This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: Qwen/Qwen3-Embedding-0.6B
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'PeftModelForFeatureExtraction'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Mercity/memory-retrieval-qwen3-0.6b-lora")
# Run inference
queries = [
    "Freelancer\u0027s on board, but I\u0027m anxious about the budget stretching for these fixes.",
]
documents = [
    'Victor strongly values authenticity and has explicitly stated he would rather deliver 4 excellent, deeply resonant ads than 6 mediocre ones that rely on viral gimmicks.',
    'Carlos successfully mastered the past tense (Passé Composé) last month by focusing solely on verb conjugation tables rather than contextual sentences.',
    'Raj recently won a $500 gift certificate specifically for experiences/tours redeemable through a regional travel aggregator.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.3463, -0.1885, -0.0302]])

Training Details

Training Dataset

Unnamed Dataset

Size: 369,891 training samples
Columns: sentence_0, sentence_1, and sentence_2
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 sentence_2
type string string string
details
min: 9 tokens
mean: 29.84 tokens
max: 67 tokens
min: 16 tokens
mean: 34.6 tokens
max: 66 tokens
min: 16 tokens
mean: 30.9 tokens
max: 56 tokens

Samples:

sentence_0	sentence_1	sentence_2
`Our budget's stretched thin with the condo mortgage and student loans, yet we're locked into this $4k Ireland trip next summer—am I being irresponsible by not prioritizing career growth over these family splurges?`	`Liam's father, who was rarely present due to travel, recently reconciled with Liam and offered to pay for 100% of Ava's future college tuition, regardless of Liam's career path.`	`Liam is extremely dedicated to his career and has always prioritized professional advancement over personal comfort.`
`I've got $1800 left after flights for my Tokyo conference trip—can you recommend budget vegan hotels close to the venue to keep things simple?`	`Lena's primary networking goal for this trip is securing a mentorship with Kenji Tanaka, a speaker at the conference known for hosting small, private dinners for potential mentees.`	`Lena is intensely private about her financial struggles and views asking for help or discussing debt openly as a personal failure.`
`Inflation's killing my shop, but I wanna surprise Lena with milestone magic on a dime.`	`Last year, Marcus successfully organized a surprise 'Family Fun Day' at a local park for Lena's birthday, which she cited as the best day they'd had since before Jamal got sick, despite being low-cost.`	`Maria recently spent an hour reviewing the history of Mandarin tonal development since the Han Dynasty.`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.COSINE", 3 "triplet_margin": 0.5 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 5
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
bf16: False
fp16: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.0865	500	0.1927
0.1730	1000	0.1023
0.2595	1500	0.0841
0.3460	2000	0.0772
0.4325	2500	0.0704
0.5190	3000	0.0635
0.6055	3500	0.0576
0.6920	4000	0.0545
0.7785	4500	0.0522
0.8651	5000	0.0484
0.9516	5500	0.0465
1.0	5780	-
1.0381	6000	0.0388
1.1246	6500	0.0323
1.2111	7000	0.0318
1.2976	7500	0.0314
1.3841	8000	0.0294
1.4706	8500	0.0301
1.5571	9000	0.0283
1.6436	9500	0.0272
1.7301	10000	0.0247
1.8166	10500	0.0248
1.9031	11000	0.0233
1.9896	11500	0.0228
2.0	11560	-
2.0761	12000	0.0154
2.1626	12500	0.0141
2.2491	13000	0.0146
2.3356	13500	0.0135
2.4221	14000	0.0143
2.5087	14500	0.0139
2.5952	15000	0.014
2.6817	15500	0.0128
2.7682	16000	0.0126
2.8547	16500	0.0122
2.9412	17000	0.0114
3.0	17340	-
3.0277	17500	0.0104
3.1142	18000	0.007
3.2007	18500	0.0069
3.2872	19000	0.0067
3.3737	19500	0.0064
3.4602	20000	0.0067
3.5467	20500	0.0063
3.6332	21000	0.0061
3.7197	21500	0.0058
3.8062	22000	0.0058
3.8927	22500	0.0051
3.9792	23000	0.005
4.0	23120	-
4.0657	23500	0.0035
4.1522	24000	0.0027
4.2388	24500	0.0028
4.3253	25000	0.0025
4.4118	25500	0.0025
4.4983	26000	0.0024

Framework Versions

Python: 3.12.3
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.8.0+cu128
Accelerate: 1.11.0
Datasets: 4.4.1
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

TripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 9 tokens mean: 29.84 tokens max: 67 tokens	min: 16 tokens mean: 34.6 tokens max: 66 tokens	min: 16 tokens mean: 30.9 tokens max: 56 tokens