license: apache-2.0
datasets:
- TeichAI/claude-4.5-opus-high-reasoning-250x
language: - en
library_name: mlx
tags: - finetune
- unsloth
- abliterated
- uncensored
- specialized post tuning
- claude-4.5-opus
- reasoning
- thinking
- distill-fine-tune
- moe
- 128 experts
- 256k context
- mixture of experts
- mlx
base_model: DavidAU/Qwen3-30B-A3B-Claude-4.5-Opus-High-Reasoning-2507-ABLITERATED-UNCENSORED-V2
pipeline_tag: text-generation
Qwen3-30B-A3B-Claude-4.5-Opus-High-Reasoning-2507-ABLITERATED-UNCENSORED-V2-qx86-hi-mlx
Brainwaves
arc arc/e boolq hswag obkqa piqa wino
qx86-hi 0.375,0.472,0.687,0.652,0.380,0.778,0.605
Qwen3-30B-A3B-Claude-4.5-Opus-High-Reasoning-2507
qx86-hi 0.398,0.458,0.823,0.650,0.388,0.776,0.618
Qwen3-30B-A3B-Thinking-2507-512k
qx6 0.413,0.449,0.677,0.635,0.392,0.767,0.665
This model Qwen3-30B-A3B-Claude-4.5-Opus-High-Reasoning-2507-ABLITERATED-UNCENSORED-V2-qx86-hi-mlx was
converted to MLX format from DavidAU/Qwen3-30B-A3B-Claude-4.5-Opus-High-Reasoning-2507-ABLITERATED-UNCENSORED-V2
using mlx-lm version 0.30.7.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Qwen3-30B-A3B-Claude-4.5-Opus-High-Reasoning-2507-ABLITERATED-UNCENSORED-V2-qx86-hi-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, return_dict=False,
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)