diff --git a/.runpod/hub.json b/.runpod/hub.json index becd7ee..4aca0f3 100644 --- a/.runpod/hub.json +++ b/.runpod/hub.json @@ -12,7 +12,7 @@ "presets": [], "env": [ { - "key": "MODEL_NAME", + "key": "OLLAMA_MODEL_NAME", "input": { "name": "Model Name", "type": "string", diff --git a/.runpod/tests.json b/.runpod/tests.json index 6d1180f..860a40b 100644 --- a/.runpod/tests.json +++ b/.runpod/tests.json @@ -13,7 +13,7 @@ "gpuCount": 1, "env": [ { - "key": "MODEL_NAME", + "key": "OLLAMA_MODEL_NAME", "value": "phi3" } ], diff --git a/README.md b/README.md index df4bf1c..62db75e 100644 --- a/README.md +++ b/README.md @@ -2,16 +2,16 @@ ## How to use -Start a runpod serverless with the docker container ``svenbrnn/runpod-ollama:latest``. Set ``MODEL_NAME`` environment to a model from ollama.com to automatically download a model. +Start a runpod serverless with the docker container ``svenbrnn/runpod-ollama:latest``. Set ``OLLAMA_MODEL_NAME`` environment to a model from ollama.com to automatically download a model. A mounted volume will be automatically used. [![RunPod](https://api.runpod.io/badge/SvenBrnn/runpod-worker-ollama)](https://www.runpod.io/console/hub/SvenBrnn/runpod-worker-ollama) ## Environment variables -| Variable Name | Description | Default Value | -|---------------|------------------------------------------|---------------------| -| `MODEL_NAME` | The name of the model to download | NULL | +| Variable Name | Description | Default Value | +|---------------------|------------------------------------------|---------------------| +| `OLLAMA_MODEL_NAME` | The name of the model to download | NULL | ## Test requests for runpod.io console diff --git a/src/engine.py b/src/engine.py index 3cdf5c0..e1a1393 100644 --- a/src/engine.py +++ b/src/engine.py @@ -18,8 +18,8 @@ class OllamaEngine: print ("OllamaEngine initialized") async def generate(self, job_input): - # Get model from MODEL_NAME defauting to llama3.2:1b - model = os.getenv("MODEL_NAME", "llama3.2:1b") + # Get model from OLLAMA_MODEL_NAME defauting to llama3.2:1b + model = os.getenv("OLLAMA_MODEL_NAME", "llama3.2:1b") # Depending if prompt is a string or a list, we need to handle it differently and send it to the OpenAI API if isinstance(job_input.llm_input, str):