Qwen/Qwen3-Embedding-0.6B · Training supported

Qwen3 embedding models can be fine-tuned by SWIFT:

pip install ms-swift -U

INFONCE_MASK_FAKE_NEGATIVE=true \
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
NPROC_PER_NODE=8 \
swift sft \
    --model Qwen/Qwen3-Embedding-0.6B \
    --task_type embedding \
    --model_type qwen3_emb \
    --train_type full \
    --dataset sentence-transformers/stsb:positive \
    --split_dataset_ratio 0.05 \
    --eval_strategy steps \
    --output_dir output \
    --eval_steps 20 \
    --num_train_epochs 5 \
    --save_steps 70 \
    --per_device_train_batch_size 4 \
    --per_device_eval_batch_size 4 \
    --gradient_accumulation_steps 4 \
    --learning_rate 6e-6 \
    --loss_type infonce \
    --label_names labels \
    --dataloader_drop_last true \
    --deepspeed zero3

We uses --loss_type infonce, which is also the loss type used in training the original model. Other loss types can also be used, such as --loss_type cosine_similarity. InfoNCE loss is a contrastive learning loss. In the above script, we default to treating different samples as negative examples, which can be controlled through the INFONCE_USE_BATCH environment variable, which defaults to True. The above script has an additional environment variable: INFONCE_MASK_FAKE_NEGATIVE=true, which ignores negative examples with excessively high similarity values (e.g., negative examples with similarity higher than positive example similarity + 0.1), preventing interference from dataset duplication or false negative issues during training.

The dataset format corresponding to InfoNCE loss is as follows:

{"query": "sentence1", "response": "sentence1-pos", "rejected_response": ["sentence1-neg1", "sentence1-neg2"]}

The negative examples of this sample and the positive and negative examples of other samples will all be used as negative examples for this sample.

Documentation here:

https://swift.readthedocs.io/en/latest/BestPractices/Embedding.html