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Updated docs with load_in_4bit #558

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Feb 21, 2024
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Updated docs with load_in_4bit #558

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Updated docs with load_in_4bit
AlexKoff88 Feb 12, 2024
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Update documentation
echarlaix Feb 16, 2024
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21 changes: 9 additions & 12 deletions docs/source/optimization_ov.mdx
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Expand Up @@ -74,19 +74,16 @@ model = OVModelForCausalLM.from_pretrained(model_id, load_in_8bit=True)

> **NOTE:** `load_in_8bit` is enabled by default for models larger than 1 billion parameters.

For the 4-bit weight quantization we recommend using the NNCF API like below:
For the 4-bit weight quantization you can use the `quantization_config` to specify the optimization parameters, for example:

```python
from optimum.intel import OVModelForCausalLM
import nncf

model = OVModelForCausalLM.from_pretrained(model_id, load_in_8bit=False)
model.model = nncf.compress_weights(
model.model,
mode=nncf.CompressWeightsMode.INT4_SYM,
ratio=0.8,
group_size=128,
)
model.save_pretrained("compressed_model")
from optimum.intel import OVModelForCausalLM, OVWeightQuantizationConfig

model = OVModelForCausalLM.from_pretrained(
model_id,
export=True,
quantization_config=OVWeightQuantizationConfig(bits=4, sym=False, ratio=0.8, dataset="ptb"),
)
```

For more details, please refer to the corresponding NNCF [documentation](https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/CompressWeights.md).
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