marcelbinz/Psych-101
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How to use HillPhelmuth/Llama-3.1-Minitaur-8B-mlx-4Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Llama-3.1-Minitaur-8B-mlx-4Bit HillPhelmuth/Llama-3.1-Minitaur-8B-mlx-4Bit
How to use HillPhelmuth/Llama-3.1-Minitaur-8B-mlx-4Bit with Unsloth Studio:
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for HillPhelmuth/Llama-3.1-Minitaur-8B-mlx-4Bit to start chatting
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for HillPhelmuth/Llama-3.1-Minitaur-8B-mlx-4Bit to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for HillPhelmuth/Llama-3.1-Minitaur-8B-mlx-4Bit to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="HillPhelmuth/Llama-3.1-Minitaur-8B-mlx-4Bit",
max_seq_length=2048,
)The Model HillPhelmuth/Llama-3.1-Minitaur-8B-mlx-4Bit was converted to MLX format from marcelbinz/Llama-3.1-Minitaur-8B using mlx-lm version 0.22.3.
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("HillPhelmuth/Llama-3.1-Minitaur-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)
4-bit
Base model
marcelbinz/Llama-3.1-Minitaur-8B