license: apache-2.0 base_model: DavidAU/Llama-3.2-PKD-Deckard-Almost-Human-abliterated-uncensored-7B datasets:

DavidAU/The-works-PK-Dick language:
en pipeline_tag: text-generation tags:
programming
code generation
code
coding
coder
chat
brainstorm
llama 3.2
llama
llama-3.2
brainstorm 40x
all uses cases
finetune
unsloth
not-for-all-audiences
mlx library_name: mlx

Llama-3.2-PKD-Deckard-Almost-Human-abliterated-uncensored-7B-qx86-hi-mlx

This model Llama-3.2-PKD-Deckard-Almost-Human-abliterated-uncensored-7B-qx86-hi-mlx was converted to MLX format from DavidAU/Llama-3.2-PKD-Deckard-Almost-Human-abliterated-uncensored-7B using mlx-lm version 0.28.0.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Llama-3.2-PKD-Deckard-Almost-Human-abliterated-uncensored-7B-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
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate model, tokenizer = load("Llama-3.2-PKD-Deckard-Almost-Human-abliterated-uncensored-7B-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 ) response = generate(model, tokenizer, prompt=prompt, verbose=True)