The Model
Kagandi/Frontis-MA1-30B-mlx-4Bit was converted to MLX format from
FrontisAI/Frontis-MA1-30B using mlx-lm version
0.31.2.
1from mlx_lm import load, generate
2
3model, tokenizer = load("Kagandi/Frontis-MA1-30B-mlx-4Bit")
4
5prompt="hello"
6
7if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
8 messages = [{"role": "user", "content": prompt}]
9 prompt = tokenizer.apply_chat_template(
10 messages, tokenize=False, add_generation_prompt=True
11 )
12
13response = generate(model, tokenizer, prompt=prompt, verbose=True)
