Instructions to use tuochao/Llama-3.2-1B-Proactive-Classifier-Aug-mlx-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tuochao/Llama-3.2-1B-Proactive-Classifier-Aug-mlx-fp16 with Transformers:
# Load model directly from transformers import LlamaForCausalLM_TokenClassifcation model = LlamaForCausalLM_TokenClassifcation.from_pretrained("tuochao/Llama-3.2-1B-Proactive-Classifier-Aug-mlx-fp16", dtype="auto") - MLX
How to use tuochao/Llama-3.2-1B-Proactive-Classifier-Aug-mlx-fp16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Llama-3.2-1B-Proactive-Classifier-Aug-mlx-fp16 tuochao/Llama-3.2-1B-Proactive-Classifier-Aug-mlx-fp16
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
tuochao/Llama-3.2-1B-Proactive-Classifier-Aug-mlx-fp16
The Model tuochao/Llama-3.2-1B-Proactive-Classifier-Aug-mlx-fp16 was converted to MLX format from tuochao/Llama-3.2-1B-Proactive-Classifier-Aug using mlx-lm version 0.21.0.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("tuochao/Llama-3.2-1B-Proactive-Classifier-Aug-mlx-fp16")
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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Model size
1B params
Tensor type
BF16
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