cf6a620bae
* Change order of LLM in header * Some updates to footer * Add BigDL-LLM index page and basic file structure * Update index page for key features * Add initial content for BigDL-LLM in 5 mins * Improvement to footnote * Add initial contents based on current contents we have * Add initial quick links * Small fix * Rename file * Hide cli section for now and change model supports to examples * Hugging Face format -> Hugging Face transformers format * Add placeholder for GPU supports * Add GPU related content structure * Add cpu/gpu installation initial contents * Add initial contents for GPU supports * Add image link to LLM index page * Hide tips and known issues for now * Small fix * Update based on comments * Small fix * Add notes for Python 3.9 * Add placehoder optimize model & reveal CLI; small revision * examples add gpu part * Hide CLI part again for first version of merging * add keyfeatures-optimize_model part (#1) * change gif link to the ones hosted on github * Small fix --------- Co-authored-by: plusbang <binbin1.deng@intel.com> Co-authored-by: binbin Deng <108676127+plusbang@users.noreply.github.com>
68 lines
3.1 KiB
Markdown
68 lines
3.1 KiB
Markdown
# BigDL-LLM in 5 minutes
|
|
|
|
You can use BigDL-LLM to run any [*Hugging Face Transformers*](https://huggingface.co/docs/transformers/index) PyTorch model. It automatically optimizes and accelerates LLMs using low-precision (INT4/INT5/INT8) techniques, modern hardware accelerations and latest software optimizations.
|
|
|
|
Hugging Face transformers-based applications can run on BigDL-LLM with one-line code change, and you'll immediately observe significant speedup<sup><a href="#footnote-perf" id="ref-perf">[1]</a></sup>.
|
|
|
|
Here, let's take a relatively small LLM model, i.e [open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2), and BigDL-LLM INT4 optimizations as an example.
|
|
|
|
## Load a Pretrained Model
|
|
|
|
Simply use one-line `transformers`-style API in `bigdl-llm` to load `open_llama_3b_v2` with INT4 optimization (by specifying `load_in_4bit=True`) as follows:
|
|
|
|
```python
|
|
from bigdl.llm.transformers import AutoModelForCausalLM
|
|
|
|
model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path="openlm-research/open_llama_3b_v2",
|
|
load_in_4bit=True)
|
|
```
|
|
|
|
```eval_rst
|
|
.. tip::
|
|
|
|
`open_llama_3b_v2 <https://huggingface.co/openlm-research/open_llama_3b_v2>`_ is a pretrained large language model hosted on Hugging Face. ``openlm-research/open_llama_3b_v2`` is its Hugging Face model id. ``from_pretrained`` will automatically download the model from Hugging Face to a local cache path (e.g. ``~/.cache/huggingface``), load the model, and converted it to ``bigdl-llm`` INT4 format.
|
|
|
|
It may take a long time to download the model using API. You can also download the model yourself, and set ``pretrained_model_name_or_path`` to the local path of the downloaded model. This way, ``from_pretrained`` will load and convert directly from local path without download.
|
|
```
|
|
## Load Tokenizer
|
|
|
|
You also need a tokenizer for inference. Just use the official `transformers` API to load `LlamaTokenizer`:
|
|
|
|
```python
|
|
from transformers import LlamaTokenizer
|
|
|
|
tokenizer = LlamaTokenizer.from_pretrained(pretrained_model_name_or_path="openlm-research/open_llama_3b_v2")
|
|
```
|
|
|
|
## Run LLM
|
|
|
|
Now you can do model inference exactly the same way as using official `transformers` API:
|
|
|
|
```python
|
|
import torch
|
|
|
|
with torch.inference_mode():
|
|
prompt = 'Q: What is CPU?\nA:'
|
|
|
|
# tokenize the input prompt from string to token ids
|
|
input_ids = tokenizer.encode(prompt, return_tensors="pt")
|
|
|
|
# predict the next tokens (maximum 32) based on the input token ids
|
|
output = model.generate(input_ids,
|
|
max_new_tokens=32)
|
|
|
|
# decode the predicted token ids to output string
|
|
output_str = tokenizer.decode(output[0], skip_special_tokens=True)
|
|
|
|
print(output_str)
|
|
```
|
|
|
|
------
|
|
|
|
<div>
|
|
<p>
|
|
<sup><a href="#ref-perf" id="footnote-perf">[1]</a>
|
|
Performance varies by use, configuration and other factors. <code><span>bigdl-llm</span></code> may not optimize to the same degree for non-Intel products. Learn more at <a href="https://www.Intel.com/PerformanceIndex">www.Intel.com/PerformanceIndex</a>.
|
|
</sup>
|
|
</p>
|
|
</div>
|