Update README.md

README.md

@@ -15,45 +15,62 @@ licence: license
 This model is a fine-tuned version of [unsloth/qwen2-vl-2b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen2-vl-2b-instruct-unsloth-bnb-4bit).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 
-## Quick start
-
-```python
-from transformers import pipeline
-
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="oddadmix/Khanandeh-0.1-Persian-OCR-2B-Instruct", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+You can load this model using the `transformers` and `qwen_vl_utils` library:
+```
+!pip install transformers qwen_vl_utils accelerate>=0.26.0 PEFT -U
+!pip install -U bitsandbytes
 ```
 
-## Training procedure
-
- 
-
-
-This model was trained with SFT.
+```python
+from PIL import Image
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+import torch
+import os
+from qwen_vl_utils import process_vision_info
 
-### Framework versions
 
-- TRL: 0.14.0
-- Transformers: 4.49.0
-- Pytorch: 2.4.1
-- Datasets: 3.4.1
-- Tokenizers: 0.21.1
 
-## Citations
+model_name = "oddadmix/Khanandeh-0.1-Persian-OCR-2B-Instruct"
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+                model_name,
+                torch_dtype="auto",
+                device_map="auto"
+            )
+processor = AutoProcessor.from_pretrained(model_name)
+max_tokens = 2000
 
+prompt = "Below is the image of one page of a document, as well as some raw textual content that was previously extracted for it. Just return the plain text representation of this document as if you were reading it naturally. Do not hallucinate."
+image.save("image.png")
 
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": f"file://{src}"},
+            {"type": "text", "text": prompt},
+        ],
+    }
+]
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+generated_ids = model.generate(**inputs, max_new_tokens=max_tokens)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)[0]
+os.remove(src)
+print(output_text)
 
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
 ```