Translation
Transformers
Safetensors
MLX
hunyuan_v1_dense
text-generation
mlx-my-repo
4-bit precision
Instructions to use windht/HY-MT1.5-1.8B-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use windht/HY-MT1.5-1.8B-mlx-4Bit with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="windht/HY-MT1.5-1.8B-mlx-4Bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("windht/HY-MT1.5-1.8B-mlx-4Bit") model = AutoModelForCausalLM.from_pretrained("windht/HY-MT1.5-1.8B-mlx-4Bit") - MLX
How to use windht/HY-MT1.5-1.8B-mlx-4Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir HY-MT1.5-1.8B-mlx-4Bit windht/HY-MT1.5-1.8B-mlx-4Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
windht/HY-MT1.5-1.8B-mlx-4Bit
The Model windht/HY-MT1.5-1.8B-mlx-4Bit was converted to MLX format from tencent/HY-MT1.5-1.8B using mlx-lm version 0.29.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("windht/HY-MT1.5-1.8B-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)
- Downloads last month
- 17
Model size
0.3B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
Log In to add your hardware
4-bit
Model tree for windht/HY-MT1.5-1.8B-mlx-4Bit
Base model
tencent/HY-MT1.5-1.8B