license: apache-2.0
datasets:
- TeichAI/claude-4.5-opus-high-reasoning-250x
language: - en
library_name: transformers
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
- mlx-my-repo
base_model: DavidAU/Qwen3-30B-A3B-Claude-4.5-Opus-High-Reasoning-2507-ABLITERATED-UNCENSORED-V2
pipeline_tag: text-generation
nrlmuojb/Qwen3-30B-A3B-Claude-4.5-Opus-High-Reasoning-2507-ABLITERATED-UNCENSORED-V2-mlx-6Bit
The Model nrlmuojb/Qwen3-30B-A3B-Claude-4.5-Opus-High-Reasoning-2507-ABLITERATED-UNCENSORED-V2-mlx-6Bit was converted to MLX format from DavidAU/Qwen3-30B-A3B-Claude-4.5-Opus-High-Reasoning-2507-ABLITERATED-UNCENSORED-V2 using mlx-lm version 0.29.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("nrlmuojb/Qwen3-30B-A3B-Claude-4.5-Opus-High-Reasoning-2507-ABLITERATED-UNCENSORED-V2-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)