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
| PYTHON := python3.12 | |
| PIP := $(PYTHON) -m pip | |
| MODEL_SIZE ?= medium | |
| CKPT_DIR ?= checkpoints/final | |
| TOK_DIR ?= data/tokenizer | |
| HF_REPO ?= your-org/llm-go-$(MODEL_SIZE) | |
| # ββ Setup ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| install: | |
| $(PIP) install --upgrade pip | |
| $(PIP) install -r requirements.txt | |
| $(PIP) install -e ".[dev]" | |
| install-gpu: | |
| $(PIP) install --upgrade pip | |
| $(PIP) install -r requirements.txt -r requirements-gpu.txt | |
| $(PIP) install -e ".[dev]" | |
| # ββ Data pipeline ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| collect: | |
| llm-go-collect --token $$GITHUB_TOKEN --max-repos 50000 | |
| tokenize: | |
| llm-go-tokenize --raw-dir data/raw --out-dir $(TOK_DIR) --vocab-size 32000 | |
| preprocess: | |
| $(PYTHON) -c "\ | |
| from llm_go.tokenizer import GoTokenizer; \ | |
| from llm_go.data import GoPreprocessor; \ | |
| tok = GoTokenizer.load('$(TOK_DIR)'); \ | |
| p = GoPreprocessor(tok); \ | |
| p.run()" | |
| # ββ Training βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| train: | |
| llm-go-train --model-size $(MODEL_SIZE) --data-dir data/processed \ | |
| --ckpt-dir checkpoints --log-dir logs | |
| train-small: | |
| $(MAKE) train MODEL_SIZE=small | |
| train-medium: | |
| $(MAKE) train MODEL_SIZE=medium | |
| train-large: | |
| $(MAKE) train MODEL_SIZE=large | |
| # ββ Evaluation βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| evaluate: | |
| llm-go-evaluate --model-dir $(CKPT_DIR) --tok-dir $(TOK_DIR) | |
| # ββ Generation βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| generate: | |
| llm-go-generate --model-dir $(CKPT_DIR) --tok-dir $(TOK_DIR) | |
| # ββ Deploy βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| deploy: | |
| llm-go-deploy --ckpt-dir $(CKPT_DIR) --tok-dir $(TOK_DIR) \ | |
| --repo-id $(HF_REPO) --token $$HF_TOKEN | |
| # ββ Tensorboard ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| tb: | |
| tensorboard --logdir logs | |
| # ββ Quality ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| test: | |
| pytest tests/ -v --cov=llm_go --cov-report=term-missing | |
| lint: | |
| ruff check src/ tests/ | |
| mypy src/ | |
| fmt: | |
| black src/ tests/ | |
| ruff check --fix src/ tests/ | |
| # ββ Full pipeline ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| pipeline: collect tokenize preprocess train evaluate deploy | |