Instructions to use nhe-ai/Llasa-1B-Multilingual-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nhe-ai/Llasa-1B-Multilingual-mlx-4Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Llasa-1B-Multilingual-mlx-4Bit nhe-ai/Llasa-1B-Multilingual-mlx-4Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)
nhe-ai/Llasa-1B-Multilingual-mlx-4Bit
The Model nhe-ai/Llasa-1B-Multilingual-mlx-4Bit was converted to MLX format from HKUSTAudio/Llasa-1B-Multilingual using mlx-lm version 0.22.3.
⚠️ Important: This model was automatically converted for experimentation. The following guide was not designed for this model and may not work as expected. Do not expect to function out of the box. Use at your own experimentation.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("nhe-ai/Llasa-1B-Multilingual-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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Model size
0.2B params
Tensor type
F16
·
U32 ·
Hardware compatibility
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4-bit
Model tree for nhe-ai/Llasa-1B-Multilingual-mlx-4Bit
Base model
meta-llama/Llama-3.2-1B-Instruct Finetuned
HKUSTAudio/Llasa-1B-Multilingual