One step further: extreme quantization of Llama 2 to 3 bits
One step further: extreme quantization of Llama 2 to 3 bits
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October 5, 2023
Language models have shown exponential progress in recent years. Their performance in natural language generation has reached significant levels, increasingly approaching human quality. From contextual responses in conversations to the drafting of creative content, these models have opened up a range of promising applications in various industries.
Resource consumption of LLMs
However, language models currently present a handicap: the high consumption of computational resources required for optimal functioning, posing a barrier to widespread access and use of this technology.
This is where quantization comes into play, a technique that allows leveraging the performance of language models by significantly reducing the resources needed to interact with them, resulting in more efficient and accessible artificial intelligence.
What is quantization?
Quantizing a language model involves simplifying the representation of its numerical parameters, reducing the amount of decimal information stored, making the model more efficient in terms of memory and calculations.
This process allows reducing the size of the language model, resulting in lower resource consumption and a reduction in the process cost.
Commitment to AI accessibility
Our AI research and development laboratory, Clibrain Labs, aware of the computational challenges posed by these models, carried out the quantization of all our open-source models, achieving significant efficiency improvements without compromising performance.
Today, they decide to go a step further, performing an extreme quantization of our adaptation of Llama 2 to Spanish in its 7B and 13B versions, reducing the model weights to 3 bits.
This quantization results in a model with lower weight and much lower computing requirements than the original to interact with the model, all without compromising the efficiency and performance of the language model.
In pursuit of maximum efficiency, our team also quantized the model to 2 bits, but this extreme level of quantization compromised the quality of results that the model provided, reducing its effectiveness. Therefore, in the 3-bit quantization, they found the perfect balance of efficiency and performance.
Models available under open-source license
In line with our commitment to the community, we have published the model on Hugging Face so that everyone can make use of it.
You can find the 3-bit quantization of Llama 2 in its 7B and 13B parameter versions and the rest of our open-source models at hf.co/clibrain.
Language models have shown exponential progress in recent years. Their performance in natural language generation has reached significant levels, increasingly approaching human quality. From contextual responses in conversations to the drafting of creative content, these models have opened up a range of promising applications in various industries.
Resource consumption of LLMs
However, language models currently present a handicap: the high consumption of computational resources required for optimal functioning, posing a barrier to widespread access and use of this technology.
This is where quantization comes into play, a technique that allows leveraging the performance of language models by significantly reducing the resources needed to interact with them, resulting in more efficient and accessible artificial intelligence.
What is quantization?
Quantizing a language model involves simplifying the representation of its numerical parameters, reducing the amount of decimal information stored, making the model more efficient in terms of memory and calculations.
This process allows reducing the size of the language model, resulting in lower resource consumption and a reduction in the process cost.
Commitment to AI accessibility
Our AI research and development laboratory, Clibrain Labs, aware of the computational challenges posed by these models, carried out the quantization of all our open-source models, achieving significant efficiency improvements without compromising performance.
Today, they decide to go a step further, performing an extreme quantization of our adaptation of Llama 2 to Spanish in its 7B and 13B versions, reducing the model weights to 3 bits.
This quantization results in a model with lower weight and much lower computing requirements than the original to interact with the model, all without compromising the efficiency and performance of the language model.
In pursuit of maximum efficiency, our team also quantized the model to 2 bits, but this extreme level of quantization compromised the quality of results that the model provided, reducing its effectiveness. Therefore, in the 3-bit quantization, they found the perfect balance of efficiency and performance.
Models available under open-source license
In line with our commitment to the community, we have published the model on Hugging Face so that everyone can make use of it.
You can find the 3-bit quantization of Llama 2 in its 7B and 13B parameter versions and the rest of our open-source models at hf.co/clibrain.
Language models have shown exponential progress in recent years. Their performance in natural language generation has reached significant levels, increasingly approaching human quality. From contextual responses in conversations to the drafting of creative content, these models have opened up a range of promising applications in various industries.
Resource consumption of LLMs
However, language models currently present a handicap: the high consumption of computational resources required for optimal functioning, posing a barrier to widespread access and use of this technology.
This is where quantization comes into play, a technique that allows leveraging the performance of language models by significantly reducing the resources needed to interact with them, resulting in more efficient and accessible artificial intelligence.
What is quantization?
Quantizing a language model involves simplifying the representation of its numerical parameters, reducing the amount of decimal information stored, making the model more efficient in terms of memory and calculations.
This process allows reducing the size of the language model, resulting in lower resource consumption and a reduction in the process cost.
Commitment to AI accessibility
Our AI research and development laboratory, Clibrain Labs, aware of the computational challenges posed by these models, carried out the quantization of all our open-source models, achieving significant efficiency improvements without compromising performance.
Today, they decide to go a step further, performing an extreme quantization of our adaptation of Llama 2 to Spanish in its 7B and 13B versions, reducing the model weights to 3 bits.
This quantization results in a model with lower weight and much lower computing requirements than the original to interact with the model, all without compromising the efficiency and performance of the language model.
In pursuit of maximum efficiency, our team also quantized the model to 2 bits, but this extreme level of quantization compromised the quality of results that the model provided, reducing its effectiveness. Therefore, in the 3-bit quantization, they found the perfect balance of efficiency and performance.
Models available under open-source license
In line with our commitment to the community, we have published the model on Hugging Face so that everyone can make use of it.
You can find the 3-bit quantization of Llama 2 in its 7B and 13B parameter versions and the rest of our open-source models at hf.co/clibrain.
Language models have shown exponential progress in recent years. Their performance in natural language generation has reached significant levels, increasingly approaching human quality. From contextual responses in conversations to the drafting of creative content, these models have opened up a range of promising applications in various industries.
Resource consumption of LLMs
However, language models currently present a handicap: the high consumption of computational resources required for optimal functioning, posing a barrier to widespread access and use of this technology.
This is where quantization comes into play, a technique that allows leveraging the performance of language models by significantly reducing the resources needed to interact with them, resulting in more efficient and accessible artificial intelligence.
What is quantization?
Quantizing a language model involves simplifying the representation of its numerical parameters, reducing the amount of decimal information stored, making the model more efficient in terms of memory and calculations.
This process allows reducing the size of the language model, resulting in lower resource consumption and a reduction in the process cost.
Commitment to AI accessibility
Our AI research and development laboratory, Clibrain Labs, aware of the computational challenges posed by these models, carried out the quantization of all our open-source models, achieving significant efficiency improvements without compromising performance.
Today, they decide to go a step further, performing an extreme quantization of our adaptation of Llama 2 to Spanish in its 7B and 13B versions, reducing the model weights to 3 bits.
This quantization results in a model with lower weight and much lower computing requirements than the original to interact with the model, all without compromising the efficiency and performance of the language model.
In pursuit of maximum efficiency, our team also quantized the model to 2 bits, but this extreme level of quantization compromised the quality of results that the model provided, reducing its effectiveness. Therefore, in the 3-bit quantization, they found the perfect balance of efficiency and performance.
Models available under open-source license
In line with our commitment to the community, we have published the model on Hugging Face so that everyone can make use of it.
You can find the 3-bit quantization of Llama 2 in its 7B and 13B parameter versions and the rest of our open-source models at hf.co/clibrain.