someone have some idea?

Thank you. 😀

Dude you mentioned weviate twice and didn’t even mention what the retriever does. If you want to do promotion, dedicate a segment to it. It will come across more professional and sincere without confusing viewers.

@3:46, how can low reach to high with search action?

🎯 Key points for quick navigation: Exciting new framework Programming model optimization Graph computation programs 09:55 *Signature, dock string* 10:10 *Prompt optimization, syntax* 10:38 *Input, output fields* 10:52 *Control flow, loops* 11:20 *UAPI, web queries* 12:26 *DSPy Assertions, suggestions* 13:49 *Citation attribution suggestions* 14:16 *Optimization, instructions, examples* 14:59 *DpY as PyTorch* 16:31 *Inductive biases, depth* 17:43 *Intermediate supervision, DpY compiler* 19:22 *Testing with programs* 19:35 *Optimizing instructions and examples* 20:03 *Automatic data labeling* 20:46 *Ending manual prompts* 21:14 *Adapting to new models* 22:49 *Structured output with prompts* 25:33 *Fine-tuning neural networks* 26:26 *Using few-shot examples* 27:51 *Bootstrapping rationales* 28:19 *Evaluating synthetic examples* Overlapping keywords metrics LM judge prompt LM produce metric 37:58 *Deep learning paradigm shift* 38:41 *Data set formatting* 40:03 *Inspect intermediate outputs* 40:59 *Add Chain of Thought* 43:35 *Define optimization metric* 45:13 *Value of reasoning* 45:28 *Inspect parameters* 46:25 *Multi-hop search integration* Queries connected to final answer Introduction to multi-hop search Supervision on intermediate hops Made with HARPA AI

The recipe is gone

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👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿😁😁👏🏿💯💯💯💯💯

short and sweet, gets to the point. marvelous video

Hi Connor, thanks for the awesome content. I have one small suggestion - Instead of covering maximum information, if it was topic by topic it would be more better. Example: In depth Information on 1 topic "Optimizers (formerly Teleprompters)". Thank you🙂

😁😁😁😁😁😁 0:10 0:11 0:11 👀 0:13 0:13 0:13 👀👏🏿👏🏿👏🏿👏🏿👏🏿👏🏿Educational

Is this notebook shared somewhere?

🎉🎉🎉🎉🎉🎉🎉🎉😊

😊😊😊😊😊🎉🎉🎉🎉

Back on it again

Do you laugh when you go in small tangents?

lets gooo 🎉🎉🎉

Hey Connor, this is great content. Thanks for posting it.

zooming in and out is distracting

I tried the implementation but i keep getting the error "model not found"

great intro, and any GitHub repository for it verification?if so ,will be greatly appreciated.

Super interesting. But boy you move quickly through all this. It's really hard to follow at times.

Thanks

What metrics should i keep an eye on to know what to change to get better and better results? I'm trying to create the fake images to use for data augmentation, and obviously I want them as realistic as possible, but I honestly don't know which parameters to change for it to get better. Plus, i have 10 classes and I dont know if i should just change the same for all of them or just see what works for each. But, again, i dont even know how to make it better for even one

Two questions: - Why use gpt-4 instead of gpt-4-turbo for the teleprompter? - What are you using to make your pointer act like that?

I always thought it was pronounced as D ES Pie. thanks for the deep dive btw!

great presentation!

Thanks for the great content. One of the things I am missing is how to save the optimized program so I can use it after that without constantly re-training.

Thank you.

i'm getting a headache by the zooming-in and then skipping across the page.

Connor be experimenting with video formats.

This is almost exactly the same video as the one by Qdrant. Weird.

I ran your notebook and got the following error. print(RAG()("What is binary quantization?").answer) AttributeError Traceback (most recent call last) Cell In[7], line 1 ----> 1 print(RAG()("What is binary quantization?").answer) File ~/code/vector_search/weaviate/recipes/.wenv/lib/python3.11/site-packages/dspy/primitives/program.py:26, in Module.__call__(self, *args, **kwargs) 25 def __call__(self, *args, **kwargs): ---> 26 return self.forward(*args, **kwargs) Cell In[6], line 16 15 def forward(self, question): ---> 16 context = self.retrieve(question).passages 17 pred = self.generate_answer(context=context, question=question).answer 18 return dspy.Prediction(context=context, answer=pred, question=question) File ~/code/vector_search/weaviate/recipes/.wenv/lib/python3.11/site-packages/dspy/retrieve/retrieve.py:30, in Retrieve.__call__(self, *args, **kwargs) 29 def __call__(self, *args, **kwargs): ---> 30 return self.forward(*args, **kwargs) File ~/code/vector_search/weaviate/recipes/.wenv/lib/python3.11/site-packages/dspy/retrieve/retrieve.py:39, in Retrieve.forward(self, query_or_queries, k) 36 # print(queries) 37 # TODO: Consider removing any quote-like markers that surround the query too. 38 k = k if k is not None else self.k ---> 39 passages = dsp.retrieveEnsemble(queries, k=k) 40 return Prediction(passages=passages) ... 79 .do() 81 results = results["data"]["Get"][self._weaviate_collection_name] 82 parsed_results = [result[self._weaviate_collection_text_key] for result in results] AttributeError: 'WeaviateClient' object has no attribute 'query'

Hey, what version of Weaviate-client you are using????