Instructions to use autotools/ai_video_studio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use autotools/ai_video_studio with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="autotools/ai_video_studio", filename="runtime/Auto Movie Reviewer/models/Phi-3.5-mini-balanced.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- 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: 2,474 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 | #!/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.
"""Data utilities for batch inference and evaluation.
Provides ``read_test_list()`` to parse JSONL test list files used by
``omnivoice.cli.infer_batch`` and evaluation scripts.
"""
import json
import logging
from pathlib import Path
def read_test_list(path):
"""Read a JSONL test list file.
Each line should be a JSON object. Only ``id`` and ``text`` are required;
all other fields are optional (default to ``None``):
id, text, ref_audio, ref_text, instruct,
language_id, language_name, duration, speed
Note: ``language_name`` is only used by evaluation scripts (under
``omnivoice/eval/``) for grouping and reporting results. The model
itself only consumes ``language_id``.
Returns a list of dicts.
"""
path = Path(path)
samples = []
with path.open("r", encoding="utf-8") as f:
for line_no, line in enumerate(f, 1):
line = line.strip()
if not line:
continue
try:
obj = json.loads(line)
except json.JSONDecodeError:
logging.warning(f"Skipping malformed JSON at line {line_no}: {line}")
continue
sample = {
"id": obj.get("id"),
"text": obj.get("text"),
"ref_audio": obj.get("ref_audio"),
"ref_text": obj.get("ref_text"),
"language_id": obj.get("language_id"),
"language_name": obj.get("language_name"),
"duration": obj.get("duration"),
"speed": obj.get("speed"),
"instruct": obj.get("instruct"),
}
samples.append(sample)
return samples
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