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 32 33 34 35 36 37 38 39 40 41 42 43 | #!/usr/bin/env bash
# scripts/preprocess.sh — filtra, desduplicamenta, tokeniza e empacota em TFRecords
set -euo pipefail
RAW_DIR=${RAW_DIR:-data/raw}
TOK_DIR=${TOK_DIR:-data/tokenizer}
OUT_DIR=${OUT_DIR:-data/processed}
SEQ_LEN=${SEQ_LEN:-2048}
SHARD_SIZE=${SHARD_SIZE:-10000}
if [ ! -f "$TOK_DIR/tokenizer.json" ]; then
echo "ERRO: Tokenizador não encontrado em $TOK_DIR."
echo " Execute scripts/build_tokenizer.sh primeiro."
exit 1
fi
echo "==> Pré-processando corpus..."
echo " raw=$RAW_DIR tok=$TOK_DIR out=$OUT_DIR"
echo " seq_len=$SEQ_LEN shard_size=$SHARD_SIZE"
python3 -c "
from llm_go.tokenizer import GoTokenizer
from llm_go.data import GoPreprocessor
tok = GoTokenizer.load('$TOK_DIR')
p = GoPreprocessor(
tokenizer=$tok,
raw_dir='$RAW_DIR',
out_dir='$OUT_DIR',
seq_len=$SEQ_LEN,
shard_size=$SHARD_SIZE,
)
counts = p.run()
print('Splits:', counts)
"
echo ""
echo "✅ TFRecords escritos em $OUT_DIR"
for split in train val test; do
n=$(find "$OUT_DIR/$split" -name "*.tfrecord" 2>/dev/null | wc -l)
echo " $split: $n shards"
done
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