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feat(forge/llm): Add
LlamafileProvider
(#7091)
* Add minimal implementation of `LlamafileProvider`, a new `ChatModelProvider` for llamafiles. It extends `BaseOpenAIProvider` and only overrides methods that are necessary to get the system to work at a basic level. * Add support for `mistral-7b-instruct-v0.2`. This is the only model currently supported by `LlamafileProvider` because this is the only model I tested anything with. * Add instructions to use AutoGPT with llamafile in the docs at `autogpt/setup/index.md` * Add helper script to get it running quickly at `scripts/llamafile/serve.py` --------- Co-authored-by: Reinier van der Leer <[email protected]>
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*.llamafile | ||
*.llamafile.exe | ||
llamafile.exe |
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#!/usr/bin/env python3 | ||
""" | ||
Use llamafile to serve a (quantized) mistral-7b-instruct-v0.2 model | ||
Usage: | ||
cd <repo-root>/autogpt | ||
./scripts/llamafile/serve.py | ||
""" | ||
|
||
import os | ||
import platform | ||
import subprocess | ||
from pathlib import Path | ||
from typing import Optional | ||
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import click | ||
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LLAMAFILE = Path("mistral-7b-instruct-v0.2.Q5_K_M.llamafile") | ||
LLAMAFILE_URL = f"https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/{LLAMAFILE.name}" # noqa | ||
LLAMAFILE_EXE = Path("llamafile.exe") | ||
LLAMAFILE_EXE_URL = "https://github.com/Mozilla-Ocho/llamafile/releases/download/0.8.6/llamafile-0.8.6" # noqa | ||
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@click.command() | ||
@click.option( | ||
"--llamafile", | ||
type=click.Path(dir_okay=False, path_type=Path), | ||
help=f"Name of the llamafile to serve. Default: {LLAMAFILE.name}", | ||
) | ||
@click.option("--llamafile_url", help="Download URL for the llamafile you want to use") | ||
@click.option( | ||
"--host", help="Specify the address for the llamafile server to listen on" | ||
) | ||
@click.option( | ||
"--port", type=int, help="Specify the port for the llamafile server to listen on" | ||
) | ||
@click.option( | ||
"--force-gpu", | ||
is_flag=True, | ||
hidden=platform.system() != "Darwin", | ||
help="Run the model using only the GPU (AMD or Nvidia). " | ||
"Otherwise, both CPU and GPU may be (partially) used.", | ||
) | ||
def main( | ||
llamafile: Optional[Path] = None, | ||
llamafile_url: Optional[str] = None, | ||
host: Optional[str] = None, | ||
port: Optional[int] = None, | ||
force_gpu: bool = False, | ||
): | ||
print(f"type(llamafile) = {type(llamafile)}") | ||
if not llamafile: | ||
if not llamafile_url: | ||
llamafile = LLAMAFILE | ||
else: | ||
llamafile = Path(llamafile_url.rsplit("/", 1)[1]) | ||
if llamafile.suffix != ".llamafile": | ||
click.echo( | ||
click.style( | ||
"The given URL does not end with '.llamafile' -> " | ||
"can't get filename from URL. " | ||
"Specify the filename using --llamafile.", | ||
fg="red", | ||
), | ||
err=True, | ||
) | ||
return | ||
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if llamafile == LLAMAFILE and not llamafile_url: | ||
llamafile_url = LLAMAFILE_URL | ||
elif llamafile_url != LLAMAFILE_URL: | ||
if not click.prompt( | ||
click.style( | ||
"You seem to have specified a different URL for the default model " | ||
f"({llamafile.name}). Are you sure this is correct? " | ||
"If you want to use a different model, also specify --llamafile.", | ||
fg="yellow", | ||
), | ||
type=bool, | ||
): | ||
return | ||
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# Go to autogpt/scripts/llamafile/ | ||
os.chdir(Path(__file__).resolve().parent) | ||
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on_windows = platform.system() == "Windows" | ||
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if not llamafile.is_file(): | ||
if not llamafile_url: | ||
click.echo( | ||
click.style( | ||
"Please use --lamafile_url to specify a download URL for " | ||
f"'{llamafile.name}'. " | ||
"This will only be necessary once, so we can download the model.", | ||
fg="red", | ||
), | ||
err=True, | ||
) | ||
return | ||
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download_file(llamafile_url, llamafile) | ||
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if not on_windows: | ||
llamafile.chmod(0o755) | ||
subprocess.run([llamafile, "--version"], check=True) | ||
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if not on_windows: | ||
base_command = [f"./{llamafile}"] | ||
else: | ||
# Windows does not allow executables over 4GB, so we have to download a | ||
# model-less llamafile.exe and run that instead. | ||
if not LLAMAFILE_EXE.is_file(): | ||
download_file(LLAMAFILE_EXE_URL, LLAMAFILE_EXE) | ||
LLAMAFILE_EXE.chmod(0o755) | ||
subprocess.run([f".\\{LLAMAFILE_EXE}", "--version"], check=True) | ||
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base_command = [f".\\{LLAMAFILE_EXE}", "-m", llamafile] | ||
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if host: | ||
base_command.extend(["--host", host]) | ||
if port: | ||
base_command.extend(["--port", str(port)]) | ||
if force_gpu: | ||
base_command.extend(["-ngl", "9999"]) | ||
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subprocess.run( | ||
[ | ||
*base_command, | ||
"--server", | ||
"--nobrowser", | ||
"--ctx-size", | ||
"0", | ||
"--n-predict", | ||
"1024", | ||
], | ||
check=True, | ||
) | ||
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# note: --ctx-size 0 means the prompt context size will be set directly from the | ||
# underlying model configuration. This may cause slow response times or consume | ||
# a lot of memory. | ||
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def download_file(url: str, to_file: Path) -> None: | ||
print(f"Downloading {to_file.name}...") | ||
import urllib.request | ||
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urllib.request.urlretrieve(url, to_file, reporthook=report_download_progress) | ||
print() | ||
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def report_download_progress(chunk_number: int, chunk_size: int, total_size: int): | ||
if total_size != -1: | ||
downloaded_size = chunk_number * chunk_size | ||
percent = min(1, downloaded_size / total_size) | ||
bar = "#" * int(40 * percent) | ||
print( | ||
f"\rDownloading: [{bar:<40}] {percent:.0%}" | ||
f" - {downloaded_size/1e6:.1f}/{total_size/1e6:.1f} MB", | ||
end="", | ||
) | ||
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if __name__ == "__main__": | ||
main() |
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# Llamafile Integration Notes | ||
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Tested with: | ||
* Python 3.11 | ||
* Apple M2 Pro (32 GB), macOS 14.2.1 | ||
* quantized mistral-7b-instruct-v0.2 | ||
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## Setup | ||
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Download a `mistral-7b-instruct-v0.2` llamafile: | ||
```shell | ||
wget -nc https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/mistral-7b-instruct-v0.2.Q5_K_M.llamafile | ||
chmod +x mistral-7b-instruct-v0.2.Q5_K_M.llamafile | ||
./mistral-7b-instruct-v0.2.Q5_K_M.llamafile --version | ||
``` | ||
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Run the llamafile server: | ||
```shell | ||
LLAMAFILE="./mistral-7b-instruct-v0.2.Q5_K_M.llamafile" | ||
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"${LLAMAFILE}" \ | ||
--server \ | ||
--nobrowser \ | ||
--ctx-size 0 \ | ||
--n-predict 1024 | ||
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# note: ctx-size=0 means the prompt context size will be set directly from the | ||
# underlying model configuration. This may cause slow response times or consume | ||
# a lot of memory. | ||
``` | ||
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## TODOs | ||
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* `SMART_LLM`/`FAST_LLM` configuration: Currently, the llamafile server only serves one model at a time. However, there's no reason you can't start multiple llamafile servers on different ports. To support using different models for `smart_llm` and `fast_llm`, you could implement config vars like `LLAMAFILE_SMART_LLM_URL` and `LLAMAFILE_FAST_LLM_URL` that point to different llamafile servers (one serving a 'big model' and one serving a 'fast model'). | ||
* Authorization: the `serve.sh` script does not set up any authorization for the llamafile server; this can be turned on by adding arg `--api-key <some-key>` to the server startup command. However I haven't attempted to test whether the integration with autogpt works when this feature is turned on. | ||
* Test with other models |
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from .llamafile import ( | ||
LLAMAFILE_CHAT_MODELS, | ||
LLAMAFILE_EMBEDDING_MODELS, | ||
LlamafileCredentials, | ||
LlamafileModelName, | ||
LlamafileProvider, | ||
LlamafileSettings, | ||
) | ||
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__all__ = [ | ||
"LLAMAFILE_CHAT_MODELS", | ||
"LLAMAFILE_EMBEDDING_MODELS", | ||
"LlamafileCredentials", | ||
"LlamafileModelName", | ||
"LlamafileProvider", | ||
"LlamafileSettings", | ||
] |
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