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TimeLens 7B Mlx 4Bit

bsd-3-clause

KindText modelBase modelTencentARC/TimeLens-7BVersionmodelCgrade

Model source

Kind
Text model
File size
3.99 GB
Base model
TencentARC/TimeLens-7B
Version
vmodel
Updated
Oct 21, 2026
Source
Hugging Face

Source description

JungleGym/TimeLens-7B-mlx-4Bit

The Model JungleGym/TimeLens-7B-mlx-4Bit was converted to MLX format from TencentARC/TimeLens-7B using mlx-lm version 0.28.3.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("JungleGym/TimeLens-7B-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)

Sources

1 source
Hugging FacePrimaryVersion 427230469e7e65d7c4e48f5d897e1234f319f463
huggingface.co/JungleGym/TimeLens-7B-mlx-4Bit/tree/427230469e7e65d7c4e48f5d897e1234f319f463

Verified Aug 7

Model artifacts

1 artifact

model.safetensors

safetensors · 3.99 GB · SHA-256 c52fc7e8be1b…f5e5 · Hugging Face

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Source excerpts

2 excerpts

--- license: other license_name: bsd-3-clause license_link: https://github.com/TencentARC/TimeLens/blob/main/LICENSE language: - en tags: - video-grounding - temporal-grounding - video-understanding - qwen2-vl - mlx - mlx-my-repo library_name: transformers pipeline_tag: video-text-to-text datasets: - TencentARC/TimeLens-100K - TencentARC/TimeLens-Bench base_model: TencentARC/TimeLens-7B --- # JungleGym/TimeLens-7B-mlx-4Bit The Model [JungleGym/TimeLens-7B-mlx-4Bit](https://huggingface.co/JungleGym/TimeLens-7B-mlx-4Bit) was converted to MLX format from [TencentARC/TimeLens-7B](https://huggingface.co/TencentARC/TimeLens-7B) using mlx-lm version **0.28.3**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("JungleGym/TimeLens-7B-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) ```

Source context: 4 downloads · 0 likes · Pipeline video-text-to-text · Library transformers · Repo JungleGym/TimeLens-7B-mlx-4Bit