Text Generation
MLX
Safetensors
qwen2
chat
conversational
Eval Results
4-bit precision

mlx-community/magnum-v2-72b-4bit

The Model mlx-community/magnum-v2-72b-4bit was converted to MLX format from anthracite-org/magnum-v2-72b using mlx-lm version 0.20.4.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/magnum-v2-72b-4bit")

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)
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Qwen/Qwen2-72B
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Datasets used to train mlx-community/magnum-v2-72b-4bit

Evaluation results