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Fixed minor grammatical and code bugs #1542

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Mar 7, 2024
Merged

Fixed minor grammatical and code bugs #1542

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6 changes: 3 additions & 3 deletions docs/source/developer_guides/quantization.md
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Original file line number Diff line number Diff line change
Expand Up @@ -56,7 +56,7 @@ from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", quantization_config=config)
```

Next, you should call the [`~peft.utils.prepare_model_for_kbit_training`] function to preprocess the quantized model for traininng.
Next, you should call the [`~peft.utils.prepare_model_for_kbit_training`] function to preprocess the quantized model for training.

```py
from peft import prepare_model_for_kbit_training
Expand All @@ -77,7 +77,7 @@ config = LoraConfig(
r=16,
lora_alpha=8,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
lora_dropout=0.05
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
Expand Down Expand Up @@ -138,7 +138,7 @@ QLoRA adds trainable weights to all the linear layers in the transformer archite
config = LoraConfig(target_modules="all-linear", ...)
```

## AQLM quantizaion
## AQLM quantization

Additive Quantization of Language Models ([AQLM](https://arxiv.org/abs/2401.06118)) is a Large Language Models compression method. It quantizes multiple weights together and takes advantage of interdependencies between them. AQLM represents groups of 8-16 weights as a sum of multiple vector codes. This allows it to compress models down to as low as 2-bit with considerably low accuracy losses.

Expand Down
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