Qwen3-0.6B-MLX-bf16

Name: Qwen3-0.6B-MLX-bf16
Author: Qwen

436

600M

1 language

license:apache-2.0

Qwen

Language Model

OTHER

0.6B params

New

436 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

2GB+ RAM

Mobile

Laptop

Server

Quick Summary

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

1GB+ RAM

Code Examples

bash

pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

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pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

bash

pip install --upgrade transformers mlx_lm

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-0.6B-MLX-bf16")
prompt = "Hello, please introduce yourself and tell me what you can do."

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,
    max_tokens=1024
)

print(response)

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