Text Generation
MLX
Safetensors
qwen3_moe
programming
code generation
code
codeqwen
Mixture of Experts
coding
coder
qwen2
chat
qwen
qwen-coder
Qwen3-30B-A3B-Instruct-2507
Qwen3-30B-A3B
mixture of experts
128 experts
8 active experts
qwen3
finetune
brainstorm 40x
brainstorm
optional thinking
mlx-my-repo
conversational
6-bit
introvoyz041/Qwen3-55B-A3B-2507-TOTAL-RECALL-v2-MASTER-CODER-q6-hi-mlx-mlx-6Bit
The Model introvoyz041/Qwen3-55B-A3B-2507-TOTAL-RECALL-v2-MASTER-CODER-q6-hi-mlx-mlx-6Bit was converted to MLX format from nightmedia/Qwen3-55B-A3B-2507-TOTAL-RECALL-v2-MASTER-CODER-q6-hi-mlx using mlx-lm version 0.28.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("introvoyz041/Qwen3-55B-A3B-2507-TOTAL-RECALL-v2-MASTER-CODER-q6-hi-mlx-mlx-6Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model tree for introvoyz041/Qwen3-55B-A3B-2507-TOTAL-RECALL-v2-MASTER-CODER-q6-hi-mlx-mlx-6Bit
Base model
Qwen/Qwen3-30B-A3B-Instruct-2507