---
base_model: Qwen/Qwen3-8B
library_name: peft
pipeline_tag: text-generation
tags:
- qwen3
- peft
- lora
- sft
- transformers
- trl
- unsloth
- poker
---

# Model Card for Model ID

# PokerBench Qwen3-8B LoRA Adapter

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))