Update README.md

README.md

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-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-### Model Sources [optional]
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure 
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-## Glossary [optional]
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-
+# Malaysian TinyLlama + siglip-large-patch16-384
+
+WanDB https://wandb.ai/huseinzol05/vision-tinyllama?workspace=user-huseinzol05
+
+## how-to
+
+```python
+from modeling_vision import MM_LLMs, MM_LLMs_Config
+from transformers import AutoTokenizer, AutoProcessor
+from PIL import Image
+import requests
+
+model = MM_LLMs.from_pretrained(
+    'mesolitica/malaysian-tinyllama-1.1b-siglip-large-384-vision',
+    flash_attention = True,
+    dtype = torch.bfloat16,
+    torch_dtype = torch.bfloat16
+)
+_ = model.cuda()
+
+image_processor = AutoProcessor.from_pretrained('google/siglip-large-patch16-384')
+tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-tinyllama-1.1b-siglip-large-384-vision')
+
+def prepare_dataset(messages, images: List[str] = None):
+    if images is not None:
+        images = [Image.open(f).convert('RGB') for f in images]
+        image_output = image_processor(images=images, return_tensors='pt')['pixel_values']
+    else:
+        image_output = None
+    
+    prompt = tokenizer.apply_chat_template(messages, tokenize = False)
+    outputs = tokenizer(
+                    prompt,
+                    return_tensors='pt',
+                    return_overflowing_tokens=False,
+                    return_length=False)
+
+    outputs['images'] = image_output
+    outputs['image_index'] = torch.tensor([0] * len(outputs['images']))
+    outputs['image_starts'] = torch.tensor([tokenizer.convert_tokens_to_ids('<image>')] * len(outputs['images']))
+    return outputs
+
+with open('Persian-cat-breed.jpg', 'wb') as fopen:
+    fopen.write(requests.get('https://cdn.beautifulnara.net/wp-content/uploads/2017/12/10201620/Persian-cat-breed.jpg').content)
+
+with open('nasi-goreng-1-23.jpg', 'wb') as fopen:
+    fopen.write(requests.get('https://www.jocooks.com/wp-content/uploads/2023/09/nasi-goreng-1-23.jpg').content)
+
+messages = [
+    {'role': 'user', 'content': '<image> </image> ini gambar apa'},
+]
+outputs = prepare_dataset(messages, images = ['Persian-cat-breed.jpg'])
+outputs['images'] = outputs['images'].type(model.dtype)
+for k in outputs.keys():
+    if outputs[k] is not None:
+        outputs[k] = outputs[k].cuda()
+
+with torch.no_grad():
+    model_inputs = model.prepare_inputs_for_generation(**outputs)
+r = model_inputs.pop('input_ids', None)
+
+generate_kwargs = dict(
+    model_inputs,
+    max_new_tokens=300,
+    top_p=0.95,
+    top_k=50,
+    temperature=0.1,
+    do_sample=True,
+    num_beams=1,
+)
+
+r = model.llm.generate(**generate_kwargs)
+print(tokenizer.decode(r[0]))
+```
+
+```
+<s>Imej itu menunjukkan seekor kucing putih yang comel duduk di atas sofa hitam.</s>
+```
+
+```python
+messages = [
+    {'role': 'user', 'content': '<image> </image> <image> </image> apa kaitan 2 gambar ni'},
+]
+outputs = prepare_dataset(messages, images = ['Persian-cat-breed.jpg', 'nasi-goreng-1-23.jpg'])
+outputs['images'] = outputs['images'].type(model.dtype)
+for k in outputs.keys():
+    if outputs[k] is not None:
+        outputs[k] = outputs[k].cuda()
+
+with torch.no_grad():
+    model_inputs = model.prepare_inputs_for_generation(**outputs)
+r = model_inputs.pop('input_ids', None)
+
+generate_kwargs = dict(
+    model_inputs,
+    max_new_tokens=300,
+    top_p=0.95,
+    top_k=50,
+    temperature=0.1,
+    do_sample=True,
+    num_beams=1,
+)
+
+r = model.llm.generate(**generate_kwargs)
+print(tokenizer.decode(r[0]))
+```
+
+```
+<s>Tiada hubungan yang jelas antara gambar 1 (anak kucing putih duduk di atas sofa) dan gambar 2 (foto penutup mangkuk mi telur dengan nasi dan cili). Gambar pertama ialah imej haiwan, manakala gambar kedua ialah imej makanan. Mereka tergolong dalam kategori yang berbeza dan tidak mempunyai hubungan antara satu sama lain.</s>
+```