Qwen3-1.7B-MLX-6bit

Name: Qwen3-1.7B-MLX-6bit
Author: Qwen

136

1.7B

1 language

license:apache-2.0

Qwen

Language Model

OTHER

1.7B params

New

136 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

4GB+ 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

2GB+ RAM

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by Qwen3-1.7B-MLX-6bit with quality assessment

Specialized For

general

multilingual

Training Datasets (1)

🔵 6/10

general

multilingual

Key Strengths

•Scale and Accessibility: 750GB of publicly available, filtered text
•Systematic Filtering: Documented heuristics enable reproducibility
•Language Diversity: Despite English-only, captures diverse writing styles

Considerations

•English-Only: Limits multilingual applications
•Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

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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

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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

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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

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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

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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

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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/Qwen3-1.7B-MLX-6bit")
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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