Instructions to use Dominicvdb/pokerqwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dominicvdb/pokerqwen with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-8b-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "Dominicvdb/pokerqwen")

Transformers

How to use Dominicvdb/pokerqwen with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Dominicvdb/pokerqwen")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Dominicvdb/pokerqwen", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Dominicvdb/pokerqwen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dominicvdb/pokerqwen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dominicvdb/pokerqwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dominicvdb/pokerqwen

SGLang

How to use Dominicvdb/pokerqwen with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Dominicvdb/pokerqwen" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dominicvdb/pokerqwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Dominicvdb/pokerqwen" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dominicvdb/pokerqwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Dominicvdb/pokerqwen with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Dominicvdb/pokerqwen to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Dominicvdb/pokerqwen to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Dominicvdb/pokerqwen to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Dominicvdb/pokerqwen",
    max_seq_length=2048,
)

Docker Model Runner
How to use Dominicvdb/pokerqwen with Docker Model Runner:
```
docker model run hf.co/Dominicvdb/pokerqwen
```

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.

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))

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