mlx-community/gte-Qwen2-7B-instruct-4bit-DWQ
This model mlx-community/gte-Qwen2-7B-instruct-4bit-DWQ was converted to MLX format from Alibaba-NLP/gte-Qwen2-7B-instruct using mlx-lm version 0.24.0.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/gte-Qwen2-7B-instruct-4bit-DWQ")
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)
- Downloads last month
- 56
Inference Providers
NEW
This model isn't deployed by any Inference Provider.
🙋
Ask for provider support
Model tree for mlx-community/gte-Qwen2-7B-instruct-4bit-DWQ
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