Instructions to use Aliguinga01/rule_violation2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Aliguinga01/rule_violation2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: llama cli -hf Aliguinga01/rule_violation2:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: llama cli -hf Aliguinga01/rule_violation2:F16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: ./llama-cli -hf Aliguinga01/rule_violation2:F16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Aliguinga01/rule_violation2:F16
Use Docker
docker model run hf.co/Aliguinga01/rule_violation2:F16
- LM Studio
- Jan
- Ollama
How to use Aliguinga01/rule_violation2 with Ollama:
ollama run hf.co/Aliguinga01/rule_violation2:F16
- Unsloth Studio
How to use Aliguinga01/rule_violation2 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 Aliguinga01/rule_violation2 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 Aliguinga01/rule_violation2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Aliguinga01/rule_violation2 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Aliguinga01/rule_violation2 with Docker Model Runner:
docker model run hf.co/Aliguinga01/rule_violation2:F16
- Lemonade
How to use Aliguinga01/rule_violation2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Aliguinga01/rule_violation2:F16
Run and chat with the model
lemonade run user.rule_violation2-F16
List all available models
lemonade list
| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| # type: ignore[reportUnusedImport] | |
| import subprocess | |
| import os | |
| import re | |
| import json | |
| import sys | |
| import requests | |
| import time | |
| from concurrent.futures import ThreadPoolExecutor, as_completed | |
| from typing import ( | |
| Any, | |
| Callable, | |
| ContextManager, | |
| Iterable, | |
| Iterator, | |
| List, | |
| Literal, | |
| Tuple, | |
| Set, | |
| ) | |
| from re import RegexFlag | |
| import wget | |
| DEFAULT_HTTP_TIMEOUT = 60 | |
| class ServerResponse: | |
| headers: dict | |
| status_code: int | |
| body: dict | Any | |
| class ServerError(Exception): | |
| def __init__(self, code, body): | |
| self.code = code | |
| self.body = body | |
| class ServerProcess: | |
| # default options | |
| debug: bool = False | |
| server_port: int = 8080 | |
| server_host: str = "127.0.0.1" | |
| model_hf_repo: str = "ggml-org/models" | |
| model_hf_file: str | None = "tinyllamas/stories260K.gguf" | |
| model_alias: str = "tinyllama-2" | |
| temperature: float = 0.8 | |
| seed: int = 42 | |
| offline: bool = False | |
| # custom options | |
| model_alias: str | None = None | |
| model_url: str | None = None | |
| model_file: str | None = None | |
| model_draft: str | None = None | |
| n_threads: int | None = None | |
| n_gpu_layer: int | None = None | |
| n_batch: int | None = None | |
| n_ubatch: int | None = None | |
| n_ctx: int | None = None | |
| n_ga: int | None = None | |
| n_ga_w: int | None = None | |
| n_predict: int | None = None | |
| n_prompts: int | None = 0 | |
| slot_save_path: str | None = None | |
| id_slot: int | None = None | |
| cache_prompt: bool | None = None | |
| n_slots: int | None = None | |
| ctk: str | None = None | |
| ctv: str | None = None | |
| fa: str | None = None | |
| server_continuous_batching: bool | None = False | |
| server_embeddings: bool | None = False | |
| server_reranking: bool | None = False | |
| server_metrics: bool | None = False | |
| server_slots: bool | None = False | |
| pooling: str | None = None | |
| draft: int | None = None | |
| api_key: str | None = None | |
| lora_files: List[str] | None = None | |
| enable_ctx_shift: int | None = False | |
| draft_min: int | None = None | |
| draft_max: int | None = None | |
| no_webui: bool | None = None | |
| jinja: bool | None = None | |
| reasoning_format: Literal['deepseek', 'none', 'nothink'] | None = None | |
| reasoning_budget: int | None = None | |
| chat_template: str | None = None | |
| chat_template_file: str | None = None | |
| server_path: str | None = None | |
| mmproj_url: str | None = None | |
| # session variables | |
| process: subprocess.Popen | None = None | |
| def __init__(self): | |
| if "N_GPU_LAYERS" in os.environ: | |
| self.n_gpu_layer = int(os.environ["N_GPU_LAYERS"]) | |
| if "DEBUG" in os.environ: | |
| self.debug = True | |
| if "PORT" in os.environ: | |
| self.server_port = int(os.environ["PORT"]) | |
| self.external_server = "DEBUG_EXTERNAL" in os.environ | |
| def start(self, timeout_seconds: int | None = DEFAULT_HTTP_TIMEOUT) -> None: | |
| if self.external_server: | |
| print(f"[external_server]: Assuming external server running on {self.server_host}:{self.server_port}") | |
| return | |
| if self.server_path is not None: | |
| server_path = self.server_path | |
| elif "LLAMA_SERVER_BIN_PATH" in os.environ: | |
| server_path = os.environ["LLAMA_SERVER_BIN_PATH"] | |
| elif os.name == "nt": | |
| server_path = "../../../build/bin/Release/llama-server.exe" | |
| else: | |
| server_path = "../../../build/bin/llama-server" | |
| server_args = [ | |
| "--host", | |
| self.server_host, | |
| "--port", | |
| self.server_port, | |
| "--temp", | |
| self.temperature, | |
| "--seed", | |
| self.seed, | |
| ] | |
| if self.offline: | |
| server_args.append("--offline") | |
| if self.model_file: | |
| server_args.extend(["--model", self.model_file]) | |
| if self.model_url: | |
| server_args.extend(["--model-url", self.model_url]) | |
| if self.model_draft: | |
| server_args.extend(["--model-draft", self.model_draft]) | |
| if self.model_hf_repo: | |
| server_args.extend(["--hf-repo", self.model_hf_repo]) | |
| if self.model_hf_file: | |
| server_args.extend(["--hf-file", self.model_hf_file]) | |
| if self.n_batch: | |
| server_args.extend(["--batch-size", self.n_batch]) | |
| if self.n_ubatch: | |
| server_args.extend(["--ubatch-size", self.n_ubatch]) | |
| if self.n_threads: | |
| server_args.extend(["--threads", self.n_threads]) | |
| if self.n_gpu_layer: | |
| server_args.extend(["--n-gpu-layers", self.n_gpu_layer]) | |
| if self.draft is not None: | |
| server_args.extend(["--draft", self.draft]) | |
| if self.server_continuous_batching: | |
| server_args.append("--cont-batching") | |
| if self.server_embeddings: | |
| server_args.append("--embedding") | |
| if self.server_reranking: | |
| server_args.append("--reranking") | |
| if self.server_metrics: | |
| server_args.append("--metrics") | |
| if self.server_slots: | |
| server_args.append("--slots") | |
| else: | |
| server_args.append("--no-slots") | |
| if self.pooling: | |
| server_args.extend(["--pooling", self.pooling]) | |
| if self.model_alias: | |
| server_args.extend(["--alias", self.model_alias]) | |
| if self.n_ctx: | |
| server_args.extend(["--ctx-size", self.n_ctx]) | |
| if self.n_slots: | |
| server_args.extend(["--parallel", self.n_slots]) | |
| if self.ctk: | |
| server_args.extend(["-ctk", self.ctk]) | |
| if self.ctv: | |
| server_args.extend(["-ctv", self.ctv]) | |
| if self.fa is not None: | |
| server_args.extend(["-fa", self.fa]) | |
| if self.n_predict: | |
| server_args.extend(["--n-predict", self.n_predict]) | |
| if self.slot_save_path: | |
| server_args.extend(["--slot-save-path", self.slot_save_path]) | |
| if self.n_ga: | |
| server_args.extend(["--grp-attn-n", self.n_ga]) | |
| if self.n_ga_w: | |
| server_args.extend(["--grp-attn-w", self.n_ga_w]) | |
| if self.debug: | |
| server_args.append("--verbose") | |
| if self.lora_files: | |
| for lora_file in self.lora_files: | |
| server_args.extend(["--lora", lora_file]) | |
| if self.enable_ctx_shift: | |
| server_args.append("--context-shift") | |
| if self.api_key: | |
| server_args.extend(["--api-key", self.api_key]) | |
| if self.draft_max: | |
| server_args.extend(["--draft-max", self.draft_max]) | |
| if self.draft_min: | |
| server_args.extend(["--draft-min", self.draft_min]) | |
| if self.no_webui: | |
| server_args.append("--no-webui") | |
| if self.jinja: | |
| server_args.append("--jinja") | |
| if self.reasoning_format is not None: | |
| server_args.extend(("--reasoning-format", self.reasoning_format)) | |
| if self.reasoning_budget is not None: | |
| server_args.extend(("--reasoning-budget", self.reasoning_budget)) | |
| if self.chat_template: | |
| server_args.extend(["--chat-template", self.chat_template]) | |
| if self.chat_template_file: | |
| server_args.extend(["--chat-template-file", self.chat_template_file]) | |
| if self.mmproj_url: | |
| server_args.extend(["--mmproj-url", self.mmproj_url]) | |
| args = [str(arg) for arg in [server_path, *server_args]] | |
| print(f"tests: starting server with: {' '.join(args)}") | |
| flags = 0 | |
| if "nt" == os.name: | |
| flags |= subprocess.DETACHED_PROCESS | |
| flags |= subprocess.CREATE_NEW_PROCESS_GROUP | |
| flags |= subprocess.CREATE_NO_WINDOW | |
| self.process = subprocess.Popen( | |
| [str(arg) for arg in [server_path, *server_args]], | |
| creationflags=flags, | |
| stdout=sys.stdout, | |
| stderr=sys.stdout, | |
| env={**os.environ, "LLAMA_CACHE": "tmp"} if "LLAMA_CACHE" not in os.environ else None, | |
| ) | |
| server_instances.add(self) | |
| print(f"server pid={self.process.pid}, pytest pid={os.getpid()}") | |
| # wait for server to start | |
| start_time = time.time() | |
| while time.time() - start_time < timeout_seconds: | |
| try: | |
| response = self.make_request("GET", "/health", headers={ | |
| "Authorization": f"Bearer {self.api_key}" if self.api_key else None | |
| }) | |
| if response.status_code == 200: | |
| self.ready = True | |
| return # server is ready | |
| except Exception as e: | |
| pass | |
| # Check if process died | |
| if self.process.poll() is not None: | |
| raise RuntimeError(f"Server process died with return code {self.process.returncode}") | |
| print(f"Waiting for server to start...") | |
| time.sleep(0.5) | |
| raise TimeoutError(f"Server did not start within {timeout_seconds} seconds") | |
| def stop(self) -> None: | |
| if self.external_server: | |
| print("[external_server]: Not stopping external server") | |
| return | |
| if self in server_instances: | |
| server_instances.remove(self) | |
| if self.process: | |
| print(f"Stopping server with pid={self.process.pid}") | |
| self.process.kill() | |
| self.process = None | |
| def make_request( | |
| self, | |
| method: str, | |
| path: str, | |
| data: dict | Any | None = None, | |
| headers: dict | None = None, | |
| timeout: float | None = None, | |
| ) -> ServerResponse: | |
| url = f"http://{self.server_host}:{self.server_port}{path}" | |
| parse_body = False | |
| if method == "GET": | |
| response = requests.get(url, headers=headers, timeout=timeout) | |
| parse_body = True | |
| elif method == "POST": | |
| response = requests.post(url, headers=headers, json=data, timeout=timeout) | |
| parse_body = True | |
| elif method == "OPTIONS": | |
| response = requests.options(url, headers=headers, timeout=timeout) | |
| else: | |
| raise ValueError(f"Unimplemented method: {method}") | |
| result = ServerResponse() | |
| result.headers = dict(response.headers) | |
| result.status_code = response.status_code | |
| result.body = response.json() if parse_body else None | |
| print("Response from server", json.dumps(result.body, indent=2)) | |
| return result | |
| def make_stream_request( | |
| self, | |
| method: str, | |
| path: str, | |
| data: dict | None = None, | |
| headers: dict | None = None, | |
| ) -> Iterator[dict]: | |
| url = f"http://{self.server_host}:{self.server_port}{path}" | |
| if method == "POST": | |
| response = requests.post(url, headers=headers, json=data, stream=True) | |
| else: | |
| raise ValueError(f"Unimplemented method: {method}") | |
| if response.status_code != 200: | |
| raise ServerError(response.status_code, response.json()) | |
| for line_bytes in response.iter_lines(): | |
| line = line_bytes.decode("utf-8") | |
| if '[DONE]' in line: | |
| break | |
| elif line.startswith('data: '): | |
| data = json.loads(line[6:]) | |
| print("Partial response from server", json.dumps(data, indent=2)) | |
| yield data | |
| def make_any_request( | |
| self, | |
| method: str, | |
| path: str, | |
| data: dict | None = None, | |
| headers: dict | None = None, | |
| timeout: float | None = None, | |
| ) -> dict: | |
| stream = data.get('stream', False) | |
| if stream: | |
| content: list[str] = [] | |
| reasoning_content: list[str] = [] | |
| tool_calls: list[dict] = [] | |
| finish_reason: Optional[str] = None | |
| content_parts = 0 | |
| reasoning_content_parts = 0 | |
| tool_call_parts = 0 | |
| arguments_parts = 0 | |
| for chunk in self.make_stream_request(method, path, data, headers): | |
| if chunk['choices']: | |
| assert len(chunk['choices']) == 1, f'Expected 1 choice, got {len(chunk["choices"])}' | |
| choice = chunk['choices'][0] | |
| if choice['delta'].get('content') is not None: | |
| assert len(choice['delta']['content']) > 0, f'Expected non empty content delta!' | |
| content.append(choice['delta']['content']) | |
| content_parts += 1 | |
| if choice['delta'].get('reasoning_content') is not None: | |
| assert len(choice['delta']['reasoning_content']) > 0, f'Expected non empty reasoning_content delta!' | |
| reasoning_content.append(choice['delta']['reasoning_content']) | |
| reasoning_content_parts += 1 | |
| if choice['delta'].get('finish_reason') is not None: | |
| finish_reason = choice['delta']['finish_reason'] | |
| for tc in choice['delta'].get('tool_calls', []): | |
| if 'function' not in tc: | |
| raise ValueError(f"Expected function type, got {tc['type']}") | |
| if tc['index'] >= len(tool_calls): | |
| assert 'id' in tc | |
| assert tc.get('type') == 'function' | |
| assert 'function' in tc and 'name' in tc['function'] and len(tc['function']['name']) > 0, \ | |
| f"Expected function call with name, got {tc.get('function')}" | |
| tool_calls.append(dict( | |
| id="", | |
| type="function", | |
| function=dict( | |
| name="", | |
| arguments="", | |
| ) | |
| )) | |
| tool_call = tool_calls[tc['index']] | |
| if tc.get('id') is not None: | |
| tool_call['id'] = tc['id'] | |
| fct = tc['function'] | |
| assert 'id' not in fct, f"Function call should not have id: {fct}" | |
| if fct.get('name') is not None: | |
| tool_call['function']['name'] = tool_call['function'].get('name', '') + fct['name'] | |
| if fct.get('arguments') is not None: | |
| tool_call['function']['arguments'] += fct['arguments'] | |
| arguments_parts += 1 | |
| tool_call_parts += 1 | |
| else: | |
| # When `include_usage` is True (the default), we expect the last chunk of the stream | |
| # immediately preceding the `data: [DONE]` message to contain a `choices` field with an empty array | |
| # and a `usage` field containing the usage statistics (n.b., llama-server also returns `timings` in | |
| # the last chunk) | |
| assert 'usage' in chunk, f"Expected finish_reason in chunk: {chunk}" | |
| assert 'timings' in chunk, f"Expected finish_reason in chunk: {chunk}" | |
| print(f'Streamed response had {content_parts} content parts, {reasoning_content_parts} reasoning_content parts, {tool_call_parts} tool call parts incl. {arguments_parts} arguments parts') | |
| result = dict( | |
| choices=[ | |
| dict( | |
| index=0, | |
| finish_reason=finish_reason, | |
| message=dict( | |
| role='assistant', | |
| content=''.join(content) if content else None, | |
| reasoning_content=''.join(reasoning_content) if reasoning_content else None, | |
| tool_calls=tool_calls if tool_calls else None, | |
| ), | |
| ) | |
| ], | |
| ) | |
| print("Final response from server", json.dumps(result, indent=2)) | |
| return result | |
| else: | |
| response = self.make_request(method, path, data, headers, timeout=timeout) | |
| assert response.status_code == 200, f"Server returned error: {response.status_code}" | |
| return response.body | |
| server_instances: Set[ServerProcess] = set() | |
| class ServerPreset: | |
| def load_all() -> None: | |
| """ Load all server presets to ensure model files are cached. """ | |
| servers: List[ServerProcess] = [ | |
| method() | |
| for name, method in ServerPreset.__dict__.items() | |
| if callable(method) and name != "load_all" | |
| ] | |
| for server in servers: | |
| server.offline = False | |
| server.start() | |
| server.stop() | |
| def tinyllama2() -> ServerProcess: | |
| server = ServerProcess() | |
| server.model_hf_repo = "ggml-org/models" | |
| server.model_hf_file = "tinyllamas/stories260K.gguf" | |
| server.model_alias = "tinyllama-2" | |
| server.n_ctx = 512 | |
| server.n_batch = 32 | |
| server.n_slots = 2 | |
| server.n_predict = 64 | |
| server.seed = 42 | |
| return server | |
| def bert_bge_small() -> ServerProcess: | |
| server = ServerProcess() | |
| server.offline = True # will be downloaded by load_all() | |
| server.model_hf_repo = "ggml-org/models" | |
| server.model_hf_file = "bert-bge-small/ggml-model-f16.gguf" | |
| server.model_alias = "bert-bge-small" | |
| server.n_ctx = 512 | |
| server.n_batch = 128 | |
| server.n_ubatch = 128 | |
| server.n_slots = 2 | |
| server.seed = 42 | |
| server.server_embeddings = True | |
| return server | |
| def bert_bge_small_with_fa() -> ServerProcess: | |
| server = ServerProcess() | |
| server.offline = True # will be downloaded by load_all() | |
| server.model_hf_repo = "ggml-org/models" | |
| server.model_hf_file = "bert-bge-small/ggml-model-f16.gguf" | |
| server.model_alias = "bert-bge-small" | |
| server.n_ctx = 1024 | |
| server.n_batch = 300 | |
| server.n_ubatch = 300 | |
| server.n_slots = 2 | |
| server.fa = "on" | |
| server.seed = 42 | |
| server.server_embeddings = True | |
| return server | |
| def tinyllama_infill() -> ServerProcess: | |
| server = ServerProcess() | |
| server.offline = True # will be downloaded by load_all() | |
| server.model_hf_repo = "ggml-org/models" | |
| server.model_hf_file = "tinyllamas/stories260K-infill.gguf" | |
| server.model_alias = "tinyllama-infill" | |
| server.n_ctx = 2048 | |
| server.n_batch = 1024 | |
| server.n_slots = 1 | |
| server.n_predict = 64 | |
| server.temperature = 0.0 | |
| server.seed = 42 | |
| return server | |
| def stories15m_moe() -> ServerProcess: | |
| server = ServerProcess() | |
| server.offline = True # will be downloaded by load_all() | |
| server.model_hf_repo = "ggml-org/stories15M_MOE" | |
| server.model_hf_file = "stories15M_MOE-F16.gguf" | |
| server.model_alias = "stories15m-moe" | |
| server.n_ctx = 2048 | |
| server.n_batch = 1024 | |
| server.n_slots = 1 | |
| server.n_predict = 64 | |
| server.temperature = 0.0 | |
| server.seed = 42 | |
| return server | |
| def jina_reranker_tiny() -> ServerProcess: | |
| server = ServerProcess() | |
| server.offline = True # will be downloaded by load_all() | |
| server.model_hf_repo = "ggml-org/models" | |
| server.model_hf_file = "jina-reranker-v1-tiny-en/ggml-model-f16.gguf" | |
| server.model_alias = "jina-reranker" | |
| server.n_ctx = 512 | |
| server.n_batch = 512 | |
| server.n_slots = 1 | |
| server.seed = 42 | |
| server.server_reranking = True | |
| return server | |
| def tinygemma3() -> ServerProcess: | |
| server = ServerProcess() | |
| server.offline = True # will be downloaded by load_all() | |
| # mmproj is already provided by HF registry API | |
| server.model_hf_repo = "ggml-org/tinygemma3-GGUF" | |
| server.model_hf_file = "tinygemma3-Q8_0.gguf" | |
| server.mmproj_url = "https://huggingface.co/ggml-org/tinygemma3-GGUF/resolve/main/mmproj-tinygemma3.gguf" | |
| server.model_alias = "tinygemma3" | |
| server.n_ctx = 1024 | |
| server.n_batch = 32 | |
| server.n_slots = 2 | |
| server.n_predict = 4 | |
| server.seed = 42 | |
| return server | |
| def parallel_function_calls(function_list: List[Tuple[Callable[..., Any], Tuple[Any, ...]]]) -> List[Any]: | |
| """ | |
| Run multiple functions in parallel and return results in the same order as calls. Equivalent to Promise.all in JS. | |
| Example usage: | |
| results = parallel_function_calls([ | |
| (func1, (arg1, arg2)), | |
| (func2, (arg3, arg4)), | |
| ]) | |
| """ | |
| results = [None] * len(function_list) | |
| exceptions = [] | |
| def worker(index, func, args): | |
| try: | |
| result = func(*args) | |
| results[index] = result | |
| except Exception as e: | |
| exceptions.append((index, str(e))) | |
| with ThreadPoolExecutor() as executor: | |
| futures = [] | |
| for i, (func, args) in enumerate(function_list): | |
| future = executor.submit(worker, i, func, args) | |
| futures.append(future) | |
| # Wait for all futures to complete | |
| for future in as_completed(futures): | |
| pass | |
| # Check if there were any exceptions | |
| if exceptions: | |
| print("Exceptions occurred:") | |
| for index, error in exceptions: | |
| print(f"Function at index {index}: {error}") | |
| return results | |
| def match_regex(regex: str, text: str) -> bool: | |
| return ( | |
| re.compile( | |
| regex, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL | |
| ).search(text) | |
| is not None | |
| ) | |
| def download_file(url: str, output_file_path: str | None = None) -> str: | |
| """ | |
| Download a file from a URL to a local path. If the file already exists, it will not be downloaded again. | |
| output_file_path is the local path to save the downloaded file. If not provided, the file will be saved in the root directory. | |
| Returns the local path of the downloaded file. | |
| """ | |
| file_name = url.split('/').pop() | |
| output_file = f'./tmp/{file_name}' if output_file_path is None else output_file_path | |
| if not os.path.exists(output_file): | |
| print(f"Downloading {url} to {output_file}") | |
| wget.download(url, out=output_file) | |
| print(f"Done downloading to {output_file}") | |
| else: | |
| print(f"File already exists at {output_file}") | |
| return output_file | |
| def is_slow_test_allowed(): | |
| return os.environ.get("SLOW_TESTS") == "1" or os.environ.get("SLOW_TESTS") == "ON" | |