Update README.md

README.md

@@ -3,199 +3,116 @@ library_name: transformers
 tags: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-
-
+# Malaysian Qwen1.5-0.5B + siglip-base-patch16-384
+
+WanDB https://wandb.ai/huseinzol05/vision-qwen0.5?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
+
+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
+
+model = MM_LLMs.from_pretrained(
+    'mesolitica/malaysian-Qwen1.5-0.5B-siglip-base-384-vision',
+    flash_attention = True,
+    dtype = torch.bfloat16,
+    torch_dtype = torch.bfloat16
+)
+_ = model.cuda()
+
+image_processor = AutoProcessor.from_pretrained('google/siglip-base-patch16-384')
+tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-Qwen1.5-0.5B-siglip-base-384-vision')
+model.llm.generation_config.eos_token_id = tokenizer.eos_token_id
+
+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]))
+```
+
+```
+<|endoftext|><|im_start|>assistant
+Ini adalah gambar kucing putih yang duduk di atas sofa hitam.<|im_end|>
+```
+
+```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]))
+```
+
+```
+<|endoftext|><|im_start|>assistant
+Tiada hubungan langsung antara gambar 1 dan gambar 2. Gambar 1 ialah imej kucing putih dengan bulu putih, manakala gambar 2 ialah gambar mangkuk makan tengah hari kacang hitam dan lobak merah yang dicincang, dengan garpu diletakkan di sebelahnya. Kedua-duanya tidak berkaitan dari segi kandungan.<|im_end|>
+```