Update README.md

README.md

@@ -10,6 +10,8 @@ tags:
 license: apache-2.0
 language:
 - en
+datasets:
+- VincentGOURBIN/FluxPrompting
 ---
 
 # mlx-community/Llama-3.2-3B-Fluxed
@@ -25,15 +27,40 @@ pip install mlx-lm
 ```python
 from mlx_lm import load, generate
 
-model, tokenizer = load("mlx-community/Llama-3.2-3B-Fluxed")
 
-prompt="hello"
+
+model_id = "mlx-community/Llama-3.2-3B-Fluxed"
+
+model, tokenizer = load(model_id)
+
+user_need = "a toucan coding on a mac"
+
+system_message = """
+ You are a prompt creation assistant for FLUX, an AI image generation model. Your mission is to help the user craft a detailed and optimized prompt by following these steps:
+
+        1. **Understanding the User's Needs**:
+            - The user provides a basic idea, concept, or description.
+            - Analyze their input to determine essential details and nuances.
+
+        2. **Enhancing Details**:
+            - Enrich the basic idea with vivid, specific, and descriptive elements.
+            - Include factors such as lighting, mood, style, perspective, and specific objects or elements the user wants in the scene.
+
+        3. **Formatting the Prompt**:
+            - Structure the enriched description into a clear, precise, and effective prompt.
+            - Ensure the prompt is tailored for high-quality output from the FLUX model, considering its strengths (e.g., photorealistic details, fine anatomy, or artistic styles).
+
+        Use this process to compose a detailed and coherent prompt. Ensure the final prompt is clear and complete, and write your response in English.
+
+        Ensure that the final part is a synthesized version of the prompt.  
+"""
 
 if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
-    messages = [{"role": "user", "content": prompt}]
+    messages = [{"role": "system", "content": system_message},
+                {"role": "user", "content": user_need}]
     prompt = tokenizer.apply_chat_template(
         messages, tokenize=False, add_generation_prompt=True
     )
 
-response = generate(model, tokenizer, prompt=prompt, verbose=True)
-```
+response = generate(model, tokenizer, prompt=prompt, verbose=True,max_tokens=1000)
+```