README.md

metadata

base_model: VincentGOURBIN/Llama-3.2-3B-Fluxed
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - trl
  - mlx
license: apache-2.0
language:
  - en
datasets:
  - VincentGOURBIN/FluxPrompting

mlx-community/Llama-3.2-3B-Fluxed

The Model mlx-community/Llama-3.2-3B-Fluxed was converted to MLX format from VincentGOURBIN/Llama-3.2-3B-Fluxed using mlx-lm version 0.19.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate



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": "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,max_tokens=1000)