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
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a58ece3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | #!/usr/bin/env bash
# scripts/collect_data.sh — coleta código Go do GitHub e da stdlib
set -euo pipefail
: "${GITHUB_TOKEN:?Defina GITHUB_TOKEN antes de executar}"
RAW_DIR=${RAW_DIR:-data/raw}
MAX_REPOS=${MAX_REPOS:-50000}
MIN_STARS=${MIN_STARS:-10}
GOROOT=${GOROOT:-$(go env GOROOT 2>/dev/null || echo "")}
echo "==> Iniciando coleta de repositórios Go (max=$MAX_REPOS, min_stars=$MIN_STARS)..."
llm-go-collect \
--token "$GITHUB_TOKEN" \
--out-dir "$RAW_DIR" \
--max-repos "$MAX_REPOS" \
--min-stars "$MIN_STARS"
echo "==> Coletando stdlib Go..."
if [ -n "$GOROOT" ] && [ -d "$GOROOT" ]; then
echo " Usando GOROOT local: $GOROOT"
llm-go-collect --token "$GITHUB_TOKEN" --stdlib --go-root "$GOROOT"
else
echo " GOROOT não encontrado, baixando do GitHub..."
llm-go-collect --token "$GITHUB_TOKEN" --stdlib
fi
echo ""
FILES=$(find "$RAW_DIR" -name "*.go" 2>/dev/null | wc -l)
echo "✅ Coleta concluída: $FILES arquivos .go em $RAW_DIR"
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