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models/**
docext/**
results/**

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#!/usr/bin/env bash
# Color definitions
RED='\033[0;31m'; GREEN='\033[0;32m'; YELLOW='\033[1;33m'; NC='\033[0m' # No Color
export HF_ENDPOINT=https://hf-mirror.com
trap 'printf "${YELLOW}\nDownload interrupted. You can resume by re-running the command.\n${NC}"; exit 1' INT
display_help() {
cat << EOF
Usage:
hfd <REPO_ID> [--include include_pattern1 include_pattern2 ...] [--exclude exclude_pattern1 exclude_pattern2 ...] [--hf_username username] [--hf_token token] [--tool aria2c|wget] [-x threads] [-j jobs] [--dataset] [--local-dir path] [--revision rev]
Description:
Downloads a model or dataset from Hugging Face using the provided repo ID.
Arguments:
REPO_ID The Hugging Face repo ID (Required)
Format: 'org_name/repo_name' or legacy format (e.g., gpt2)
Options:
include/exclude_pattern The patterns to match against file path, supports wildcard characters.
e.g., '--exclude *.safetensor *.md', '--include vae/*'.
--include (Optional) Patterns to include files for downloading (supports multiple patterns).
--exclude (Optional) Patterns to exclude files from downloading (supports multiple patterns).
--hf_username (Optional) Hugging Face username for authentication (not email).
--hf_token (Optional) Hugging Face token for authentication.
--tool (Optional) Download tool to use: aria2c (default) or wget.
-x (Optional) Number of download threads for aria2c (default: 4).
-j (Optional) Number of concurrent downloads for aria2c (default: 5).
--dataset (Optional) Flag to indicate downloading a dataset.
--local-dir (Optional) Directory path to store the downloaded data.
Defaults to the current directory with a subdirectory named 'repo_name'
if REPO_ID is is composed of 'org_name/repo_name'.
--revision (Optional) Model/Dataset revision to download (default: main).
Example:
hfd gpt2
hfd bigscience/bloom-560m --exclude *.safetensors
hfd meta-llama/Llama-2-7b --hf_username myuser --hf_token mytoken -x 4
hfd lavita/medical-qa-shared-task-v1-toy --dataset
hfd bartowski/Phi-3.5-mini-instruct-exl2 --revision 5_0
EOF
exit 1
}
[[ -z "$1" || "$1" =~ ^-h || "$1" =~ ^--help ]] && display_help
REPO_ID=$1
shift
# Default values
TOOL="aria2c"
THREADS=4
CONCURRENT=5
HF_ENDPOINT=${HF_ENDPOINT:-"https://huggingface.co"}
INCLUDE_PATTERNS=()
EXCLUDE_PATTERNS=()
REVISION="main"
validate_number() {
[[ "$2" =~ ^[1-9][0-9]*$ && "$2" -le "$3" ]] || { printf "${RED}[Error] $1 must be 1-$3${NC}\n"; exit 1; }
}
# Argument parsing
while [[ $# -gt 0 ]]; do
case $1 in
--include) shift; while [[ $# -gt 0 && ! ($1 =~ ^--) && ! ($1 =~ ^-[^-]) ]]; do INCLUDE_PATTERNS+=("$1"); shift; done ;;
--exclude) shift; while [[ $# -gt 0 && ! ($1 =~ ^--) && ! ($1 =~ ^-[^-]) ]]; do EXCLUDE_PATTERNS+=("$1"); shift; done ;;
--hf_username) HF_USERNAME="$2"; shift 2 ;;
--hf_token) HF_TOKEN="$2"; shift 2 ;;
--tool)
case $2 in
aria2c|wget)
TOOL="$2"
;;
*)
printf "%b[Error] Invalid tool. Use 'aria2c' or 'wget'.%b\n" "$RED" "$NC"
exit 1
;;
esac
shift 2
;;
-x) validate_number "threads (-x)" "$2" 10; THREADS="$2"; shift 2 ;;
-j) validate_number "concurrent downloads (-j)" "$2" 10; CONCURRENT="$2"; shift 2 ;;
--dataset) DATASET=1; shift ;;
--local-dir) LOCAL_DIR="$2"; shift 2 ;;
--revision) REVISION="$2"; shift 2 ;;
*) display_help ;;
esac
done
# Generate current command string
generate_command_string() {
local cmd_string="REPO_ID=$REPO_ID"
cmd_string+=" TOOL=$TOOL"
cmd_string+=" INCLUDE_PATTERNS=${INCLUDE_PATTERNS[*]}"
cmd_string+=" EXCLUDE_PATTERNS=${EXCLUDE_PATTERNS[*]}"
cmd_string+=" DATASET=${DATASET:-0}"
cmd_string+=" HF_USERNAME=${HF_USERNAME:-}"
cmd_string+=" HF_TOKEN=${HF_TOKEN:-}"
cmd_string+=" HF_ENDPOINT=${HF_ENDPOINT:-}"
cmd_string+=" REVISION=$REVISION"
echo "$cmd_string"
}
# Check if aria2, wget, curl are installed
check_command() {
if ! command -v $1 &>/dev/null; then
printf "%b%s is not installed. Please install it first.%b\n" "$RED" "$1" "$NC"
exit 1
fi
}
check_command curl; check_command "$TOOL"
LOCAL_DIR="${LOCAL_DIR:-${REPO_ID#*/}}"
mkdir -p "$LOCAL_DIR/.hfd"
if [[ "$DATASET" == 1 ]]; then
METADATA_API_PATH="datasets/$REPO_ID"
DOWNLOAD_API_PATH="datasets/$REPO_ID"
CUT_DIRS=5
else
METADATA_API_PATH="models/$REPO_ID"
DOWNLOAD_API_PATH="$REPO_ID"
CUT_DIRS=4
fi
# Modify API URL, construct based on revision
if [[ "$REVISION" != "main" ]]; then
METADATA_API_PATH="$METADATA_API_PATH/revision/$REVISION"
fi
API_URL="$HF_ENDPOINT/api/$METADATA_API_PATH"
METADATA_FILE="$LOCAL_DIR/.hfd/repo_metadata.json"
# Fetch and save metadata
fetch_and_save_metadata() {
status_code=$(curl -L -s -w "%{http_code}" -o "$METADATA_FILE" ${HF_TOKEN:+-H "Authorization: Bearer $HF_TOKEN"} "$API_URL")
RESPONSE=$(cat "$METADATA_FILE")
if [ "$status_code" -eq 200 ]; then
printf "%s\n" "$RESPONSE"
else
printf "%b[Error] Failed to fetch metadata from $API_URL. HTTP status code: $status_code.%b\n$RESPONSE\n" "${RED}" "${NC}" >&2
rm $METADATA_FILE
exit 1
fi
}
check_authentication() {
local response="$1"
if command -v jq &>/dev/null; then
local gated
gated=$(echo "$response" | jq -r '.gated // false')
if [[ "$gated" != "false" && ( -z "$HF_TOKEN" || -z "$HF_USERNAME" ) ]]; then
printf "${RED}The repository requires authentication, but --hf_username and --hf_token is not passed. Please get token from https://huggingface.co/settings/tokens.\nExiting.\n${NC}"
exit 1
fi
else
if echo "$response" | grep -q '"gated":[^f]' && [[ -z "$HF_TOKEN" || -z "$HF_USERNAME" ]]; then
printf "${RED}The repository requires authentication, but --hf_username and --hf_token is not passed. Please get token from https://huggingface.co/settings/tokens.\nExiting.\n${NC}"
exit 1
fi
fi
}
if [[ ! -f "$METADATA_FILE" ]]; then
printf "%bFetching repo metadata...%b\n" "$YELLOW" "$NC"
RESPONSE=$(fetch_and_save_metadata) || exit 1
check_authentication "$RESPONSE"
else
printf "%bUsing cached metadata: $METADATA_FILE%b\n" "$GREEN" "$NC"
RESPONSE=$(cat "$METADATA_FILE")
check_authentication "$RESPONSE"
fi
should_regenerate_filelist() {
local command_file="$LOCAL_DIR/.hfd/last_download_command"
local current_command=$(generate_command_string)
# If file list doesn't exist, regenerate
if [[ ! -f "$LOCAL_DIR/$fileslist_file" ]]; then
echo "$current_command" > "$command_file"
return 0
fi
# If command file doesn't exist, regenerate
if [[ ! -f "$command_file" ]]; then
echo "$current_command" > "$command_file"
return 0
fi
# Compare current command with saved command
local saved_command=$(cat "$command_file")
if [[ "$current_command" != "$saved_command" ]]; then
echo "$current_command" > "$command_file"
return 0
fi
return 1
}
fileslist_file=".hfd/${TOOL}_urls.txt"
if should_regenerate_filelist; then
# Remove existing file list if it exists
[[ -f "$LOCAL_DIR/$fileslist_file" ]] && rm "$LOCAL_DIR/$fileslist_file"
printf "%bGenerating file list...%b\n" "$YELLOW" "$NC"
# Convert include and exclude patterns to regex
INCLUDE_REGEX=""
EXCLUDE_REGEX=""
if ((${#INCLUDE_PATTERNS[@]})); then
INCLUDE_REGEX=$(printf '%s\n' "${INCLUDE_PATTERNS[@]}" | sed 's/\./\\./g; s/\*/.*/g' | paste -sd '|' -)
fi
if ((${#EXCLUDE_PATTERNS[@]})); then
EXCLUDE_REGEX=$(printf '%s\n' "${EXCLUDE_PATTERNS[@]}" | sed 's/\./\\./g; s/\*/.*/g' | paste -sd '|' -)
fi
# Check if jq is available
if command -v jq &>/dev/null; then
process_with_jq() {
if [[ "$TOOL" == "aria2c" ]]; then
printf "%s" "$RESPONSE" | jq -r \
--arg endpoint "$HF_ENDPOINT" \
--arg repo_id "$DOWNLOAD_API_PATH" \
--arg token "$HF_TOKEN" \
--arg include_regex "$INCLUDE_REGEX" \
--arg exclude_regex "$EXCLUDE_REGEX" \
--arg revision "$REVISION" \
'
.siblings[]
| select(
.rfilename != null
and ($include_regex == "" or (.rfilename | test($include_regex)))
and ($exclude_regex == "" or (.rfilename | test($exclude_regex) | not))
)
| [
($endpoint + "/" + $repo_id + "/resolve/" + $revision + "/" + .rfilename),
" dir=" + (.rfilename | split("/")[:-1] | join("/")),
" out=" + (.rfilename | split("/")[-1]),
if $token != "" then " header=Authorization: Bearer " + $token else empty end,
""
]
| join("\n")
'
else
printf "%s" "$RESPONSE" | jq -r \
--arg endpoint "$HF_ENDPOINT" \
--arg repo_id "$DOWNLOAD_API_PATH" \
--arg include_regex "$INCLUDE_REGEX" \
--arg exclude_regex "$EXCLUDE_REGEX" \
--arg revision "$REVISION" \
'
.siblings[]
| select(
.rfilename != null
and ($include_regex == "" or (.rfilename | test($include_regex)))
and ($exclude_regex == "" or (.rfilename | test($exclude_regex) | not))
)
| ($endpoint + "/" + $repo_id + "/resolve/" + $revision + "/" + .rfilename)
'
fi
}
result=$(process_with_jq)
printf "%s\n" "$result" > "$LOCAL_DIR/$fileslist_file"
else
printf "%b[Warning] jq not installed, using grep/awk for metadata json parsing (slower). Consider installing jq for better parsing performance.%b\n" "$YELLOW" "$NC"
process_with_grep_awk() {
local include_pattern=""
local exclude_pattern=""
local output=""
if ((${#INCLUDE_PATTERNS[@]})); then
include_pattern=$(printf '%s\n' "${INCLUDE_PATTERNS[@]}" | sed 's/\./\\./g; s/\*/.*/g' | paste -sd '|' -)
fi
if ((${#EXCLUDE_PATTERNS[@]})); then
exclude_pattern=$(printf '%s\n' "${EXCLUDE_PATTERNS[@]}" | sed 's/\./\\./g; s/\*/.*/g' | paste -sd '|' -)
fi
local files=$(printf '%s' "$RESPONSE" | grep -o '"rfilename":"[^"]*"' | awk -F'"' '{print $4}')
if [[ -n "$include_pattern" ]]; then
files=$(printf '%s\n' "$files" | grep -E "$include_pattern")
fi
if [[ -n "$exclude_pattern" ]]; then
files=$(printf '%s\n' "$files" | grep -vE "$exclude_pattern")
fi
while IFS= read -r file; do
if [[ -n "$file" ]]; then
if [[ "$TOOL" == "aria2c" ]]; then
output+="$HF_ENDPOINT/$DOWNLOAD_API_PATH/resolve/$REVISION/$file"$'\n'
output+=" dir=$(dirname "$file")"$'\n'
output+=" out=$(basename "$file")"$'\n'
[[ -n "$HF_TOKEN" ]] && output+=" header=Authorization: Bearer $HF_TOKEN"$'\n'
output+=$'\n'
else
output+="$HF_ENDPOINT/$DOWNLOAD_API_PATH/resolve/$REVISION/$file"$'\n'
fi
fi
done <<< "$files"
printf '%s' "$output"
}
result=$(process_with_grep_awk)
printf "%s\n" "$result" > "$LOCAL_DIR/$fileslist_file"
fi
else
printf "%bResume from file list: $LOCAL_DIR/$fileslist_file%b\n" "$GREEN" "$NC"
fi
# Perform download
printf "${YELLOW}Starting download with $TOOL to $LOCAL_DIR...\n${NC}"
cd "$LOCAL_DIR"
if [[ "$TOOL" == "aria2c" ]]; then
aria2c --console-log-level=error --file-allocation=none -x "$THREADS" -j "$CONCURRENT" -s "$THREADS" -k 1M -c -i "$fileslist_file" --save-session="$fileslist_file"
elif [[ "$TOOL" == "wget" ]]; then
wget -x -nH --cut-dirs="$CUT_DIRS" ${HF_TOKEN:+--header="Authorization: Bearer $HF_TOKEN"} --input-file="$fileslist_file" --continue
fi
if [[ $? -eq 0 ]]; then
printf "${GREEN}Download completed successfully. Repo directory: $PWD\n${NC}"
else
printf "${RED}Download encountered errors.\n${NC}"
exit 1
fi

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import os
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
os.environ['HF_HOME'] = '/home/ht/huggingface/models'
from transformers import AutoTokenizer, AutoModelForSequenceClassification,TrainingArguments,Trainer
from datasets import load_dataset
dataset_name = "imdb"
task = "sentiment-analysis"
dataset = load_dataset(dataset_name).shuffle()
# 分割训练集和测试集
train_dataset = dataset["train"].select(range(20000)) # 使用部分数据加速训练
test_dataset = dataset["test"].select(range(5000))
model_name = "bert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
def preprocess_function(examples):
return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)
# 应用预处理
tokenized_train = train_dataset.map(preprocess_function, batched=True)
tokenized_test = test_dataset.map(preprocess_function, batched=True)
model = AutoModelForSequenceClassification.from_pretrained(
"bert-base-cased",
num_labels=2,
id2label={0: "negative", 1: "positive"},
label2id={"negative": 0, "positive": 1}
)
# 设置训练参数
training_args = TrainingArguments(
output_dir="./results",
eval_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs= 10,
weight_decay=0.01,
save_strategy="epoch",
load_best_model_at_end=True,
)
# 定义评估指标
import numpy as np
from sklearn.metrics import accuracy_score
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return {"accuracy": accuracy_score(labels, predictions)}
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_train,
eval_dataset=tokenized_test,
compute_metrics=compute_metrics,
)
trainer.train()
# 评估模型性能
eval_results = trainer.evaluate()
print(f"评估结果: {eval_results}")
# 保存微调后的模型
model.save_pretrained("./fine_tuned_bert_imdb")
tokenizer.save_pretrained("./fine_tuned_bert_imdb")
from transformers import pipeline
classifier = pipeline(
"text-classification",
model="./fine_tuned_bert_imdb",
tokenizer="./fine_tuned_bert_imdb"
)
# 示例预测
print(classifier("This movie was fantastic! I loved every minute of it."))
# data = dataset["train"]["text"][:10]
# inputs = tokenizer(data, padding=True, truncation=True, return_tensors="pt")
# outputs = model(**inputs)
# predictions = outputs.logits.argmax(dim=-1)
# labels = dataset["train"]["label"][:10]
# for i ,(predictions,label) in enumerate(zip(predictions, labels)):
# prediction_label = "positive" if predictions == 1 else "negative"
# true_lable = "positive" if label == 1 else "negative"
# print(f"Example {i+1}: Prediction: {prediction_label}, True Label: {true_lable}")