60d7052f1a
Model: alexgusevski/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning-mlx-fp16 Source: Original Platform
82 lines
1.9 KiB
Markdown
82 lines
1.9 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- TeichAI/claude-4.5-opus-high-reasoning-250x
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base_model: DavidAU/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning
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language:
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- en
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- fr
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- de
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- es
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- it
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- pt
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- zh
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- ja
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- ru
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- ko
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tags:
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- thinking
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- reasoning
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- instruct
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- heretic
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- uncensored
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- abliterated
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- Claude4.5-Opus
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- creative
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- creative writing
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- fiction writing
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- plot generation
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- sub-plot generation
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- story generation
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- scene continue
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- storytelling
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- fiction story
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- science fiction
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- romance
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- all genres
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- story
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- writing
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- vivid prosing
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- vivid writing
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- fiction
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- roleplaying
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- bfloat16
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- role play
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- 128k context
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- llama3.3
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- llama-3
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- llama-3.3
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- unsloth
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- finetune
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- mlx
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- mlx-my-repo
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pipeline_tag: text-generation
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library_name: transformers
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---
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# alexgusevski/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning-mlx-fp16
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The Model [alexgusevski/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning-mlx-fp16](https://huggingface.co/alexgusevski/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning-mlx-fp16) was converted to MLX format from [DavidAU/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning](https://huggingface.co/DavidAU/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning) using mlx-lm version **0.29.1**.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("alexgusevski/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning-mlx-fp16")
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prompt="hello"
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if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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