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[doc] Fold long code blocks to improve readability (#19926)

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Signed-off-by: reidliu41 <reid201711@gmail.com>
Co-authored-by: reidliu41 <reid201711@gmail.com>
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Reid
2025-06-23 13:24:23 +08:00
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commit f17aec0d63
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docs/features/quantization/torchao.md
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@@ -15,26 +15,28 @@ pip install \
## Quantizing HuggingFace Models
You can quantize your own huggingface model with torchao, e.g. [transformers](https://huggingface.co/docs/transformers/main/en/quantization/torchao) and [diffusers](https://huggingface.co/docs/diffusers/en/quantization/torchao), and save the checkpoint to huggingface hub like [this](https://huggingface.co/jerryzh168/llama3-8b-int8wo) with the following example code:
```Python
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.quantization import Int8WeightOnlyConfig
??? Code
model_name = "meta-llama/Meta-Llama-3-8B"
quantization_config = TorchAoConfig(Int8WeightOnlyConfig())
quantized_model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto",
quantization_config=quantization_config
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
```Python
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.quantization import Int8WeightOnlyConfig
hub_repo = # YOUR HUB REPO ID
tokenizer.push_to_hub(hub_repo)
quantized_model.push_to_hub(hub_repo, safe_serialization=False)
```
model_name = "meta-llama/Meta-Llama-3-8B"
quantization_config = TorchAoConfig(Int8WeightOnlyConfig())
quantized_model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto",
quantization_config=quantization_config
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
hub_repo = # YOUR HUB REPO ID
tokenizer.push_to_hub(hub_repo)
quantized_model.push_to_hub(hub_repo, safe_serialization=False)
```
Alternatively, you can use the [TorchAO Quantization space](https://huggingface.co/spaces/medmekk/TorchAO_Quantization) for quantizing models with a simple UI.