Text Generation
PEFT
Safetensors
Nuosu
Chinese
English
qwen3
lora
nuosu
liangshan-yi
machine-translation
low-resource-language
academic-research
conversational
Instructions to use NiceAsiv/Qwen3-1.7B-Nuosu-MT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use NiceAsiv/Qwen3-1.7B-Nuosu-MT with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "NiceAsiv/Qwen3-1.7B-Nuosu-MT") - Notebooks
- Google Colab
- Kaggle
File size: 1,290 Bytes
797e067 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"schema": "nuosu_model_release/1.0",
"release": "qwen3-1.7b-nuosu-mt-v20260809",
"base_model": "Qwen/Qwen3-1.7B",
"base_revision": "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e",
"training_code_revision": "90b7d6c3d71e025e1336a2a585389f1dedab9b6f",
"chat_entrypoint_revision": "3c7f17f012e5a483c367ff7b5b16e905e1b2c7dd",
"dataset_projection": "nuosu-mt-clean-recover-v20260808",
"seed": 42,
"training": {
"train_records": 159083,
"validation_records": 7131,
"research_test_records": 8558,
"epochs": 1,
"dtype": "bfloat16",
"lora_rank": 64,
"lora_alpha": 128,
"lora_dropout": 0.05,
"target_modules": "all-linear",
"completion_only_loss": true
},
"tokenizer": {
"before_vocab_size": 151669,
"after_vocab_size": 152872,
"added_tokens": 1203,
"added_tokens_sha256": "74930a7511a9e4d342b5c1c036196c0771701eac01f03be60683672bce318dac"
},
"evaluation": {
"full_exact_match": 0.036808,
"full_compact_exact_match": 0.037626,
"full_chrf2": 10.9349,
"full_yi_exact_match": 0.025783,
"gate_exact_match": 0.574219,
"gate_chrf2": 60.6771,
"gate_yi_exact_match": 0.484375,
"gate_status": "waived_strict_gate_failure"
},
"institution": "Xi'an Jiaotong University",
"author": "Wuhe Axi"
}
|