QVikhr-3-4B-Instruction-MLX_4bit

Name: QVikhr-3-4B-Instruction-MLX_4bit
Author: Vikhrmodels

4.0B

3 languages

license:apache-2.0

Vikhrmodels

Language Model

OTHER

4B params

New

3 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

9GB+ RAM

Mobile

Laptop

Server

Quick Summary

This model Vikhrmodels/QVikhr-3-4B-Instruction-MLX4bit was converted to MLX format from Vikhrmodels/QVikhr-3-4B-Instruction using mlx-lm version 0.

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 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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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("Vikhrmodels/QVikhr-3-4B-Instruction-MLX_4bit")

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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