so what's actually better about this compared to whisper?

This is a really important stuff

It sounds terrible. And clearly they are lying about "10 million hours". Just two chinese guys trying to rip you off.

OMG your point is right on the spot! That's exactly the problem I had to deal with in my project

what version of chroma db you were using back then??

Thank you this was very instructive. Can you recommend the best libaries for : 1) sectioning a document based on topic changes, 2) summarizing each section while maintaining contextual continuity and coherence, and 3) combining the summaries into a cohesive final summary? I'm thinking something like transformers (Hugging Face), spaCy, Gensim, pandas?

I have a request. Can you please explain the Customer Support Bot that is an example in Langgraph documentation? Or if you could simplify some of the stuff from that tutorial so langchain beginners, who know agents and tools can follow the tutorial? I find that official langgraph tutorial video on YT extremely lacking.

The fact that it allows you to get a random speaker sample and then hold it for later use is very intriguing. It's something I wished for the first time I encountered the ElevensLab and similar platforms like Suno. Additionally, training the model with those extra tokens is another interesting feature that's challenging to achieve in ElevensLab. Now, I'm curious about the format of the data when you get that random speaker. Is it a tensor? If so, can you perform arithmetic on it? For example, if you have a sample representing a happy speaker, could you add or subtract it to/from other sounds? This could lead to some fascinating applications, similar to how you can manipulate word embeddings (e.g., "king" - "male" + "female" = "queen"). I'll definitely take a closer look at this. Thanks for sharing!

I'm using mbrola for my TTS in my home automation. Though it is outdated, the quality is still the best of the tools I've seen so far. This ChatTTS looks very promising.Which version did you use? Currently it is at 0.0.5 and that doesn't work the way you described it. Not even with the code sample shown on HF. The keyword "compile" is not in chat.load_models. The chat.sample_random_speaker doesn't exist either. I've used it with python 3.11. BTW: Would be nice if it could understand Speech Synthesis Markup Language (SSML). If anybody knows a similar TTS which does, drop me a hit please. :)

Any recommendations for arabic embeddings?

Thank you! 😃

can't get through building of pynini..perhaps it depends on some specific version of openfst.

One thing I can't figure out is why no one is talking about how using ChatGPT 4o or for that matter Gemini, to do coding leads to all kinds of poor results and errors. Based on my experience of using LLMs to code, I would say there is a long way to go before reliability is there for real world products.

I'd be careful of a model from China without any people responsible behind it.. last time we had that was in 2019 lol

can i use any other open source llm model?which one do you recommend

Have you looked into some comparisons of it with OpenVoice v2?

great!

Q is abbreviation for "Question"; and "Wen" is the PinYin for 问， which is the character "Question" in Chinese. This LLM was trained by Chinese tech giant company Alibaba (similar to AWS), and Simplified Chinese is the core for multilingual base in this model. It is interesting that Simplified Chinese is not included in the language chart in your video. 🤔

Thanks, this is great stuff. I've been teaching myself to build agents in langchain for some months and it is slow going. I think I need to step back and re-architect to use LangGraph instead. Looking forward to seeing more of your material on this stuff!

I've been very disappointed with all these frameworks. They do somethings well, but they all handle context assembly very badly, and I think context assembly is the most critical part of agent building.

Hi, can you do ollama function calling video?

Which version of Phi 3 are you using? I'm having trouble replicating your results for the structured_output example as Phi 3 is not returning any "tool_calls".

Another fantastic and super educational video on this timely topic, and great framework. Always so much to learn from these videos....

what's the latency?

@samwitteveenai can i get the image as a output based on the questions if yes how can I do it ?

Hello. Can you please make a video on how to use the SeamlessM4T HuggingFace model with langchain ? Particularly for text to text translation. I am trying to do some prompt engineering with the model using Langchain's LLMChain module. But it does not seem to work ...

Tanx a lot. I wanted to say that the background scenes can be somehow distracting.

Note that the model has been censored for topics that are sensitive to the Chinese government. If you try asking it about the sovereignty status of Tibet, the independence of Taiwan, the Tiananmen incident, etc., it will throw an error in most cases. I observed this when testing the 72B instruct chat version on HuggingFace.

useful,subscribed

Do not enable hf transfer . If you can not wait , do not use it . Or get full fiber optic connection and hope ms algoritm Will remember you aint on cable . I believe its called reno . Ms still seem to have issue scaling up speed of bandwith

This is such a great intro, thank you so much for the effort.

It is really good in creative writing if you prompt it to plan the story first. I got amazing results with the phrase “rich and enhanced narration”. It was also fun to play with untypical text strings based on patterns as an output format.

pronounced as "qian wen". Q stands for "Qian"(meaning "thousands"), "wen" means "ask". So Qwen in Chinese means "thousands of questions"

Amazing review. All the cool features and improvements in math and coding aside, I'm extremely happy to see we have an Apache model that sounds like all of us! Covering all main language regions has always been my number one concern. AI models were becoming extremely selective, and we know that when we lose genetic diversity, we face extinction. No matter our cultural background, our existence is deeply interconnected with all other cultures on planet Earth, even those we haven't yet heard of. Languages are like musical notes; losing even one changes the entire symphony. In my opinion, language defines us, and if AI is to preserve our essence, language models must reflect this diversity. The lack of linguistic diversity means a lack of humanity and, eventually, a form of cultural extinction. For me, this development is amazing, and I agree, Groq should really bring this into the game. I'm going to share your video on my X and draft a post about it. Thx for your video that motivated me to write about this topic :)

hmmm. my hobby is pizza as well. :)

The production for this model started in China - I'm out.

Qwen 1.5 and 2.0 models appear to be optimized for tasks in the STEM areas, whereas other models appear to be optimized for creative writing and conversation. Qwen 2 7B instruct reached 79.9 on HumanEval, which is very impressive for a non-coding specific model of that size. Can't wait for a coding optimized version of Qwen 2. The strongest open source coding model is currently Codestral 22B before Llama 3 70B instruct and Mixtral 8x22B instruct

Hey Sam, thanks again for putting out these videos. I think you may have a noise gate set too high on your voice as we occasionally lose trailing syllables in your videos. If you have a lot of background noise to exclude, look for a hysteresis adjustment on the gate -- then you can set the level needed to keep the gate open once it's open. If there's no hysteresis setting, the next best is to set a slower release time.

"democratizing AI" does not mean multi-lingual

BTW because you mentioned Together and a missing hosted version, you might wanna check openrouter, usually they're super fast making new models available.

So, it was trained on the gsm8k dataset we can presume don't we? What i noticed for previous versions anecdotally, is that it did pretty well in various languages in terms of style and tone of the particular language.

I would humbly ask, please don’t put in long sections of black video for too long. It makes me think YT has bugged out. I would suggest a neutral tone color other than black. Thanks for your work on these informative videos! Keep it up. ❤

I wonder how much performance you can get out of a 7B model like this using an approach similar to Anthropic's recent Monosemanticity paper. Are the answers these benchmarks are looking to be found encoded somewhere in the model given the right biases during inference, or do we really need those very large models after all?

I had used it on my custom json mode benchmark, and I got it down to a .3% failure rate(after changing the multi-turn prompts to “json objects not detect, please try again differently”). It really likes to follow json mode, I would recommend if you need a model with consistent json mode. I only think it ever failed due to out of context. Once lm studio supports llama.cpp’s quantized kv cache, then I can finally use 128k context length.

Problem is its very censored and is useless for web browsing.

Amazing as always. What are some better models (both open or closed source)? Thanks Sam

Thank you for going through the notebooks line by line. Helps noobs like me follow along.

I like how you pronounce langchain

I've been testing some agriculture stuff, maybe fine-tuning this model with roboflow datasets and see. 🤔

9:15 it actually got the sunglasses the first time itself. If you read it again you'll see it. You missed it :)