Text Generation
Transformers
Safetensors
qwen2
godot
gdscript
qlora
game-development
conversational
text-generation-inference
Instructions to use khursheed/VibeGame-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khursheed/VibeGame-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="khursheed/VibeGame-8B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("khursheed/VibeGame-8B") model = AutoModelForCausalLM.from_pretrained("khursheed/VibeGame-8B", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use khursheed/VibeGame-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "khursheed/VibeGame-8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "khursheed/VibeGame-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/khursheed/VibeGame-8B
- SGLang
How to use khursheed/VibeGame-8B 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 "khursheed/VibeGame-8B" \ --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": "khursheed/VibeGame-8B", "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 "khursheed/VibeGame-8B" \ --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": "khursheed/VibeGame-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use khursheed/VibeGame-8B with Docker Model Runner:
docker model run hf.co/khursheed/VibeGame-8B
Download code/tests/test_data.py from khursheed/VibeGame-8B: direct link, hf CLI and curl.
- Browser
- Download file 7.72 kB
-
https://huggingface.co/khursheed/VibeGame-8B/resolve/main/code/tests/test_data.py
- Command line
-
hf download hf://khursheed/VibeGame-8B/code/tests/test_data.py
-
curl -L -o test_data.py https://huggingface.co/khursheed/VibeGame-8B/resolve/main/code/tests/test_data.py
7.72 kB
| """Data contract tests; these never start Godot or require network access.""" | |
| import importlib.util | |
| import json | |
| from pathlib import Path | |
| import sys | |
| import tempfile | |
| import unittest | |
| ROOT = Path(__file__).resolve().parents[1] | |
| sys.path.insert(0, str(ROOT / "src")) | |
| from vibegame.data import (Deduper, canonical_output, has_conflicting_header, | |
| identify_license, read_jsonl, sha256, write_jsonl) | |
| spec = importlib.util.spec_from_file_location("build_dataset", ROOT / "scripts/build_dataset.py") | |
| builder = importlib.util.module_from_spec(spec) | |
| spec.loader.exec_module(builder) | |
| def bundle(extra=None): | |
| files = {"project.godot": '[application]\nrun/main_scene="res://main.tscn"', | |
| "main.tscn": '[gd_scene format=3]\n[node name="Main" type="Node2D"]', | |
| "main.gd": "extends Node2D\n"} | |
| files.update(extra or {}) | |
| return json.dumps({"files": files}) | |
| class DataTests(unittest.TestCase): | |
| def test_canonical_output_has_required_files(self): | |
| canonical, files = canonical_output(bundle()) | |
| self.assertEqual(set(json.loads(canonical)), {"files"}) | |
| self.assertEqual(len(files), 3) | |
| def test_refuse_traversal_and_assets(self): | |
| for path in ["../escape.gd", "/escape.gd", ".godot/cache.gd", "a/../bad.gd", "C:\\bad.gd", "art.png"]: | |
| with self.subTest(path=path), self.assertRaises(ValueError): | |
| canonical_output(bundle({path: "payload"})) | |
| def test_refuse_missing_project_or_extra_top_level(self): | |
| for output in ['{"files":{"main.gd":"extends Node"}}', '{"files":{},"explanation":"x"}']: | |
| with self.assertRaises(ValueError): | |
| canonical_output(output) | |
| def test_license_filter_rejects_mixed_license(self): | |
| mit = ('Permission is hereby granted, free of charge to anyone. ' | |
| 'The above copyright notice and this permission notice. ' | |
| 'THE SOFTWARE IS PROVIDED AS IS.') | |
| self.assertEqual(identify_license(mit), "MIT") | |
| self.assertIsNone(identify_license(mit + " Art assets CC-BY-NC-SA 4.0")) | |
| self.assertIsNone(identify_license("See website for license")) | |
| self.assertTrue(has_conflicting_header("# SPDX-License-Identifier: GPL-3.0-only\nextends Node")) | |
| self.assertTrue(has_conflicting_header("# SPDX-License-Identifier: MIT OR GPL-3.0-only")) | |
| self.assertFalse(has_conflicting_header("# SPDX-License-Identifier: MIT\nextends Node")) | |
| self.assertTrue(has_conflicting_header("# SPDX-License-Identifier: Apache-2.0", expected_license="MIT")) | |
| self.assertTrue(has_conflicting_header("# Released under the Apache License", expected_license="MIT")) | |
| self.assertFalse(has_conflicting_header('var label = "GPL"')) | |
| def test_dedupe_catches_whitespace_and_near_copies(self): | |
| deduper = Deduper(threshold=.75, width=3) | |
| original = "make an arcade game where the player catches a ball and scores ten points to win" | |
| deduper.add("first", original) | |
| self.assertEqual(deduper.match(original.upper() + " "), "first") | |
| self.assertEqual(deduper.match(original + " please"), "first") | |
| self.assertIsNone(deduper.match("Solve a warehouse crate puzzle by pushing boxes onto goals")) | |
| def test_eval_frozen_unique_and_unlabeled(self): | |
| rows = builder.frozen_eval_rows() | |
| self.assertEqual(len(rows), 500) | |
| self.assertEqual(len({row["id"] for row in rows}), 500) | |
| self.assertEqual(len({row["sha256"] for row in rows}), 500) | |
| self.assertEqual(len({row["family"] for row in rows}), 25) | |
| self.assertTrue(all("output" not in row for row in rows)) | |
| self.assertTrue(all(sha256(row["instruction"]) == row["sha256"] for row in rows)) | |
| def test_freeze_refuses_modified_benchmark(self): | |
| with tempfile.TemporaryDirectory() as directory: | |
| root = Path(directory) | |
| builder.freeze_eval(root) | |
| builder.freeze_eval(root) | |
| target = root / "eval_prompts.jsonl" | |
| rows = list(read_jsonl(target)) | |
| rows[0]["instruction"] = "Changed benchmark" | |
| write_jsonl(target, rows) | |
| with self.assertRaises(ValueError): | |
| builder.freeze_eval(root) | |
| def test_split_no_duplicated_rows_and_order_independent(self): | |
| rows = [{"instruction": f"unique prompt {i}", "output": f"project {i}"} for i in range(10)] | |
| train, dev = builder.split_records(rows) | |
| self.assertEqual((len(train), len(dev)), (9, 1)) | |
| self.assertEqual((train, dev), builder.split_records(list(reversed(rows)))) | |
| self.assertFalse({row["instruction"] for row in train} & {row["instruction"] for row in dev}) | |
| with self.assertRaises(ValueError): | |
| builder.split_records(rows[:1]) | |
| def test_cache_requires_hashes_runtime_and_exact_sources(self): | |
| from vibegame.project import project_sha256 | |
| _, files = canonical_output(bundle()) | |
| with tempfile.TemporaryDirectory() as directory: | |
| target = Path(directory) | |
| (target / "project").mkdir() | |
| for name, source in files.items(): | |
| (target / "project" / name).write_text(source) | |
| result = {"compile_success": True, "clean_run_60s": True, "run_seconds": 60, | |
| "godot_binary_sha256": "test-binary", "project_sha256": project_sha256(files)} | |
| for phase in ["import", "runtime"]: | |
| (target / f"{phase}.log").write_text("test fixture\n") | |
| result[phase] = {"success": True, "wall_seconds": 60.1, | |
| "log_sha256": sha256("test fixture\n")} | |
| (target / "validation.json").write_text(json.dumps(result)) | |
| self.assertIsNotNone(builder.successful_cached_validation(target, files, "test-binary", 60)) | |
| self.assertIsNone(builder.successful_cached_validation(target, files, "different-binary", 60)) | |
| self.assertIsNone(builder.successful_cached_validation(target, files, "test-binary", 61)) | |
| (target / "runtime.log").write_text("changed fixture") | |
| self.assertIsNone(builder.successful_cached_validation(target, files, "test-binary", 60)) | |
| def test_teacher_prompts_are_not_blind_eval(self): | |
| target = ROOT / "data/teacher_prompts.jsonl" | |
| if not target.exists(): | |
| self.skipTest("teacher prompts not included") | |
| prompts = Deduper(threshold=.78, width=3) | |
| for row in builder.frozen_eval_rows(): | |
| prompts.add(row["id"], row["instruction"]) | |
| rows = list(read_jsonl(target)) | |
| self.assertEqual(len(rows), 100) | |
| for row in rows: | |
| self.assertEqual(set(row), {"id", "instruction"}) | |
| self.assertIsNone(prompts.match(row["instruction"]), row["id"]) | |
| prompts.add(row["id"], row["instruction"]) | |
| def test_collected_reference_integrity(self): | |
| target = ROOT / "data/reference_gd.jsonl" | |
| if not target.exists(): | |
| self.skipTest("optional reference corpus not downloaded") | |
| for row in read_jsonl(target): | |
| self.assertIn(row["license"], {"MIT", "Apache-2.0", "CC0-1.0"}) | |
| self.assertTrue(row["path"].endswith(".gd")) | |
| self.assertEqual(len(row["commit"]), 40) | |
| self.assertEqual(row["sha256"], sha256(row["code"])) | |
| self.assertEqual(row["use"], "reference_only_not_supervised_training") | |
| self.assertFalse(has_conflicting_header(row["code"], expected_license=row["license"])) | |
| for license_path in row["license_files"]: | |
| self.assertTrue((ROOT / "data" / license_path).is_file()) | |
| if __name__ == "__main__": | |
| unittest.main() | |