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[Doc] Update examples to remove SparseAutoModelForCausalLM #12062

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Jan 15, 2025
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[Doc] Update examples to remove SparseAutoModelForCausalLM #12062

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11 changes: 5 additions & 6 deletions docs/source/features/quantization/fp8.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
```

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7 changes: 3 additions & 4 deletions docs/source/features/quantization/int8.md
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Expand Up @@ -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)
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