DeepSeek-R1-Distill-Qwen-1.5B-MLX-8bits

Name: DeepSeek-R1-Distill-Qwen-1.5B-MLX-8bits
Author: moot20

1 language

license:mit

moot20

Language Model

OTHER

1.5B params

New

3 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

4GB+ RAM

Mobile

Laptop

Server

Quick Summary

The Model moot20/DeepSeek-R1-Distill-Qwen-1.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

2GB+ 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 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("moot20/DeepSeek-R1-Distill-Qwen-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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-1.5B-MLX-8bits")

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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