Instructions to use darthcrawl/Bond-24B-v1.0-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use darthcrawl/Bond-24B-v1.0-MLX-4bit 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("darthcrawl/Bond-24B-v1.0-MLX-4bit") 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
- MLX LM
How to use darthcrawl/Bond-24B-v1.0-MLX-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "darthcrawl/Bond-24B-v1.0-MLX-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "darthcrawl/Bond-24B-v1.0-MLX-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "darthcrawl/Bond-24B-v1.0-MLX-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
Bond v1 — first-person character voice (MLX 4-bit)
MLX 4-bit quantization (affine, group size 64) of darthcrawl/bond,
for Apple Silicon (mlx-lm / LM Studio). ~12 GB.
Bond is a first-person character-voice fine-tune of Mistral-Small-3.1-24B-Instruct: it holds a committed in-character first-person voice instead of leaking back to helpful-assistant mode. Uncensored, explicit-capable. Eval, training, and the design goal are on the base model card.
This quant
Smallest. Comfortable on a 24GB Mac (tight on 16GB). Minor quality cost vs bf16 — fine for most use.
Other MLX sizes: 6-bit, 8-bit. (4-bit smallest, 6-bit near-lossless, 8-bit lossless.)
Use
from mlx_lm import load, generate
model, tok = load("darthcrawl/bond-mlx-4bit")
prompt = tok.apply_chat_template(
[{"role": "user", "content": "Tell me about the worst night of your life."}],
add_generation_prompt=True,
)
print(generate(model, tok, prompt=prompt, max_tokens=512, verbose=True))
Or the CLI: mlx_lm.generate --model darthcrawl/bond-mlx-4bit --prompt "...".
- Downloads last month
- 14
4-bit
Model tree for darthcrawl/Bond-24B-v1.0-MLX-4bit
Base model
mistralai/Mistral-Small-3.1-24B-Base-2503