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Model: aifeifei798/Qwen3-1.7B-Flux-Prompt
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- qwen
+- qwen3
+- unsloth
+- decision-making
+- strategic-analysis
+- cognitive-architecture
+- chat
+- philosophy-driven-ai
+pipeline_tag: text-generation
+base_model:
+  - Qwen/Qwen3-1.7B
+datasets:
+  - ABDALLALSWAITI/flux_prompt
+---
+
+---
+
+### Thanks mradermacher: For creating the GGUF versions of these models
+
+https://huggingface.co/mradermacher/Qwen3-1.7B-Flux-Prompt-GGUF
+
+https://huggingface.co/mradermacher/Qwen3-1.7B-Flux-Prompt-i1-GGUF
+
+---
+
+# Qwen3-1.7B-Flux-Prompt
+
+**Turn simple words into professional Flux.1 image prompts instantly.**
+
+This is a fine-tuned version of **Qwen3-1.7B-Instruct**, specialized in expanding short concepts into detailed, high-quality descriptions optimized for **Flux.1** image generation models.
+
+## ✨ Key Features
+
+*   **🚀 Lightweight & Fast**: Based on the 1.7B model, it runs extremely fast even on older GPUs or CPU.
+*   **🧠 "Invisible" System Prompt**: The specialized system prompt is **baked into the `tokenizer_config.json`**. You don't need to type complex instructions. Just input `a cat`, and it outputs the full prompt automatically.
+*   **🎨 Non-Conversational**: It doesn't chat. It doesn't say "Here is your prompt". It only outputs the raw prompt, ready for your stable diffusion pipeline.
+*   **✅ Validated Quality**: Tested thoroughly for diversity and detail (see examples below).
+
+---
+
+## 🖼️ Examples
+
+**Input:** `a sexy model at home bed`
+
+**Output (Generated by this model):**
+> a sexy model at home bed, close-up shot of a woman lying on a luxurious velvet bed with soft golden lighting, elegant floral decor, minimalist modern furniture, subtle candlelight flicker, realistic textures, sensual atmosphere, high-quality photography, dramatic shadows, and a calm yet intimate mood.
+
+---
+
+## 💻 How to Use
+
+### Method 1: Python (Transformers)
+
+Since the system prompt is integrated, the usage is extremely simple:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "aifeifei798/Qwen3-1.7B-Flux-Prompt" # Replace with your actual repo name
+
+# Load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+def generate_flux_prompt(prompt):
+    
+    messages = [{"role": "user", "content": prompt}]
+
+    # Applying the template
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+        # enable_thinking=False
+    )
+    
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    
+    # Generation with optimized parameters for Flux prompting
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=512,      # [Key] 512 tokens are sufficient for a detailed visual description.
+        do_sample=True,          # [Required] Enable sampling to allow for creative variations.
+        temperature=0.7,         # [Creativity] 0.7 offers a balance between imaginative detail and coherence.
+        top_p=0.9,               # [Focus] Nucleus sampling: filters out very unlikely words.
+        top_k=50,                # [Stability] Limits vocabulary to top 50 tokens to prevent hallucinations.
+        repetition_penalty=1.15,  # [Variety] Slight penalty to reduce repetitive phrases without breaking grammar.
+        no_repeat_ngram_size=3, 
+    )
+    
+    # Extracting the newly generated tokens only
+    output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+    
+    # Parsing logic to handle potential reasoning content (if using a reasoning-capable base model)
+    # Looks for the </think> token ID (usually 151668 in Qwen architecture)
+    try:
+        index = len(output_ids) - output_ids[::-1].index(151668)
+    except ValueError:
+        index = 0 # No thinking trace found, output starts from the beginning
+
+    # Decode the final prompt
+    # thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip()
+    content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip()
+    
+    return content
+
+# Example Usage
+# if __name__ == "__main__":
+    # user_input = "a sexy model at new york"
+    # print(f"Input: {user_input}\n")
+    # print(f"Generated Flux Prompt:\n{generate_flux_prompt(user_input)}")
+```
+
+---
+
+## 🔧 Training Details
+
+*   **Base Model:** Qwen/Qwen3-1.7B-Instruct
+*   **Dataset:** `flux_prompt` (Alpaca format)
+*   **Fine-tuning Framework:** Unsloth
+*   **Configuration:** 
+    *   `temperature`: 0.7 (Recommended)
+    *   `repetition_penalty`: 1.05 - 1.1 (Recommended)
+
+---
+
+### 💡 Tips for Users
+
+*   **Keep it Simple:** The model thrives on simple inputs like "a girl in rain" or "futuristic car".
+*   **Sampling:** Always use `do_sample=True` with `temperature` around 0.7 to get diverse results every time you run it.
+