Instructions to use autotools/ai_video_studio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use autotools/ai_video_studio with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf autotools/ai_video_studio:Q4_K_M # Run inference directly in the terminal: llama cli -hf autotools/ai_video_studio:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf autotools/ai_video_studio:Q4_K_M # Run inference directly in the terminal: llama cli -hf autotools/ai_video_studio:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf autotools/ai_video_studio:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf autotools/ai_video_studio:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf autotools/ai_video_studio:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf autotools/ai_video_studio:Q4_K_M
Use Docker
docker model run hf.co/autotools/ai_video_studio:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use autotools/ai_video_studio with Ollama:
ollama run hf.co/autotools/ai_video_studio:Q4_K_M
- Unsloth Studio
How to use autotools/ai_video_studio with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for autotools/ai_video_studio to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for autotools/ai_video_studio to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for autotools/ai_video_studio to start chatting
- Atomic Chat new
- Docker Model Runner
How to use autotools/ai_video_studio with Docker Model Runner:
docker model run hf.co/autotools/ai_video_studio:Q4_K_M
- Lemonade
How to use autotools/ai_video_studio with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull autotools/ai_video_studio:Q4_K_M
Run and chat with the model
lemonade run user.ai_video_studio-Q4_K_M
List all available models
lemonade list
File size: 5,613 Bytes
e6aed17 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 | #!/usr/bin/env python3
# Copyright 2026 Xiaomi Corp. (authors: Han Zhu)
#
# See ../../LICENSE for clarification regarding multiple authors
#
# 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.
"""Text processing utilities for TTS inference.
Provides:
- ``chunk_text_punctuation()``: Splits long text into model-friendly chunks at
sentence boundaries, with abbreviation-aware punctuation splitting.
- ``add_punctuation()``: Appends missing end punctuation (Chinese or English).
"""
from typing import List, Optional
SPLIT_PUNCTUATION = set(".,;:!?。,;:!?")
CLOSING_MARKS = set("\"'""')]》》>」】")
END_PUNCTUATION = {
";",
":",
",",
".",
"!",
"?",
"…",
")",
"]",
"}",
'"',
"'",
""",
"'",
";",
":",
",",
"。",
"!",
"?",
"、",
"……",
")",
"】",
""",
"'",
}
ABBREVIATIONS = {
"Mr.",
"Mrs.",
"Ms.",
"Dr.",
"Prof.",
"Sr.",
"Jr.",
"Rev.",
"Fr.",
"Hon.",
"Pres.",
"Gov.",
"Capt.",
"Gen.",
"Sen.",
"Rep.",
"Col.",
"Maj.",
"Lt.",
"Cmdr.",
"Sgt.",
"Cpl.",
"Co.",
"Corp.",
"Inc.",
"Ltd.",
"Est.",
"Dept.",
"St.",
"Ave.",
"Blvd.",
"Rd.",
"Mt.",
"Ft.",
"No.",
"Jan.",
"Feb.",
"Mar.",
"Apr.",
"Aug.",
"Sep.",
"Sept.",
"Oct.",
"Nov.",
"Dec.",
"i.e.",
"e.g.",
"vs.",
"Vs.",
"Etc.",
"approx.",
"fig.",
"def.",
}
def chunk_text_punctuation(
text: str,
chunk_len: int,
min_chunk_len: Optional[int] = None,
) -> List[str]:
"""
Splits the input tokens list into chunks according to punctuations,
avoiding splits on common abbreviations (e.g., Mr., No.).
"""
# 1. Split the tokens according to punctuations.
sentences = []
current_sentence = []
tokens_list = list(text)
for token in tokens_list:
# If the first token of current sentence is punctuation,
# append it to the end of the previous sentence.
if (
len(current_sentence) == 0
and len(sentences) != 0
and (token in SPLIT_PUNCTUATION or token in CLOSING_MARKS)
):
sentences[-1].append(token)
# Otherwise, append the current token to the current sentence.
else:
current_sentence.append(token)
# Split the sentence in positions of punctuations.
if token in SPLIT_PUNCTUATION:
is_abbreviation = False
if token == ".":
temp_str = "".join(current_sentence).strip()
if temp_str:
last_word = temp_str.split()[-1]
if last_word in ABBREVIATIONS:
is_abbreviation = True
if not is_abbreviation:
sentences.append(current_sentence)
current_sentence = []
# Assume the last few tokens are also a sentence
if len(current_sentence) != 0:
sentences.append(current_sentence)
# 2. Merge short sentences.
merged_chunks = []
current_chunk = []
for sentence in sentences:
if len(current_chunk) + len(sentence) <= chunk_len:
current_chunk.extend(sentence)
else:
if len(current_chunk) > 0:
merged_chunks.append(current_chunk)
current_chunk = sentence
if len(current_chunk) > 0:
merged_chunks.append(current_chunk)
# 4. Post-process: Check for undersized chunks and merge them
# with the previous chunk or next chunk (if it's the first chunk).
if min_chunk_len is not None:
first_chunk_short_flag = (
len(merged_chunks) > 0 and len(merged_chunks[0]) < min_chunk_len
)
final_chunks = []
for i, chunk in enumerate(merged_chunks):
if i == 1 and first_chunk_short_flag:
final_chunks[-1].extend(chunk)
else:
if len(chunk) >= min_chunk_len:
final_chunks.append(chunk)
else:
if len(final_chunks) == 0:
final_chunks.append(chunk)
else:
final_chunks[-1].extend(chunk)
else:
final_chunks = merged_chunks
chunk_strings = [
"".join(chunk).strip() for chunk in final_chunks if "".join(chunk).strip()
]
return chunk_strings
def add_punctuation(text: str):
"""Add punctuation if there is not in the end of text"""
text = text.strip()
if not text:
return text
if text[-1] not in END_PUNCTUATION:
is_chinese = any("\u4e00" <= char <= "\u9fff" for char in text)
text += "。" if is_chinese else "."
return text
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