DeepSeek-R1-Distill-Qwen-32B-MLX-4bits

4
1 language
license:mit
by
moot20
Language Model
OTHER
32B params
New
4 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
72GB+ RAM
Mobile
Laptop
Server
Quick Summary

The Model moot20/DeepSeek-R1-Distill-Qwen-32B-MLX-4bits was converted to MLX format from deepseek-ai/DeepSeek-R1-Distill-Qwen-32B using mlx-lm version 0.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
30GB+ 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 mlxpython
from mlx_lm import load, generate

model, tokenizer = load("moot20/DeepSeek-R1-Distill-Qwen-32B-MLX-4bits")

prompt = "hello"

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("moot20/DeepSeek-R1-Distill-Qwen-32B-MLX-4bits")

prompt = "hello"

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("moot20/DeepSeek-R1-Distill-Qwen-32B-MLX-4bits")

prompt = "hello"

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("moot20/DeepSeek-R1-Distill-Qwen-32B-MLX-4bits")

prompt = "hello"

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("moot20/DeepSeek-R1-Distill-Qwen-32B-MLX-4bits")

prompt = "hello"

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("moot20/DeepSeek-R1-Distill-Qwen-32B-MLX-4bits")

prompt = "hello"

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)
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("moot20/DeepSeek-R1-Distill-Qwen-32B-MLX-4bits")

prompt = "hello"

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)

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