Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit

90
235.0B
1 language
license:apache-2.0
by
Wwayu
Language Model
OTHER
235B params
New
90 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
526GB+ RAM
Mobile
Laptop
Server
Quick Summary

The Model Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit was converted to MLX format from chutesai/Qwen3-235B-A22B-Instruct-2507-1M using mlx-lm version 0.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
219GB+ RAM

Code Examples

Use with mlxbash
pip install mlx-lm
Use with mlxbash
pip install mlx-lm
Use with mlxbash
pip install mlx-lm
Use with mlxbash
pip install mlx-lm
Use with mlxbash
pip install mlx-lm
Use with mlxbash
pip install mlx-lm
Use with mlxbash
pip install mlx-lm
Use with mlxbash
pip install mlx-lm
Use with mlxbash
pip install mlx-lm
Use with mlxbash
pip install mlx-lm
Use with mlxbash
pip install mlx-lm
Use with mlxbash
pip install mlx-lm
Use with mlxbash
pip install mlx-lm
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit")

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit")

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit")

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit")

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit")

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit")

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit")

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit")

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit")

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit")

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit")

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit")

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/Qwen3-235B-A22B-Instruct-2507-1M-mlx-2Bit")

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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