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[docs] fix bugs in the bitsandbytes documentation (#35868)

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* fix doc

* update model
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Fanli Lin 2025-02-06 00:21:20 +08:00 committed by MekkCyber
parent 741865eebf
commit 8e499a203c
1 changed files with 5 additions and 4 deletions
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9
docs/source/en/quantization/bitsandbytes.md
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@ -208,7 +208,8 @@ from transformers import AutoModelForCausalLM, BitsAndBytesConfig
model_id = "bigscience/bloom-1b7"
quantization_config = BitsAndBytesConfig(
llm_int8_threshold=10,
llm_int8_threshold=10.0,
llm_int8_enable_fp32_cpu_offload=True
)
model_8bit = AutoModelForCausalLM.from_pretrained(
@ -285,7 +286,7 @@ For inference, the `bnb_4bit_quant_type` does not have a huge impact on performa
### Nested quantization
Nested quantization is a technique that can save additional memory at no additional performance cost. This feature performs a second quantization of the already quantized weights to save an additional 0.4 bits/parameter. For example, with nested quantization, you can finetune a [Llama-13b](https://huggingface.co/meta-llama/Llama-2-13b) model on a 16GB NVIDIA T4 GPU with a sequence length of 1024, a batch size of 1, and enabling gradient accumulation with 4 steps.
Nested quantization is a technique that can save additional memory at no additional performance cost. This feature performs a second quantization of the already quantized weights to save an additional 0.4 bits/parameter. For example, with nested quantization, you can finetune a [Llama-13b](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) model on a 16GB NVIDIA T4 GPU with a sequence length of 1024, a batch size of 1, and enabling gradient accumulation with 4 steps.
```py
from transformers import BitsAndBytesConfig
@ -295,7 +296,7 @@ double_quant_config = BitsAndBytesConfig(
bnb_4bit_use_double_quant=True,
)
model_double_quant = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b", torch_dtype="auto", quantization_config=double_quant_config)
model_double_quant = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf", torch_dtype="auto", quantization_config=double_quant_config)
```
## Dequantizing `bitsandbytes` models
@ -307,7 +308,7 @@ from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer
model_id = "facebook/opt-125m"
model = AutoModelForCausalLM.from_pretrained(model_id, BitsAndBytesConfig(load_in_4bit=True))
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=BitsAndBytesConfig(load_in_4bit=True))
tokenizer = AutoTokenizer.from_pretrained(model_id)
model.dequantize()