Instructions to use LesterCerioli/LLM-GO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LesterCerioli/LLM-GO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LesterCerioli/LLM-GO")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LesterCerioli/LLM-GO", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LesterCerioli/LLM-GO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LesterCerioli/LLM-GO" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LesterCerioli/LLM-GO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LesterCerioli/LLM-GO
- SGLang
How to use LesterCerioli/LLM-GO 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 "LesterCerioli/LLM-GO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LesterCerioli/LLM-GO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "LesterCerioli/LLM-GO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LesterCerioli/LLM-GO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LesterCerioli/LLM-GO with Docker Model Runner:
docker model run hf.co/LesterCerioli/LLM-GO
| """Loads canonical Go template files from disk and exposes them as training texts.""" | |
| from __future__ import annotations | |
| import os | |
| from pathlib import Path | |
| from typing import Iterator | |
| _TEMPLATES_ROOT = Path(__file__).parent | |
| class TemplateLoader: | |
| """Walks the go_project template tree and yields each file as a training string. | |
| Each yielded string is wrapped with structural tags so the tokenizer | |
| can inject <go_file>, <go_func>, etc. just like real corpus files. | |
| """ | |
| def __init__(self, root: Path | str | None = None) -> None: | |
| self._root = Path(root) if root else _TEMPLATES_ROOT / "go_project" | |
| # ------------------------------------------------------------------ | |
| # Public API | |
| # ------------------------------------------------------------------ | |
| def all_texts(self) -> list[str]: | |
| return list(self._iter_texts()) | |
| def _iter_texts(self) -> Iterator[str]: | |
| for path in sorted(self._root.rglob("*")): | |
| if path.is_file(): | |
| yield self._format(path) | |
| # ------------------------------------------------------------------ | |
| # Internal helpers | |
| # ------------------------------------------------------------------ | |
| def _format(self, path: Path) -> str: | |
| rel = path.relative_to(self._root) | |
| content = path.read_text(encoding="utf-8") | |
| pkg = _infer_package(path) | |
| return ( | |
| f"<go_file>{rel}</go_file>\n" | |
| f"<go_pkg>{pkg}</go_pkg>\n" | |
| f"<go_version>1.24</go_version>\n" | |
| f"{content}" | |
| ) | |
| def _infer_package(path: Path) -> str: | |
| """Derive a Go package name from the file path (last non-extension component).""" | |
| if path.suffix == ".go": | |
| return path.parent.name | |
| if path.name == "go.mod": | |
| try: | |
| for line in path.read_text().splitlines(): | |
| if line.startswith("module "): | |
| return line.split()[1] | |
| except OSError: | |
| pass | |
| return os.path.splitext(path.name)[0] | |