ModelÜberprüftExtern

Janhq Jan v3.5 4B MLX 4bit

This model schnow265/janhqJan-v3.5-4B-MLX-4bit was converted to MLX format from janhq/Jan-v3.5-4B using mlx-lm version 0.31.3.

Quellehuggingface.co/schnow265/janhq_Jan-v3.5-4B-MLX-4bit/tree/82db087bd738e5eb72d91eb652d47a9a0bbe15a7

ArtText modelBasismodelljanhq/Jan-v3.5-4BVersionmodelVerleger@schnow265CBewertung

Modellquelle

Art: Text model
Dateigröße: 2,11 GB
Basismodell: janhq/Jan-v3.5-4B
Version: vmodel
Aktualisiert: 3. Okt. 2026
Verleger: @schnow265
Downloads: 35
Parameter: 4B
Aufgaben: Textgenerierung
Quelle: Hugging Face

Quellenbeschreibung

schnow265/janhq_Jan-v3.5-4B-MLX-4bit

This model schnow265/janhq_Jan-v3.5-4B-MLX-4bit was converted to MLX format from janhq/Jan-v3.5-4B using mlx-lm version 0.31.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("schnow265/janhq_Jan-v3.5-4B-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, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)

Quellen

1 Quelle

Hugging FacePrimärVersion 82db087bd738e5eb72d91eb652d47a9a0bbe15a7

huggingface.co/schnow265/janhq_Jan-v3.5-4B-MLX-4bit/tree/82db087bd738e5eb72d91eb652d47a9a0bbe15a7

Verifiziert 3. Okt.

Modellartefakte

1 Artefakt

model.safetensors

safetensors · 2,11 GB · SHA-256 d1edff0cbd2c…2662 · Hugging Face

Herunterladen

Quellenauszüge

3 Auszüge

Readme11. Julihuggingface.co/schnow265/janhq_Jan-v3.5-4B-MLX-4bit

--- license: apache-2.0 language: - en base_model: janhq/Jan-v3.5-4B pipeline_tag: text-generation library_name: mlx tags: - math - identity - mlx --- # schnow265/janhq_Jan-v3.5-4B-MLX-4bit This model [schnow265/janhq_Jan-v3.5-4B-MLX-4bit](https://huggingface.co/schnow265/janhq_Jan-v3.5-4B-MLX-4bit) was converted to MLX format from [janhq/Jan-v3.5-4B](https://huggingface.co/janhq/Jan-v3.5-4B) using mlx-lm version **0.31.3**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("schnow265/janhq_Jan-v3.5-4B-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, return_dict=False, ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

Note3. Okt.huggingface.co/schnow265/janhq_Jan-v3.5-4B-MLX-4bit

Source context: 35 downloads · 0 likes · Pipeline text-generation · Library mlx · Repo schnow265/janhq_Jan-v3.5-4B-MLX-4bit

Note17. Julihuggingface.co/schnow265/janhq_Jan-v3.5-4B-MLX-4bit

Source context: 50 downloads · 0 likes · Pipeline text-generation · Library mlx · Repo schnow265/janhq_Jan-v3.5-4B-MLX-4bit