Update README.md

README.md

@@ -18,9 +18,9 @@ tags:
 
 **Try our demo available on [Demo](https://ocr.opentyphoon.ai/)**
 
-**Github available on [Github](https://github.com/scb-10x/typhoon-ocr)**
+**Code / Examples available on [Github](https://github.com/scb-10x/typhoon-ocr)**
 
-**Blog available on Blog**
+**Release Blog available on [OpenTyphoon Blog](https://opentyphoon.ai/blog/en/typhoon-ocr-release)**
 
 
 ## **Real-World Document Support**
@@ -61,11 +61,29 @@ For this version, our primary focus has been on achieving high-quality OCR for b
 ## Usage Example
 **(Recommended): Full inference code available on [Colab](https://colab.research.google.com/drive/1z4Fm2BZnKcFIoWuyxzzIIIn8oI2GKl3r?usp=sharing)**
 
+
+**(Recommended): Using Typhoon-OCR Package**
+```bash
+pip install typhoon-ocr
+```
+
+```
+from typhoon_ocr import ocr_document
+
+# please set env TYPHOON_OCR_API_KEY or OPENAI_API_KEY to use this function
+markdown = ocr_document("test.png")
+print(markdown)
+```
+**Run Manually**
+
 Below is a partial snippet. You can run inference using either the API or a local model.
 
-**API**:
+*API*:
 ```python
 from typing import Callable
+from openai import OpenAI
+from PIL import Image
+from typhoon_ocr.ocr_utils import render_pdf_to_base64png, get_anchor_text
 
 PROMPTS_SYS = {
     "default": lambda base_text: (f"Below is an image of a document page along with its dimensions. "
@@ -127,7 +145,7 @@ response = openai.chat.completions.create(
 text_output = response.choices[0].message.content
 print(text_output)
 ```
-**Local Model (GPU Required)**:
+*Local Model (GPU Required)*:
 ```python
 # Initialize the model
 model = Qwen2_5_VLForConditionalGeneration.from_pretrained("scb10x/typhoon-ocr-7b", torch_dtype=torch.bfloat16 ).eval()
@@ -167,7 +185,7 @@ print(text_output[0])
 
 ## **Intended Uses & Limitations**
 
-This model is an instructional model. However, it’s still undergoing development. It incorporates some level of guardrails, but it still may produce answers that are inaccurate, biased, or otherwise objectionable in response to user prompts. We recommend that developers assess these risks in the context of their use case.
+This is a task-specific model intended to be used only with the provided prompts. It does not include any guardrails or VQA capability. Due to the nature of large language models (LLMs), a certain level of hallucination may occur. We recommend that developers carefully assess these risks in the context of their specific use case.
 
 ## **Follow us**