Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit

235
1
4.0B
1 language
llama
by
hsefz-ChenJunJie
Language Model
OTHER
4B params
New
235 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
9GB+ RAM
Mobile
Laptop
Server
Quick Summary

hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit The Model hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit was converted to MLX format from merg...

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
4GB+ 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 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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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("hsefz-ChenJunJie/Deepseek-R1-Distill-NSFW-RPv1-mlx-4Bit")

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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