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Elly_7B-GGUF-IQ-Imatrix

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Hi, thank you very much. Model Elly has excellent reasoning skills and is very obedient. She can distinguish grammar and verbs well, and is well informed about history. And he knows how to write well-composed lyrics,
this is an example:

Question: Hi, let's make a text of this scene again with your beautiful poetic writing: A lonely woman goes for a walk on the Ligurian hills
Answer:
In the heart of the horizon, the Ligurian hills rise like the teeth of an immense print, adorned with plastered greens and reds.

A lone woman climbs a path on one of the slopes. The sunlight becomes softer, almost like a veil that has lost some of its features, making the panorama a little blurry.

On her shoulders, she carries an olive green jacket and in her right hand she tightly holds a brown bag. The head is tilted upwards, with closed eyes enjoying the warm sun and the gentle wind on the skin.

In a rhythmic cadence, she begins to move her feet on that dry, grassy grass, hearing the clicks of flowers beneath her steps. For a few moments, life seems underlined by the sound of bees flying around her, and the chimes of a distant bell tower.

Thoughts are like little clouds moving in all directions; but at some point, she has a feeling of tranquility within herself, an image of something greater than the physical world. Yes, there is a moment in which the soul collides with the Divine and, for an instant, the two entities merge as one.

In the very heart of the solitary woman, a profound respect for nature and its grandiose powers takes shape. The oldest trees and the flowers that have lived the longest turn to her with a mysterious love.

As she continues her journey, she begins to observe the appearance of things around her. A whitish stone, perfectly round, lies on the road under his feet; a white insect flies in front of the woman and then disappears into the blue sky.

At this point, the solitary woman has a vision: the inner and outer worlds blend together, creating a world of images, sounds, as well as colors and sensations.

She continues climbing for another few minutes, until she reaches a small dry stone hut. Her eyes approach the portal, and with a deep breath, she enters the place of peace.

Inside that simple but forceful room, the solitary woman finds herself and begins to speak high up, with a transparent voice that mixes with the wind and the notes of the birds.

Coming out of the hut after a few minutes, the woman has even more strength in her soul. From now on, all her steps are accompanied by the feeling of being united with the entire universe.

Request;
What can you tell me about Quantized AI models that are made in open source like:

quantization_options = [
    "Q4_K_M", "Q4_K_S", "IQ4_XS", "Q5_K_M", "Q5_K_S",
    "Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XXS"
]

answer:
Quantized AIs, also known as quantized neural models, are a type of neural models in which numerical variables are quantified in discrete intervals rather than continuously.

The quantization process can be optimized in several ways, including relying on the use of fixed bit values ​​or dynamic coding. When a fixed number of bits is used for each parameter, it is called "fixed quantization", while when a variable number of bits is used depending on the needs, it is called "dynamic coding".

The quantization process can be applied to the weight of connections in the model, the activation of each unit in the path of the input through the layers of the neural network, or the final combination of the output signal. Typically, quantization is used to improve the inference speed and energy efficiency of models, making them more practical for mobile devices and onboard systems.

The list you provided shows some examples of quantization options commonly used in neural networks. Q4_K_M, for example, means 4-bit quantization with a dynamic encoding (K) and a larger broadening factor (M). The meaning of the symbols used in these options has been defined by the IEEE and can be found in their standard definition.

These quantization options are generally available as part of the open-source TensorFlow library, which is one of the leading platforms for building and training neural models. In your list, the options "Q4_K_M", "Q4_K_S", and "IQ4_XS" are mainly used in models that are based on int32 or int16 type tensors. The other models can be used with different data types, such as int8, uint8, qint8 and quint8.

It is important to take into account that when quantizing a neural model, its ability to generalize may be diminished. This is because the quantization process can introduce errors that can negatively impact model performance. However, recent advances in quantization research have allowed us to obtain better results than in the early years of development of quantized models, although there are still many open fields for research in this direction.

Request:
Question: If I have 64 RAM CPU with AMD Ryzen 5 2400G processor and 8 GB Nividia RTX 3050 GPU, what quantification do you recommend?

Answer
Regarding the hardware you described, I would suggest using the Q4_K_M model to quantize the weights of the connections in the neural network and IQ3_XXS or IQ3_XXS for the quantization of the activations.

Thanks to your AMD Ryzen 5 2400G CPU and NVIDIA RTX 3050 GPU with 8 GB of memory, this hardware can support the execution of lower-bit quantized neural models than other lower-performance systems.

Additionally, the processor and GPU available on your system support hardware acceleration for graphics calculations, which can lead to further performance improvement of quantized models in terms of speed and energy efficiency.

For this reason, the Q4_K_M option combines 4-bit quantization with dynamic encoding and a larger broadening factor, which can further improve model performance.

For trigger quantization, the IQ3_XXS or IQ3_XXS options offer dynamic encoding with a minimal broadening factor, which can improve the energy efficiency of models and reduce their memory impact.

However, if you find yourself unable to tackle multiple computations, you can also attempt using the Q6_K or Q8_0 model for weight quantization, which will offer greater accuracy and performance improvement.

In any case, the optimization process for quantization is a very complex subject and requires an iterative approach, which may also include some fine-tuning steps of the model after quantization. For this reason, you can try using a tool like TensorFlow, which provides open source libraries for quantizing and developing neural models.
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