---
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.