---
title: Llama-3.2-3B-Fluxed
canonical_url: "https://www.modelscope.cn/models/mlx-community/Llama-3.2-3B-Fluxed"
md_url: "https://www.modelscope.cn/models/mlx-community/Llama-3.2-3B-Fluxed.md"
repository: mlx-community/Llama-3.2-3B-Fluxed
last_updated: 2024-12-19
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - llama
architectures:
  - LlamaForCausalLM
parameters: 3.2B
tensor_type:
  - F16
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 1015
stars: 0
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - trl
  - mlx
---

# Llama-3.2-3B-Fluxed

> Llama-3.2-3B-Fluxed - mlx-community 在 ModelScope 开源的模型。mlx-community/Llama-3.2-3B-Fluxed

mlx-community/Llama-3.2-3B-Fluxed 是 ModelScope 魔搭社区上的 3.2B 参数text-generation模型，采用 apache-2.0 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: mlx-community/Llama-3.2-3B-Fluxed
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 3.2B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: text-generation-inference, transformers, unsloth, llama, trl, mlx
- **Downloads**: 1015
- **Stars**: 0
- **Last updated**: 2024-12-19

Source: https://www.modelscope.cn/models/mlx-community/Llama-3.2-3B-Fluxed

---

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

The Model [mlx-community/Llama-3.2-3B-Fluxed](https://huggingface.co/mlx-community/Llama-3.2-3B-Fluxed) was converted to MLX format from [VincentGOURBIN/Llama-3.2-3B-Fluxed](https://huggingface.co/VincentGOURBIN/Llama-3.2-3B-Fluxed) using mlx-lm version **0.19.3**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
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)
```