bitext/Bitext-customer-support-llm-chatbot-training-dataset
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How to use OldEngine/qwen3-0.6b-bitext-ticket-router-sft with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B")
model = PeftModel.from_pretrained(base_model, "OldEngine/qwen3-0.6b-bitext-ticket-router-sft")LoRA SFT adapter for routing customer-support tickets to the top 12 intents
by frequency from the first 50000 rows of bitext/Bitext-customer-support-llm-chatbot-training-dataset.
The training prompt enforces strict JSON-only output:
system: You are a customer-support ticket router. Return only one strict JSON object...
user: <ticket text>
assistant: {"intent":"<label>","confidence":0.99,"reason":"<short reason>"}
Held-out eval examples: 1000
{
"accuracy": 0.999,
"valid_json_rate": 1.0,
"schema_pass_rate": 1.0,
"accuracy_on_schema": 0.999,
"num_eval_examples": 1000,
"num_train_examples": 10793,
"top_intents": [
"check_invoice",
"complaint",
"contact_customer_service",
"edit_account",
"switch_account",
"check_payment_methods",
"contact_human_agent",
"delivery_period",
"get_invoice",
"newsletter_subscription",
"payment_issue",
"registration_problems"
],
"base_model": "Qwen/Qwen3-0.6B",
"dataset": "bitext/Bitext-customer-support-llm-chatbot-training-dataset",
"max_steps": 800,
"learning_rate": 0.0002,
"warmup_ratio": 0.03,
"max_length": 512
}