Instructions to use Alogotron/GameTheory-Solver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alogotron/GameTheory-Solver with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "Alogotron/GameTheory-Solver")

Transformers

How to use Alogotron/GameTheory-Solver with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Alogotron/GameTheory-Solver", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Alogotron/GameTheory-Solver with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Alogotron/GameTheory-Solver

SGLang

How to use Alogotron/GameTheory-Solver 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 "Alogotron/GameTheory-Solver" \
    --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": "Alogotron/GameTheory-Solver",
		"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 "Alogotron/GameTheory-Solver" \
        --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": "Alogotron/GameTheory-Solver",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Alogotron/GameTheory-Solver with Docker Model Runner:
```
docker model run hf.co/Alogotron/GameTheory-Solver
```

GameTheory-Solver / training_stats.json

2reb

Upload GameTheory-Solver QLoRA adapter with evaluation results

eb51fe2 verified 6 months ago

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	{
	"base_model": "Qwen/Qwen2.5-7B-Instruct",
	"dataset": "2reb/GameTheory-Bench",
	"train_examples": 2767,
	"eval_examples": 146,
	"lora_r": 64,
	"lora_alpha": 128,
	"epochs": 3,
	"batch_size": 2,
	"grad_accum": 8,
	"effective_batch": 16,
	"lr": 0.0002,
	"train_loss": 0.1613485331888552,
	"eval_loss": 0.08727391809225082,
	"runtime_seconds": 6895.8492
	}