update

README.md

@@ -30,7 +30,7 @@ pipeline_tag: text-generation
 
 <sup>*</sup> Project lead  <sup>†</sup> First authors  <sup>‡</sup> Second authors  <sup>▽</sup> Leadership  
 
-\[[arXiv Paper](https://www.arxiv.org/pdf/2502.13130)\] &nbsp; \[[Project Page](https://microsoft.github.io/Magma/)\] &nbsp; \[[Hugging Face Model](https://huggingface.co/microsoft/Magma-8B)\] &nbsp; \[[Github Repo](https://github.com/microsoft/Magma)\]
+\[[arXiv Paper](https://www.arxiv.org/pdf/2502.13130)\] &nbsp; \[[Project Page](https://microsoft.github.io/Magma/)\] &nbsp; \[[Hugging Face Paper](https://huggingface.co/papers/2502.13130)\] &nbsp; \[[Github Repo](https://github.com/microsoft/Magma)\]
 
 </div>
 
@@ -67,6 +67,45 @@ The model is developed by Microsoft and is funded by Microsoft Research. The mod
 - **License:** {{ license | default("[More Information Needed]", true)}}
 - **Finetuned from model [optional]:** {{ base_model | default("[More Information Needed]", true)}} -->
 
+## How to Get Started with the Model
+
+<!-- {{ get_started_code | default("[More Information Needed]", true)}} -->
+
+Use the code below to get started with the model.
+
+```python
+import torch
+from PIL import Image
+import requests
+
+from transformers import AutoModelForCausalLM
+from transformers import AutoProcessor
+
+# Load the model and processor
+model = AutoModelForCausalLM.from_pretrained("microsoft/Magma-8B", trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("microsoft/Magma-8B", trust_remote_code=True)
+
+# Inference
+url = "https://assets-c4akfrf5b4d3f4b7.z01.azurefd.net/assets/2024/04/BMDataViz_661fb89f3845e.png" 
+image = Image.open(requests.get(url, stream=True).raw) 
+
+convs = [
+    {"role": "system", "content": "You are agent that can see, talk and act."},            
+    {"role": "user", "content": "<image_start><image><image_end>\nWhat is in this image?"},
+]
+prompt = processor.tokenizer.apply_chat_template(convs, tokenize=False, add_generation_prompt=True)
+inputs = processor(images=[image], texts=prompt, return_tensors="pt")
+inputs = inputs.to("cuda")
+
+with torch.inference_mode():
+    generate_ids = model.generate(**inputs, **generation_args)
+
+generate_ids = generate_ids[:, inputs["input_ids"].shape[-1] :]
+response = processor.decode(generate_ids[0], skip_special_tokens=True).strip()
+
+print(response)
+```
+
 ## Intended 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. -->
@@ -141,43 +180,6 @@ For the robotic manipulation task, some mitigation strategies to use for human s
 * **Safety Standards and Compliance:** Adhere to established safety standards (e.g., ISO 10218, ISO/TS 15066) for industrial robots and collaborative robots.
 * **User Training and Awareness:** Provide comprehensive training for all personnel working around robotic arms to understand their functions, safety features, and emergency procedures. Promote awareness of the potential risks associated with robotic manipulation.
 
-## How to Get Started with the Model
-
-<!-- {{ get_started_code | default("[More Information Needed]", true)}} -->
-
-Use the code below to get started with the model.
-
-```python
-from magma.image_processing_magma import MagmaImageProcessor
-from magma.processing_magma import MagmaProcessor
-from magma.modeling_magma import MagmaForConditionalGeneration
-
-# Load the model
-model_id = "microsoft/Magma-8B"
-processor = MagmaProcessor.from_pretrained(model_id, trust_remote_code=True) 
-model = MagmaForConditionalGeneration.from_pretrained(model_id, device_map="cuda", low_cpu_mem_usage=True)
-
-# Inference
-url = "https://assets-c4akfrf5b4d3f4b7.z01.azurefd.net/assets/2024/04/BMDataViz_661fb89f3845e.png" 
-image = Image.open(requests.get(url, stream=True).raw) 
-
-convs = [
-    {"role": "system", "content": "You are agent that can see, talk and act."},            
-    {"role": "user", "content": "<image_start><image><image_end>\nWhat is in this image?"},
-]
-prompt = processor.tokenizer.apply_chat_template(convs, tokenize=False, add_generation_prompt=True)
-inputs = processor(images=[image], texts=prompt, return_tensors="pt")
-inputs = inputs.to("cuda")
-
-with torch.inference_mode():
-    generate_ids = model.generate(**inputs, **generation_args)
-
-generate_ids = generate_ids[:, inputs["input_ids"].shape[-1] :]
-response = processor.decode(generate_ids[0], skip_special_tokens=True).strip()
-
-print(response)
-```
-
 ## Training Details
 
 ### Training Data