HPAI-BSC/Aloe-Beta-General-Collection
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How to use mlx-community/Qwen2.5-Aloe-Beta-7B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="mlx-community/Qwen2.5-Aloe-Beta-7B") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("mlx-community/Qwen2.5-Aloe-Beta-7B")
model = AutoModelForCausalLM.from_pretrained("mlx-community/Qwen2.5-Aloe-Beta-7B")How to use mlx-community/Qwen2.5-Aloe-Beta-7B with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen2.5-Aloe-Beta-7B mlx-community/Qwen2.5-Aloe-Beta-7B
The Model mlx-community/Qwen2.5-Aloe-Beta-7B was converted to MLX format from HPAI-BSC/Qwen2.5-Aloe-Beta-7B using mlx-lm version 0.20.1.
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen2.5-Aloe-Beta-7B")
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
HPAI-BSC/Qwen2.5-Aloe-Beta-7B