Update conversion instructions

samples/python/image_generation/README.md

@@ -30,12 +30,29 @@ Users can change the sample code and play with the following generation paramete
 The `--upgrade-strategy eager` option is needed to ensure `optimum-intel` is upgraded to the latest version.
 
 Install [../../export-requirements.txt](../../export-requirements.txt) to convert a model.
-
 ```sh
 pip install --upgrade-strategy eager -r ../../export-requirements.txt
+```
+
+Then, run the export with Optimum CLI:
+
+```sh
 optimum-cli export openvino --model dreamlike-art/dreamlike-anime-1.0 --task stable-diffusion --weight-format fp16 dreamlike_anime_1_0_ov/FP16
 ```
 
+Alternatively, do it in Python code (FP16 is used by default). If NNCF is installed, the model will be compressed to INT8 automatically.
+
+```python
+from optimum.exporters.openvino.convert import export_tokenizer
+from optimum.intel import OVPipelineForText2Image
+
+output_dir = "dreamlike_anime_1_0_ov/FP16"
+
+pipeline = OVPipelineForText2Image.from_pretrained("dreamlike-art/dreamlike-anime-1.0", export=True)
+pipeline.save_pretrained(output_dir)
+export_tokenizer(pipeline.tokenizer, output_dir + "/tokenizer")
+```
+
 ## Run text to image
 
 Install [deployment-requirements.txt](../../deployment-requirements.txt) via `pip install -r ../../deployment-requirements.txt` and then, run a sample:

samples/python/rag/README.md

@@ -10,9 +10,30 @@ Install [../../export-requirements.txt](../../export-requirements.txt) to conver
 
 ```sh
 pip install --upgrade-strategy eager -r ../../export-requirements.txt
+```
+
+Then, run the export with Optimum CLI:
+
+```sh
 optimum-cli export openvino --trust-remote-code --model BAAI/bge-small-en-v1.5 BAAI/bge-small-en-v1.5
 ```
 
+Alternatively, do it in Python code:
+
+```python
+from optimum.exporters.openvino.convert import export_tokenizer
+from optimum.intel import OVModelForFeatureExtraction
+from transformers import AutoTokenizer
+
+output_dir = "embedding_model"
+
+model = OVModelForFeatureExtraction.from_pretrained("BAAI/bge-small-en-v1.5", export=True, trust_remote_code=True)
+model.save_pretrained(output_dir)
+
+tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-small-en-v1.5")
+export_tokenizer(tokenizer, output_dir)
+```
+
 ## Run
 
 Install [deployment-requirements.txt](../../deployment-requirements.txt) via `pip install -r ../../deployment-requirements.txt` and then, run a sample:
@@ -24,7 +45,6 @@ See [SUPPORTED_MODELS.md](../../../SUPPORTED_MODELS.md#text-embeddings-models) f
 # Text Embedding Pipeline Usage
 
 ```python
-import argparse
 import openvino_genai
 
 pipeline = openvino_genai.TextEmbeddingPipeline(model_dir, "CPU")

samples/python/speech_generation/README.md

@@ -12,10 +12,31 @@ Install [../../export-requirements.txt](../../export-requirements.txt) to conver
 
 ```sh
 pip install --upgrade-strategy eager -r ../../export-requirements.txt
+```
+
+Then, run the export with Optimum CLI:
+
+```sh
 optimum-cli export openvino --model microsoft/speecht5_tts --model-kwargs "{\"vocoder\": \"microsoft/speecht5_hifigan\"}" speecht5_tts
 ```
 
-**Note:** Currently, text-to-speech in OpenVINO GenAI supports the `SpeechT5 TTS` model.
+Alternatively, you can do it in Python code:
+
+```python
+from optimum.exporters.openvino.convert import export_tokenizer
+from optimum.intel import OVModelForTextToSpeechSeq2Seq
+from transformers import AutoTokenizer
+
+output_dir = "speecht5_tts"
+
+model = OVModelForTextToSpeechSeq2Seq.from_pretrained("microsoft/speecht5_tts", vocoder="microsoft/speecht5_hifigan", export=True)
+model.save_pretrained(output_dir)
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/speecht5_tts")
+export_tokenizer(tokenizer, output_dir)
+```
+
+**Note:** Currently, text-to-speech in OpenVINO GenAI supports the `SpeechT5 TTS` model. 
 When exporting the model, you must specify a vocoder using the `--model-kwargs` option in JSON format.
 
 ## Prepare speaker embedding file

samples/python/text_generation/README.md

@@ -13,10 +13,35 @@ There are also Jupyter notebooks for some samples. You can find links to them in
 ## Download and convert the model and tokenizers
 The `--upgrade-strategy eager` option is needed to ensure `optimum-intel` is upgraded to the latest version.
 Install [../../export-requirements.txt](../../export-requirements.txt) if model conversion is required.
+
 ```sh
 pip install --upgrade-strategy eager -r ../../export-requirements.txt
+```
+
+Then, run the export with Optimum CLI:
+
+```sh
 optimum-cli export openvino --model <model> <output_folder>
 ```
+
+Alternatively, do it in Python code (e.g. TinyLlama_v1.1). If NNCF is installed, the model will be compressed to INT8 automatically.
+
+```python
+from optimum.exporters.openvino.convert import export_tokenizer
+from optimum.intel import OVModelForCausalLM
+from transformers import AutoTokenizer
+
+output_dir = "chat_model"
+
+model = OVModelForCausalLM.from_pretrained("TinyLlama/TinyLlama_v1.1", export=True)
+model.save_pretrained(output_dir)
+
+tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama_v1.1")
+tokenizer.save_pretrained(output_dir)
+export_tokenizer(tokenizer, output_dir)
+```
+[//]: # "tokenizer.save_pretrained(output_dir) is required above to mitigate runtime errors"
+
 If a converted model in OpenVINO IR format is already available in the collection of [OpenVINO optimized LLMs](https://huggingface.co/collections/OpenVINO/llm-6687aaa2abca3bbcec71a9bd) on Hugging Face, it can be downloaded directly via huggingface-cli.
 ```sh
 pip install huggingface-hub

samples/python/visual_language_chat/README.md

@@ -14,9 +14,30 @@ Install [../../export-requirements.txt](../../export-requirements.txt) to conver
 
 ```sh
 pip install --upgrade-strategy eager -r ../../export-requirements.txt
+```
+
+Then, run the export with Optimum CLI:
+
+```sh
 optimum-cli export openvino --model openbmb/MiniCPM-V-2_6 --trust-remote-code MiniCPM-V-2_6
 ```
 
+Alternatively, you can do it in Python code:
+
+```python
+from optimum.exporters.openvino.convert import export_tokenizer
+from optimum.intel import OVModelForVisualCausalLM
+from transformers import AutoTokenizer
+
+output_dir = "MiniCPM-V-2_6"
+
+model = OVModelForVisualCausalLM.from_pretrained("openbmb/MiniCPM-V-2_6", export=True, trust_remote_code=True)
+model.save_pretrained(output_dir)
+
+tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM-V-2_6")
+export_tokenizer(tokenizer, output_dir)
+```
+
 ## Run:
 
 [This image](https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/d5fbbd1a-d484-415c-88cb-9986625b7b11) can be used as a sample image.

samples/python/whisper_speech_recognition/README.md

@@ -10,9 +10,30 @@ Install [../../export-requirements.txt](../../export-requirements.txt) to conver
 
 ```sh
 pip install --upgrade-strategy eager -r ../../export-requirements.txt
+```
+
+Then, run the export with Optimum CLI:
+
+```sh
 optimum-cli export openvino --trust-remote-code --model openai/whisper-base whisper-base
 ```
 
+Alternatively, you can do it in Python code:
+
+```python
+from optimum.exporters.openvino.convert import export_tokenizer
+from optimum.intel import OVModelForSpeechSeq2Seq
+from transformers import AutoTokenizer
+
+output_dir = "whisper-base"
+
+model = OVModelForSpeechSeq2Seq.from_pretrained("openai/whisper-base", export=True, trust_remote_code=True)
+model.save_pretrained(output_dir)
+
+tokenizer = AutoTokenizer.from_pretrained("openai/whisper-base")
+export_tokenizer(tokenizer, output_dir)
+```
+
 ## Prepare audio file
 
 Download example audio file: https://storage.openvinotoolkit.org/models_contrib/speech/2021.2/librispeech_s5/how_are_you_doing_today.wav