Yi-6B-Chat-4bit / README.md
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metadata
license: apache-2.0
widget:
  - example_title: Yi-34B-Chat
    text: hi
    output:
      text: ' Hello! How can I assist you today?'
  - example_title: Yi-34B
    text: >-
      There's a place where time stands still. A place of breath taking wonder,
      but also
    output:
      text: >2-
         an eerie sense that something is just not right…
        Between the two worlds lies The Forgotten Kingdom - home to creatures
        long since thought extinct and ancient magic so strong it defies belief!
        Only here can you find what has been lost for centuries: An Elixir Of
        Life which will restore youth and vitality if only those who seek its
        power are brave enough to face up against all manner of dangers lurking
        in this mysterious land! But beware; some say there may even exist
        powerful entities beyond our comprehension whose intentions towards
        humanity remain unclear at best ---- they might want nothing more than
        destruction itself rather then anything else from their quest after
        immortality (and maybe someone should tell them about modern medicine)?
        In any event though  one thing remains true regardless : whether or not
        success comes easy depends entirely upon how much effort we put into
        conquering whatever challenges lie ahead along with having faith deep
        down inside ourselves too ;) So let’s get started now shall We?
pipeline_tag: text-generation
base_model: 01-ai/Yi-6B-Chat
tags:
  - mlx

mlx-community/Yi-6B-Chat-4bit

The Model mlx-community/Yi-6B-Chat-4bit was converted to MLX format from 01-ai/Yi-6B-Chat using mlx-lm version 0.19.2.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Yi-6B-Chat-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)