sugiv
/

Fuyu-8b-transfer-learned-spiqa-simplified

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 ---
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 ---
 library_name: transformers
+license: cc-by-4.0
 ---
+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.
+``` 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)
+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()}
+# 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']]
+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
+        )
+# Decode the output
+generated_text = processor.decode(outputs[0], skip_special_tokens=True)
+# 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()
+print(generated_text)
+```