13 Commits
9 changed files with 112 additions and 22 deletions
+2 -2
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@@ -5,7 +5,7 @@
"input": {
"prompt": "Hi! Who are you?"
},
"timeout": 120000
"timeout": 60000
}
],
"config": {
@@ -14,7 +14,7 @@
"env": [
{
"key": "LLAMA_SERVER_CMD_ARGS",
"value": "-hf unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q4_K_M -ctx_size 4096"
"value": "-hf unsloth/gemma-3-270m-it-GGUF:IQ2_XXS --ctx-size 512 -ngl 999"
}
],
"allowedCudaVersions": [
+2 -2
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@@ -14,10 +14,10 @@
{
"key": "LLAMA_SERVER_CMD_ARGS",
"input": {
"name": "Model Name",
"name": "Command line arguments for llama-server",
"type": "string",
"description": "Launch command line arguments (argv) for the llama-server binary. Do not define the port.",
"default": "-hf unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q4_K_M -ctx_size 4096",
"default": "-hf unsloth/gemma-3-270m-it-GGUF:Q6_K --ctx-size 4096 -ngl 99",
"advanced": false
}
},
+1 -1
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@@ -33,7 +33,7 @@ WORKDIR /work
ADD ./src /work
# Install runpod and its dependencies
RUN pip install -r requirements.txt && chmod +x /work/start.sh
RUN pip install -r ./requirements.txt && chmod +x /work/start.sh
# Set the entrypoint
ENTRYPOINT ["/bin/sh", "-c", "/work/start.sh"]
+7 -3
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@@ -4,8 +4,6 @@
# Serverless llama.cpp inference worker for RunPod
[![Runpod badge](https://api.runpod.io/badge/Jacob-ML/inference-worker)](https://console.runpod.io/hub/Jacob-ML/inference-worker)
This repository contains a serverless inference worker for running llama.cpp models on RunPod. It uses the `llama-server` image to provide an API for interacting with the models.
The following OpenAI API endpoints are supported:
@@ -15,6 +13,10 @@ The following OpenAI API endpoints are supported:
Streaming responses is also supported.
**Important!** This project is still relatively new. Please [open a new issue](https://github.com/Jacob-ML/inference-worker/issues/new) if you encounter any problems in order to get help.
**This is a fork of [SvenBrnn's `runpod-worker-ollama`](https://github.com/SvenBrnn/runpod-worker-ollama).**
## Setup
For the setup to work best, it is recommended to use a network volume attached to all workers which stores the model GGUFs and then reference those files in the launch arguments.
@@ -24,9 +26,11 @@ Make sure your RunPod worker has access to the network volume, i.e. is located i
The worker can be configured via environment variables set in the RunPod hub configuration:
- `LLAMA_SERVER_CMD_ARGS`: Command line arguments (argv) for the `llama-server` binary. Example: `-hf /path/to/model.gguf:Q4_K_M -ctx_size 4096`. **IMPORTANT**: Please do not define the port argument here, as the worker will always use port `3098` automatically.
- `LLAMA_SERVER_CMD_ARGS`: Command line arguments (argv) for the `llama-server` binary. Example: `-hf /path/to/model.gguf:Q4_K_M --ctx-size 4096`. **IMPORTANT**: Please do not define the port argument here, as the worker will always use port `3098` automatically.
- `MAX_CONCURRENCY`: Maximum number of concurrent requests the worker can handle. Default is `8`.
## License
Please see the [LICENSE](./LICENSE) file for more information.
[![Runpod badge](https://api.runpod.io/badge/Jacob-ML/inference-worker)](https://console.runpod.io/hub/Jacob-ML/inference-worker)
+6
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@@ -0,0 +1,6 @@
"""
This is an empty file used to mark the repository as a Runpod-compatible
serverless endpoint because they won't stop pretending it's not.
I'm tired.
"""
-1
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@@ -21,7 +21,6 @@ Typical usage:
"""
import json
import os
from dotenv import load_dotenv
from openai import OpenAI
+59
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@@ -0,0 +1,59 @@
"""
Finds the full LLM GGUF path from the Hugging Face cache.
"""
import os
import argparse
CACHE_DIR = "/runpod-volume/huggingface-cache/hub"
def find_model_path(model_name, gguf_in_repo="model.gguf"):
"""
Find the path to a cached model.
Args:
model_name: The model name from Hugging Face
Returns:
The full path to the cached model, or None if not found
"""
cache_name = model_name.replace("/", "--")
snapshots_dir = os.path.join(
CACHE_DIR, f"models--{cache_name}", "snapshots"
)
if os.path.exists(snapshots_dir):
snapshots = os.listdir(snapshots_dir)
if snapshots:
return os.path.join(snapshots_dir, snapshots[0], gguf_in_repo)
return None
def main():
"""
Main function to find and print the model path.
"""
parser = argparse.ArgumentParser(
description="Find the full GGUF path from the Hugging Face cache."
)
parser.add_argument(
"model", type=str, help="The model name from Hugging Face"
)
parser.add_argument(
"path",
type=str,
help="The path to the GGUF file within the model repository",
)
args = parser.parse_args()
model_path = find_model_path(args.model, args.path)
print(model_path, end="")
if __name__ == "__main__":
main()
-1
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@@ -1,4 +1,3 @@
runpod
python-dotenv
openai
orjson==3.10.14
+35 -12
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@@ -1,24 +1,33 @@
#!/bin/bash
# fail on error:
set -e -o pipefail
# This script starts the llama-server with the command line arguments
# specified in the environment variable LLAMA_SERVER_CMD_ARGS, ensuring
# that the server listens on port 3098. It also starts the handler.py
# script after the server is up and running.
cleanup() {
echo "Cleaning up..."
echo "start.sh: Cleaning up..."
pkill -P $$ # kill all child processes of the current script
exit 0
}
# check if the substring /workspace is in LLAMA_SERVER_CMD_ARGS
if [[ "$LLAMA_SERVER_CMD_ARGS" != *"/workspace"* ]]; then
echo "Tip: For reduced downloads and faster startup times, consider using a model stored in a network volume mounted to /workspace."
# check if $LLAMA_SERVER_CMD_ARGS is set
if [ -z "$LLAMA_SERVER_CMD_ARGS" ]; then
echo "start.sh: Warning: LLAMA_SERVER_CMD_ARGS is not set. Defaulting to -hf unsloth/gemma-3-270m-it-GGUF:IQ2_XXS --ctx-size 512 -ngl 999"
LLAMA_SERVER_CMD_ARGS="-hf unsloth/gemma-3-270m-it-GGUF:IQ2_XXS --ctx-size 512 -ngl 999"
fi
# check if the substring -port is in LLAMA_SERVER_CMD_ARGS
if [[ "$LLAMA_SERVER_CMD_ARGS" != *"-port"* ]]; then
echo "Error: You must not define -port in LLAMA_SERVER_CMD_ARGS, as port 3098 is required."
# check if the substring /workspace is in LLAMA_SERVER_CMD_ARGS
if [[ "$LLAMA_SERVER_CMD_ARGS" != *"/workspace"* ]]; then
echo "start.sh: Tip: For reduced downloads and faster startup times, consider using a model stored in the RunPod cache."
fi
# check if the substring --port is in LLAMA_SERVER_CMD_ARGS and if yes, raise an error:
if [[ "$LLAMA_SERVER_CMD_ARGS" == *"--port"* ]]; then
echo "start.sh: Error: You must not define --port in LLAMA_SERVER_CMD_ARGS, as port 3098 is required."
exit 1
fi
@@ -26,18 +35,27 @@ fi
trap cleanup SIGINT SIGTERM
# kill any existing llama-server processes
pgrep llama-server | xargs kill
echo "start.sh: Stopping existing llama-server instances (if any)..."
{
pkill llama-server 2>/dev/null
} || {
echo "start.sh: No llama-server running"
}
# we have a string with all the command line arguments in the env var LLAMA_SERVER_CMD_ARGS;
# it contains a.e. "-hf modelname -ctx_size 4096".
# it contains a.e. "-hf modelname --ctx-size 4096 -ngl 99".
echo "start.sh: Running llama-server $LLAMA_SERVER_CMD_ARGS --port 3098"
touch llama.server.log
# We need to pass these arguments to llama-server verbatim.
llama-server $LLAMA_SERVER_CMD_ARGS -port 3098 2>&1 | tee llama.server.log &
LD_LIBRARY_PATH=/app /app/llama-server $LLAMA_SERVER_CMD_ARGS --port 3098 2>&1 | tee llama.server.log &
LLAMA_SERVER_PID=$! # store the process ID (PID) of the background command
check_server_is_running() {
echo "Checking if llama-server is done initializing..."
echo "start.sh: Checking if llama-server is done initializing..."
if cat llama.server.log | grep -q "listening"; then
return 0 # success
@@ -46,9 +64,14 @@ check_server_is_running() {
fi
}
echo "start.sh: Waiting for llama-server to start..."
# wait for the server to start
while ! check_server_is_running; do
sleep 5
# we don't want to lose too much time, so we check very frequently
sleep 0.5
done
echo "start.sh: llama-server is up and running, delegating to the handler script."
python -u handler.py $1