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[Doc] Update examples to remove SparseAutoModelForCausalLM (#12062)

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Signed-off-by: Kyle Sayers <kylesayrs@gmail.com>
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Kyle Sayers 2025-01-15 01:36:01 -05:00 committed by GitHub
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11
docs/source/features/quantization/fp8.md
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@ -54,16 +54,15 @@ The quantization process involves three main steps:
### 1. Loading the Model
Use `SparseAutoModelForCausalLM`, which wraps `AutoModelForCausalLM`, for saving and loading quantized models:
Load your model and tokenizer using the standard `transformers` AutoModel classes:
```python
from llmcompressor.transformers import SparseAutoModelForCausalLM
from transformers import AutoTokenizer
from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
model = SparseAutoModelForCausalLM.from_pretrained(
MODEL_ID, device_map="auto", torch_dtype="auto")
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID, device_map="auto", torch_dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
```

7
docs/source/features/quantization/int8.md
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@ -30,14 +30,13 @@ The quantization process involves four main steps:
### 1. Loading the Model
Use `SparseAutoModelForCausalLM`, which wraps `AutoModelForCausalLM`, for saving and loading quantized models:
Load your model and tokenizer using the standard `transformers` AutoModel classes:
```python
from llmcompressor.transformers import SparseAutoModelForCausalLM
from transformers import AutoTokenizer
from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
model = SparseAutoModelForCausalLM.from_pretrained(
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID, device_map="auto", torch_dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)