Muhammad Imran Zaman PRO
ImranzamanML
AI & ML interests
Results-driven Machine Learning Engineer with 7+ years of experience leading teams and delivering advanced AI solutions that increased revenue by up to 40%. Proven track record in enhancing business performance through consultancy and expertise in NLP, Computer Vision, LLM models and end-to-end ML pipelines. Skilled in managing critical situations and collaborating with cross-functional teams to implement scalable, impactful solutions. Kaggle Grandmaster and top performer in global competitions, dedicated to staying at the forefront of AI advancements.
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Post
213
Here is how we can calculate the size of any LLM model:
Each parameter in LLM models is typically stored as a floating-point number. The size of each parameter in bytes depends on the precision.
32-bit precision: Each parameter takes 4 bytes.
16-bit precision: Each parameter takes 2 bytes
To calculate the total memory usage of the model:
Memory usage (in bytes) = No. of Parameters × Size of Each Parameter
For example:
32-bit Precision (FP32)
In 32-bit floating-point precision, each parameter takes 4 bytes.
Memory usage in bytes = 1 billion parameters × 4 bytes
1,000,000,000 × 4 = 4,000,000,000 bytes
In gigabytes: ≈ 3.73 GB
16-bit Precision (FP16)
In 16-bit floating-point precision, each parameter takes 2 bytes.
Memory usage in bytes = 1 billion parameters × 2 bytes
1,000,000,000 × 2 = 2,000,000,000 bytes
In gigabytes: ≈ 1.86 GB
It depends on whether you use 32-bit or 16-bit precision, a model with 1 billion parameters would use approximately 3.73 GB or 1.86 GB of memory, respectively.
Each parameter in LLM models is typically stored as a floating-point number. The size of each parameter in bytes depends on the precision.
32-bit precision: Each parameter takes 4 bytes.
16-bit precision: Each parameter takes 2 bytes
To calculate the total memory usage of the model:
Memory usage (in bytes) = No. of Parameters × Size of Each Parameter
For example:
32-bit Precision (FP32)
In 32-bit floating-point precision, each parameter takes 4 bytes.
Memory usage in bytes = 1 billion parameters × 4 bytes
1,000,000,000 × 4 = 4,000,000,000 bytes
In gigabytes: ≈ 3.73 GB
16-bit Precision (FP16)
In 16-bit floating-point precision, each parameter takes 2 bytes.
Memory usage in bytes = 1 billion parameters × 2 bytes
1,000,000,000 × 2 = 2,000,000,000 bytes
In gigabytes: ≈ 1.86 GB
It depends on whether you use 32-bit or 16-bit precision, a model with 1 billion parameters would use approximately 3.73 GB or 1.86 GB of memory, respectively.
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1364
Instead of calculating errors, LLMs are better at doing self-evaluation!
It's easier to assess the quality of a response than to generate one which enables LLM models to evaluate their own performance.
It’s like trying to figure out how many ingredients you left out while cooking a recipe but without knowing exactly which ones you missed. LLM models like experienced cooks, can’t always tell you what specific step they skipped but they can guess how close they got to the final dish. For example, if your meal tastes 75%, you know something is off, but you are not sure what exactly.
Now instead of focusing on identifying every missed ingredient, think about just estimating how well the dish turned out overall. It’s easier to guess if the meal tastes good than to pinpoint each small mistake. LLMs do the same, they estimate how well they performed without knowing every single error, allowing them to self-evaluate!
meta-llama/Llama-3.2-1B
It's easier to assess the quality of a response than to generate one which enables LLM models to evaluate their own performance.
It’s like trying to figure out how many ingredients you left out while cooking a recipe but without knowing exactly which ones you missed. LLM models like experienced cooks, can’t always tell you what specific step they skipped but they can guess how close they got to the final dish. For example, if your meal tastes 75%, you know something is off, but you are not sure what exactly.
Now instead of focusing on identifying every missed ingredient, think about just estimating how well the dish turned out overall. It’s easier to guess if the meal tastes good than to pinpoint each small mistake. LLMs do the same, they estimate how well they performed without knowing every single error, allowing them to self-evaluate!
meta-llama/Llama-3.2-1B
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ImranzamanML/multilingual-mpnet-finetuned
Sentence Similarity
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ImranzamanML/1B_finetuned_llama3.2
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ImranzamanML/7B_finetuned_Mistral
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ImranzamanML/finetuned_llama3.1
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ImranzamanML/ai_psychiatrist
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ImranzamanML/GEFS-language-detector
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