ProCreations's picture
download
raw
2.6 kB
#!/usr/bin/env bash
set -euo pipefail
export HF_ENDPOINT=https://hf-mirror.com
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
source "$SCRIPT_DIR/../common_env.sh"
# TwoTower-BERT LoRA setting:
# - Training remains TwoTower + in-batch InfoNCE.
# - BERT encoder is updated online through lightweight LoRA adapters.
DEVICE="${DEVICE:-cuda:4}"
EPOCHS="${EPOCHS:-5}"
BATCH_SIZE="${BATCH_SIZE:-256}"
LR="${LR:-1e-3}"
TUNE_MODE="${TUNE_MODE:-lora}"
PRETRAINED_MODEL="${PRETRAINED_MODEL:-path_to/models/distilbert-base-uncased}"
POOLING="${POOLING:-cls}"
MAX_LEN="${MAX_LEN:-128}"
ENCODE_BATCH_SIZE="${ENCODE_BATCH_SIZE:-256}"
HID="${HID:-256}"
TEMPERATURE="${TEMPERATURE:-0.07}"
EVAL_CHUNK="${EVAL_CHUNK:-8192}"
TOPK="${TOPK:-10}"
AMP="${AMP:-0}"
UNFREEZE_LAST_N="${UNFREEZE_LAST_N:-2}"
UNFREEZE_EMB="${UNFREEZE_EMB:-1}"
GRAD_CKPT="${GRAD_CKPT:-0}"
ENCODER_LR="${ENCODER_LR:-5e-5}"
ENCODER_WEIGHT_DECAY="${ENCODER_WEIGHT_DECAY:-0.01}"
LORA_R="${LORA_R:-8}"
LORA_ALPHA="${LORA_ALPHA:-16}"
LORA_DROPOUT="${LORA_DROPOUT:-0.1}"
LORA_TARGETS="${LORA_TARGETS:-q_lin,k_lin,v_lin,out_lin}"
USE_TOOL_ID_EMB="${USE_TOOL_ID_EMB:-${USE_TOOL_EMB:-1}}"
USE_LLM_ID_EMB="${USE_LLM_ID_EMB:-${USE_AGENT_ID_EMB:-1}}"
USE_MODEL_CONTENT_VECTOR="${USE_MODEL_CONTENT_VECTOR:-1}"
USE_TOOL_CONTENT_VECTOR="${USE_TOOL_CONTENT_VECTOR:-1}"
USE_QUERY_ID_EMB="${USE_QUERY_ID_EMB:-0}"
USE_AGENT_ID_EMB="${USE_AGENT_ID_EMB:-0}"
SOFT_EVAL="${SOFT_EVAL:-0}"
python "$SCRIPT_DIR/../../run_twotower_bert.py" \
--eval_cand_size "$EVAL_CAND_SIZE" \
--data_root "$DATA_ROOT" \
--device "$DEVICE" \
--epochs "$EPOCHS" \
--batch_size "$BATCH_SIZE" \
--lr "$LR" \
--pretrained_model "$PRETRAINED_MODEL" \
--max_len "$MAX_LEN" \
--pooling "$POOLING" \
--encode_batch_size "$ENCODE_BATCH_SIZE" \
--hid "$HID" \
--temperature "$TEMPERATURE" \
--topk "$TOPK" \
--eval_chunk "$EVAL_CHUNK" \
--amp "$AMP" \
--tune_mode "$TUNE_MODE" \
--unfreeze_last_n "$UNFREEZE_LAST_N" \
--unfreeze_emb "$UNFREEZE_EMB" \
--grad_ckpt "$GRAD_CKPT" \
--lora_r "$LORA_R" \
--lora_alpha "$LORA_ALPHA" \
--lora_dropout "$LORA_DROPOUT" \
--lora_targets "$LORA_TARGETS" \
--encoder_lr "$ENCODER_LR" \
--encoder_weight_decay "$ENCODER_WEIGHT_DECAY" \
--use_tool_id_emb "$USE_TOOL_ID_EMB" \
--use_llm_id_emb "$USE_LLM_ID_EMB" \
--use_model_content_vector "$USE_MODEL_CONTENT_VECTOR" \
--use_tool_content_vector "$USE_TOOL_CONTENT_VECTOR" \
--use_query_id_emb "$USE_QUERY_ID_EMB" \
--use_agent_id_emb "$USE_AGENT_ID_EMB" \
--exp_name "twotower_bert_${TUNE_MODE}" \
--soft_eval "$SOFT_EVAL"

Xet Storage Details

Size:
2.6 kB
·
Xet hash:
c8a88f8892725959504ff7d876fc1224510871ecf753f4464115ac56b399aca2

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.