Instructions to use divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx

Run Hermes

hermes

MLX LM

How to use divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Llama-3.3-70B-Instruct-abliterated 8-bit MLX

An 8-bit MLX quantization of huihui-ai/Llama-3.3-70B-Instruct-abliterated, packaged for fast local inference on Apple Silicon.

8-bit was chosen instead of the more common 4-bit so the quant preserves as much of the base model's quality as possible — meant for users who have the unified-memory headroom and care more about output fidelity than minimum footprint.

Model details

Field	Value
Base model	`huihui-ai/Llama-3.3-70B-Instruct-abliterated`
Quantization	8-bit affine, group size 64
Format	MLX (`safetensors`, 15 shards)
Architecture	Llama 3.3 — 80 layers, 8192 hidden, 64 attention heads / 8 KV heads, 128k context
Disk size	~75 GB
Converted with	`mlx-lm` 0.31.2

Hardware requirements

You need an Apple Silicon Mac with enough unified memory to hold the full model in RAM, plus headroom for the KV cache and your OS:

Will not run on 64 GB Macs — use a 4-bit quant instead.
Minimum: ~80 GB free unified memory (so a 96 GB Ultra or a 128 GB Max).
Comfortable: 128 GB or more, especially if you want long contexts.

Usage

Install mlx-lm:

pip install mlx-lm

Generate from the command line:

mlx_lm.generate \
    --model divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx \
    --prompt "Write a short poem about Apple Silicon." \
    --max-tokens 200

Or load it from Python:

from mlx_lm import load, generate

model, tokenizer = load("divinetribe/Llama-3.3-70B-Instruct-abliterated-8bit-mlx")

messages = [{"role": "user", "content": "Hello, who are you?"}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False
)

response = generate(model, tokenizer, prompt=prompt, max_tokens=256)
print(response)

Credits

Base abliteration by huihui-ai, built on Meta's meta-llama/Llama-3.3-70B-Instruct.
MLX 8-bit conversion by divinetribe using mlx-lm.
For background on the abliteration technique, see Maxime Labonne's write-up on Hugging Face.

License

Inherits the Llama 3.3 Community License from the upstream model. Use of this quant is bound by the same terms and Acceptable Use Policy — accept the gated-access prompt above to access the weights.

Notes

"Abliterated" means the model's built-in refusal direction has been suppressed so it doesn't refuse benign-but-edgy requests. It is not a general capability upgrade — please use it responsibly and within the bounds of the upstream license.

About the author

This model was built by Matt Macosko (@nicedreamzapp) for the claude-code-local stack — run Claude Code 100% on-device with local AI on Apple Silicon (⭐ 2,664 on GitHub).