This repository contains a LoRA adapter for Qwen3-8B, fine-tuned on the PokerBench dataset for poker-related text generation and reasoning.

Model Details

Model Description

This is a poker-domain PEFT / LoRA adapter trained on top of Qwen3-8B using supervised fine-tuning (SFT).
It is intended to improve poker-related responses, including strategy discussion, hand analysis, poker terminology, and decision reasoning.

Model type: LoRA adapter for a causal language model
Base model: Qwen/Qwen3-8B
Adapter format: PEFT adapter weights only
Fine-tuning method: LoRA / SFT
Libraries: Unsloth, PEFT, TRL, Transformers

How to Get Started with the Model

This repository contains adapter weights only.
Load the base model first, then attach the adapter.

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base_model_name = "Qwen/Qwen3-8B"
adapter_name = "your-username/your-adapter-repo"

tokenizer = AutoTokenizer.from_pretrained(base_model_name)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.float16,
    device_map="auto",
)

model = PeftModel.from_pretrained(base_model, adapter_name)

prompt = "You are on the button with AKo facing an open raise. What factors matter most?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Dominicvdb/pokerqwen

Model Card for Model ID

PokerBench Qwen3-8B LoRA Adapter

Model Details

Model Description

How to Get Started with the Model

Dominicvdb/
pokerqwen