add default behavior for undefined LLAMA_SERVER_CMD_ARGS

.runpod/hub.json

@@ -17,7 +17,7 @@
                     "name": "Model Name",
                     "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",
                     "advanced": false
                 }
             },

.runpod/tests.json

@@ -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:Q6_K --ctx-size 4096"
             }
         ],
         "allowedCudaVersions": [

Dockerfile

@@ -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"]

README.md

@@ -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:
 
@@ -24,9 +22,11 @@ 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)

src/requirements.txt

@@ -1,4 +1,3 @@
 runpod
 python-dotenv
 openai
-orjson==3.10.14

src/start.sh

@@ -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
+# check if $LLAMA_SERVER_CMD_ARGS is set
-if [[ "$LLAMA_SERVER_CMD_ARGS" != *"/workspace"* ]]; then
+if [ -z "$LLAMA_SERVER_CMD_ARGS" ]; then
-    echo "Tip: For reduced downloads and faster startup times, consider using a model stored in a network volume mounted to /workspace."
+    echo "start.sh: Warning: LLAMA_SERVER_CMD_ARGS is not set. Defaulting to -hf unsloth/gemma-3-270m-it-GGUF:Q6_K --ctx-size 4096"
+    LLAMA_SERVER_CMD_ARGS="-hf unsloth/gemma-3-270m-it-GGUF:Q6_K --ctx-size 4096"
 fi
 
-# check if the substring -port is in LLAMA_SERVER_CMD_ARGS
+# check if the substring /workspace is in LLAMA_SERVER_CMD_ARGS
-if [[ "$LLAMA_SERVER_CMD_ARGS" != *"-port"* ]]; then
+if [[ "$LLAMA_SERVER_CMD_ARGS" != *"/workspace"* ]]; then
-    echo "Error: You must not define -port in LLAMA_SERVER_CMD_ARGS, as port 3098 is required."
+    echo "start.sh: Tip: For reduced downloads and faster startup times, consider using a model stored in a network volume mounted to /workspace."
+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".
+
+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 +64,13 @@ 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
 done
 
+echo "start.sh: llama-server is up and running, delegating to the handler script."
+
 python -u handler.py $1