Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI
LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's Younes Belkada and Marc Sun, you'll:
- Learn how to quantize nearly any open source model
- Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library
- Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers
As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here: https://lnkd.in/g66yNW8W
#Me: I'm not signing up for any #AI courses for a while.
#LinkedIn: Dr. Andrew Ng just launched a new AI course.
#Me: He releases a new AI course almost every week. I'm sit this one out.
#Andrew Ng: I'm excited to introduce #Quantization fundamentals...
#Me: Ah, compressing large LLMs so they are more accessible, hmm...
#DrNg: ...with Hugging Face
#Me: Why didn't you say so? Where's the link?!
#quantizationfundamentals#opensource#huggingface#nextelligence
Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI
LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's Younes Belkada and Marc Sun, you'll:
- Learn how to quantize nearly any open source model
- Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library
- Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers
As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here: https://lnkd.in/g66yNW8W
Quantization is a game changer for deploying large language models like Mistral 7B on consumer hardware. This course offers invaluable techniques to harness powerful AI models even with limited resources.
Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI
LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's Younes Belkada and Marc Sun, you'll:
- Learn how to quantize nearly any open source model
- Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library
- Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers
As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here: https://lnkd.in/g66yNW8W
Quantization is becoming more and more relevant as LLMs become larger and more resource-intensive, helping to make models more practical and accessible.
Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI
LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's Younes Belkada and Marc Sun, you'll:
- Learn how to quantize nearly any open source model
- Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library
- Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers
As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here: https://lnkd.in/g66yNW8W
Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI
LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's Younes Belkada and Marc Sun, you'll:
- Learn how to quantize nearly any open source model
- Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library
- Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers
As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here: https://lnkd.in/g66yNW8W
Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI
LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's Younes Belkada and Marc Sun, you'll:
- Learn how to quantize nearly any open source model
- Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library
- Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers
As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here: https://lnkd.in/g66yNW8W
Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI
LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's Younes Belkada and Marc Sun, you'll:
- Learn how to quantize nearly any open source model
- Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library
- Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers
As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here: https://lnkd.in/g66yNW8W
While being futuristic, Quantization of a Neural Network model is the key to reduce dependency on high end hardware.
This one is a great explanation of mode quantization techniques that can save millions of 💵 trying to implement LLM
#LLM#ConversationalAI#GenAI#Chatbots#contactcenter
Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI
LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's Younes Belkada and Marc Sun, you'll:
- Learn how to quantize nearly any open source model
- Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library
- Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers
As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here: https://lnkd.in/g66yNW8W
🪝 “Diving into the Depths of Efficiency: The Quantum Leap in LLMs!”
As Andrew Ng introduced, In the relentless pursuit of computational excellence, we’ve reached a pivotal milestone: the quantization of Large Language Models. This isn’t just an incremental improvement; it’s a transformative approach that redefines the boundaries of AI efficiency and performance.
While the quantization of Large Language Models (LLMs) heralds a new era of AI efficiency, it’s not without its challenges. Here are some hurdles we’re facing in this quantum leap:
📌 Precision Trade-Offs: The process of quantization involves reducing the precision of the model’s parameters, which can lead to a loss in accuracy. Balancing model size with performance is a delicate act.
📌 Hardware Compatibility: Not all hardware is ready to support quantized models. Ensuring compatibility and performance across diverse platforms is a significant challenge.
📌 Optimization Complexity: Quantization requires sophisticated optimization techniques. Developing algorithms that can effectively compress models without losing essential information is a complex task.
📌 Data Sensitivity: Some tasks are more sensitive to quantization than others. Identifying and mitigating the impact on such tasks is crucial for maintaining the integrity of the model’s output.
📌 Resource Intensity: Although quantization aims to reduce resource usage, the initial process of quantizing and fine-tuning LLMs can be resource-intensive.
Despite these challenges, the potential benefits of quantized LLMs are too significant to ignore. As we continue to innovate and overcome these obstacles, we move closer to creating AI that’s not only powerful but also sustainable and inclusive.
#AIChallenges#Quantization#LLMs#MachineLearning#AI
Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI
LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's Younes Belkada and Marc Sun, you'll:
- Learn how to quantize nearly any open source model
- Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library
- Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers
As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here: https://lnkd.in/g66yNW8W
LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's Younes Belkada and Marc Sun, you'll:
- Learn how to quantize nearly any open source model
- Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library
- Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers
As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here: https://lnkd.in/g66yNW8W
Data Scientist | 3 years of experience | Machine Learning and Deep Learning Developer | Passionate about solving complex business problems with ML and LLM's
In the era of super large language models, understanding LLM quantization is essential. I highly recommend diving into the topic of one-bit quantization as well.
#LLM#Quantization#AI#DataScience#MachineLearning
Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI
LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's Younes Belkada and Marc Sun, you'll:
- Learn how to quantize nearly any open source model
- Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library
- Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers
As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here: https://lnkd.in/g66yNW8W
