Dedicated Endpoints
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Container
Run this model inference with full control and performance in your environment.
Get help setting up a custom Dedicated Endpoints.
Talk with our engineer to get a quote for reserved GPU instances with discounts.
README
Task
Given a listing context, buyer/seller profiles, and negotiation history, predict the next turn:
- : who speaks (buyer/seller)
- : one of offer, offer_with_message, accept, decline, message_only, no_response
- : float or null
- : list of strategy labels
- : string or null
Eval (val split, n=1263)
| Metric | Value |
|---|---|
| Format OK | 99.8% |
| Actor accuracy | 97.8% |
| Action accuracy | 65.7% |
| Price accuracy | 37.4% |
| Label F1 | 0.651 |
Usage
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B")model = PeftModel.from_pretrained(base, "xinyang-hu-00/negotiation-qwen3-4b-lora")tokenizer = AutoTokenizer.from_pretrained("xinyang-hu-00/negotiation-qwen3-4b-lora")
Model provider
xinyang-hu-00
Model tree
Base
Qwen/Qwen3-4B
Adapter
this model
Modalities
Input
Text
Output
Text
Pricing
Dedicated Endpoints
View detailsSupported Functionality
Model APIs
Dedicated Endpoints
Container
More information