LRAT Qwen3-Embedding-0.6B — 1 Epoch
This is a full-parameter retriever checkpoint based on
Qwen/Qwen3-Embedding-0.6B, prepared for the CCIR LRAT competition.
Training
- Data: official LRAT training pairs only; no external training data
- Records: 96,504
- Epochs: 1
- Optimizer steps: 12,063
- Hardware: 2 × NVIDIA A40
- Precision: bfloat16
- Train group: 1 positive and 5 negatives
- Global effective query batch: 8
- Query / passage maximum length: 128 / 512
- Learning rate: 1e-6
- Pooling: last token
- Normalized embeddings: yes
- Temperature: 0.02
- Query instruction:
Given a web search query, retrieve relevant passages that answer the query
The training objective uses LRAT reweight_rate values with cross-device
negatives. The checkpoint contains merged full model parameters and does not
require a LoRA or adapter at inference time.
Loading
import torch
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
model_id = "Seraphic663/lrat-qwen3-0.6b-1epoch-20260716"
tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side="left")
model = AutoModel.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
).cuda().eval()
instruction = "Given a web search query, retrieve relevant passages that answer the query"
texts = [f"Instruct: {instruction}\nQuery:example query", "example passage"]
batch = tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors="pt").to("cuda")
with torch.inference_mode():
output = model(**batch).last_hidden_state
embeddings = F.normalize(output[:, -1].float(), p=2, dim=1)
Validation Note
On a fixed 500-row candidate-set development diagnostic, this checkpoint obtained Recall@1/5/10 of 0.672/0.934/0.984 and MRR of 0.78037. These are local candidate-set diagnostics, not official BC-Plus leaderboard scores.
Integrity
model.safetensors
2,383,139,480 bytes
SHA-256 b25b3b08a3199a788ea5e8bc005ee20ab831ada36cb4eccd7ba915e0d6e02501