Update README.md

README.md

@@ -1,3 +1,89 @@
----
-license: apache-2.0
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+# FeynModel V 0.1
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/645364cbf666f76551f93111/RNgPbOsYh_XAo-rCRrNUb.png)
+
+# how to use
+
+```python
+from transformers import AutoProcessor,  AutoModelForCausalLM
+model_id='Imagroune/feynmodel'
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id,trust_remote_code=True)
+model.to('cuda')
+```
+
+# LLM Inference
+
+```python
+
+input_text = "<start_of_turn>user\nCombien d'helicoptère un humain adulte peut manger en un seul repas?.<end_of_turn> <start_of_turn>model\n"
+input_ids = processor.tokenizer(input_text, return_tensors="pt").to("cuda")
+
+# Génération du texte en mode streaming
+max_length = input_ids.input_ids.shape[1] + 1024  # Longueur maximale totale
+stream_output = []  # Liste pour stocker le flux de sortie
+
+# Génération et affichage en mode streaming
+for output in model.generate(input_ids=input_ids.input_ids,max_length=max_length, do_sample=True, temperature=0.7):
+    decoded_output = processor.tokenizer.decode(output, skip_special_tokens=True)
+    stream_output.append(decoded_output)
+    print(decoded_output, end="", flush=True)
+
+```
+
+# Vision Inference
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, StoppingCriteriaList
+
+class PrintTokensStoppingCriteria(StoppingCriteria):
+    def __init__(self, tokenizer):
+        self.tokenizer = tokenizer
+
+    def __call__(self, input_ids, scores, **kwargs):
+        # Decode the last generated token and print it
+        last_token_id = input_ids[0, -1].item()
+        token = self.tokenizer.decode([last_token_id], skip_special_tokens=True)
+        print(token, end='', flush=True)
+        
+        # Continue generating tokens until a stopping condition is met
+        # Return True to stop, False to continue
+        return False
+stopping_criteria = PrintTokensStoppingCriteria(processor.tokenizer)
+
+from PIL import Image
+import requests 
+input_text = "<start_of_turn>user\n what is this ?<end_of_turn>\n<start_of_turn>model"
+
+
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
+image = Image.open(requests.get(url, stream=True).raw)
+
+
+input_text="""<start_of_turn>user
+Create a concise caption that accurately describes the main elements in the image provided
+<end_of_turn>
+<start_of_turn>model
+
+"""
+inputs = processor(text=input_text, images=image, return_tensors="pt")
+inputs = {key: value.cuda() for key, value in inputs.items()}
+
+image
+```
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/645364cbf666f76551f93111/XVxraj69m26HtkfaWQhve.png)
+
+
+```python
+
+max_length =inputs['input_ids'].shape[1] + 1024  # Longueur maximale totale
+stream_output = []  # Liste pour stocker le flux de sortie
+# Génération et affichage en mode streaming
+ret= model.generate(inputs['input_ids'], pixel_values=inputs['pixel_values'],stopping_criteria=StoppingCriteriaList([stopping_criteria]),max_length=2048, do_sample=True, temperature=0.7)
+
+# An older, green car sits parked on the curb in front of a building.
+
+```