BillSYZhang/gte-Qwen2-7B-instruct-Q4-mlx
The Model BillSYZhang/gte-Qwen2-7B-instruct-Q4-mlx was converted to MLX format from Alibaba-NLP/gte-Qwen2-7B-instruct using mlx-lm version 0.20.5.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("BillSYZhang/gte-Qwen2-7B-instruct-Q4-mlx")
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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Base model
Alibaba-NLP/gte-Qwen2-7B-instructEvaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported91.313
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported67.643
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported87.534
- accuracy on MTEB AmazonPolarityClassificationtest set self-reported97.498
- ap on MTEB AmazonPolarityClassificationtest set self-reported96.303
- f1 on MTEB AmazonPolarityClassificationtest set self-reported97.498
- accuracy on MTEB AmazonReviewsClassification (en)test set self-reported62.564
- f1 on MTEB AmazonReviewsClassification (en)test set self-reported60.976
- map_at_1 on MTEB ArguAnatest set self-reported36.486
- map_at_10 on MTEB ArguAnatest set self-reported54.842