metadata
license: apache-2.0
library_name: mlx
language:
- en
- fr
- zh
- de
tags:
- programming
- code generation
- code
- codeqwen
- moe
- coding
- coder
- qwen2
- chat
- qwen
- qwen-coder
- mixture of experts
- 4 experts
- 2 active experts
- 40k context
- qwen3
- finetune
- qwen3_moe
- creative
- all use cases
- roleplay
- merge
- mlx
base_model: DavidAU/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2
pipeline_tag: text-generation
Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-bf16-mlx
This model Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-bf16-mlx was converted to MLX format from DavidAU/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2 using mlx-lm version 0.26.4.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-bf16-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)