Update README.md

README.md

@@ -1,199 +1,49 @@
 ---
 library_name: transformers
-tags: []
+license: cc-by-4.0
 ---
 
-# Model Card for Model ID
+This model is transfer learned on scientific image visual question answering simplified dataset, sugiv/spiqa-simplified-for-fuyu8b-transfer-learning and it is based on 
+adept/fuyu-8b. Most of the model layers are frozen and as I am GPU poor, this transfer learned model was trained only on a subset of simplified dataset and for two epochs only on A100, 80GB rented and $10 dollars was total spent. 
 
-<!-- Provide a quick summary of what the model is/does. -->
+``` python
 
+model_path="sugiv/Fuyu-8b-transfer-learned-spiqa-simplified"
+processor = FuyuProcessor.from_pretrained(model_path)
+model = FuyuForCausalLM.from_pretrained(model_path, device_map="auto")
 
+text_prompt = "What color is the bus?\n"
+url = "https://huggingface.co/adept/fuyu-8b/resolve/main/bus.png"
+image = Image.open(requests.get(url, stream=True).raw)
 
-## Model Details
+inputs = processor(text=text_prompt, images=image, return_tensors="pt").to("cuda:0")
+# Move inputs to the same device as the model
+device = next(model.parameters()).device
+inputs = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
 
-### Model Description
+# If 'image_patches' is a list of tensors, move each tensor to the correct device
+if 'image_patches' in inputs and isinstance(inputs['image_patches'], list):
+    inputs['image_patches'] = [patch.to(device) for patch in inputs['image_patches']]
 
-<!-- Provide a longer summary of what this model is. -->
+outputs = model.generate(
+            **inputs,
+            max_new_tokens=400,
+            repetition_penalty=1.2,
+            no_repeat_ngram_size=3,
+            top_k=40,
+            top_p=0.92,
+            temperature=0.7,
+            do_sample=True
+        )
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+# Decode the output
+generated_text = processor.decode(outputs[0], skip_special_tokens=True)
 
-- **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]
+# Clean up the generated text
+generated_text = generated_text.replace("|SPEAKER|", "").replace("|NEWLINE|", " ").strip()
+if "\x04" in generated_text:
+    generated_text = generated_text.split("\x04")[-1].strip()
 
-### Model Sources [optional]
+print(generated_text)
 
-<!-- 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]
+```