Instructions to use Jayfeather1024/sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jayfeather1024/sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Jayfeather1024/sft")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Jayfeather1024/sft") model = AutoModelForCausalLM.from_pretrained("Jayfeather1024/sft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Jayfeather1024/sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jayfeather1024/sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jayfeather1024/sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Jayfeather1024/sft
- SGLang
How to use Jayfeather1024/sft 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 "Jayfeather1024/sft" \ --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": "Jayfeather1024/sft", "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 "Jayfeather1024/sft" \ --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": "Jayfeather1024/sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Jayfeather1024/sft with Docker Model Runner:
docker model run hf.co/Jayfeather1024/sft
File size: 2,866 Bytes
3022639 | 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 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | #!/usr/bin/env bash
#
# Copyright 2023 PKU-Alignment Team. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
export WANDB_API_KEY="f6021dca133c93e80a7dae4620bd335d4d08cac6"
if [ -z "${BASH_VERSION}" ]; then
echo "Please use bash to run this script." >&2
exit 1
fi
set -x
SCRIPT_DIR="$(cd "$(dirname "$0")" &>/dev/null && pwd)"
ROOT_DIR="$(dirname "${SCRIPT_DIR}")"
export PYTHONPATH="${ROOT_DIR}${PYTHONPATH:+:${PYTHONPATH}}"
export LOGLEVEL="${LOGLEVEL:-WARNING}"
MODEL_NAME_OR_PATH="huggyllama/llama-7b"
OUTPUT_DIR="${ROOT_DIR}/output/sft"
ZERO_STAGE=3
while [[ "$#" -gt 0 ]]; do
arg="$1"
shift
case "${arg}" in
--model_name_or_path)
MODEL_NAME_OR_PATH="$1"
shift
;;
--model_name_or_path=*)
MODEL_NAME_OR_PATH="${arg#*=}"
;;
--output_dir)
OUTPUT_DIR="$1"
shift
;;
--output_dir=*)
OUTPUT_DIR="${arg#*=}"
;;
--zero_stage)
ZERO_STAGE="$1"
shift
;;
--zero_stage=*)
ZERO_STAGE="${arg#*=}"
;;
*)
echo "Unknown parameter passed: '${arg}'" >&2
exit 1
;;
esac
done
mkdir -p "${OUTPUT_DIR}"
OUTPUT_DIR="$(cd "${OUTPUT_DIR}" &>/dev/null && pwd)"
if [[ ! -f "${OUTPUT_DIR}/.gitignore" ]]; then
echo '*' >"${OUTPUT_DIR}/.gitignore"
fi
cp -f "$0" "${OUTPUT_DIR}/script.sh"
if [[ -z "${WANDB_API_KEY}" ]]; then
export WANDB_MODE="offline"
fi
MASTER_PORT_START=10000
MASTER_PORT_END=65535
MASTER_PORT="$(
comm -23 \
<(seq "${MASTER_PORT_START}" "${MASTER_PORT_END}" | sort) \
<(ss -Htan | awk '{ print $4 }' | awk -F ':' '{ print $NF }' | sort -u) |
shuf | head -n 1
)"
exec 1> >(tee "${OUTPUT_DIR}/stdout.log" >&1) 2> >(tee "${OUTPUT_DIR}/stderr.log" >&2)
deepspeed --num_nodes=1 --num_gpus=4 \
--master_port "${MASTER_PORT}" \
--module safe_rlhf.finetune \
--train_datasets alpaca \
--model_name_or_path "${MODEL_NAME_OR_PATH}" \
--max_length 512 \
--trust_remote_code True \
--epochs 3 \
--per_device_train_batch_size 4 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 16 \
--gradient_checkpointing \
--learning_rate 2e-5 \
--lr_scheduler_type cosine \
--lr_warmup_ratio 0.03 \
--weight_decay 0.0 \
--seed 42 \
--output_dir "${OUTPUT_DIR}" \
--log_type wandb \
--log_project Safe-RLHF-SFT \
--zero_stage "${ZERO_STAGE}" \
--bf16 True \
--tf32 True
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