Node-RED Flows for the StableCode-Completion-Alpha-3B-4K AI model
This repository contains a function node for Node-RED which can be used to run the Stability AI StableCode-Completion-Alpha-3B-4K model within a Node-RED flow. Inference is done on the CPU (without requiring any special harware) and still completes within a few seconds on a reasonably powerful computer.
Important: in contrast to my other flows for AI models, this one does not use llama.cpp but a similar program called ggml
Additionally, this repo also contains a function node to tokenize a prompt - embeddings calculation based on this model is not yet supported.
Having the inference, tokenization and embedding calculation as a self-contained function node gives you the possibility to create your own user interface or even use it as part of an autonomous agent.
Nota bene: these flows do not contain the actual model. You will have to download your own copy directly from Huggingface (use file stablecode-completion-alpha-3b-4k.ggmlv1.q8_0.bin).
Just a small note: if you like this work and plan to use it, consider "starring" this repository (you will find the "Star" button on the top right of this page), so that I know which of my repositories to take most care of.
This section shows you how to install Node.js, Node-RED, GGML and the flows from this repository - feel free to skip the steps for those components you already installed before.
"Node.js is a cross-platform, open-source server environment that can run on Windows, Linux, Unix, macOS, and more. Node.js is a back-end JavaScript runtime environment, runs on the V8 JavaScript engine, and executes JavaScript code outside a web browser." (according to Wikipedia)
Start by installing Node.js as described on their web page.
"Node-RED is a flow-based, low-code development tool for visual programming developed originally by IBM..." (according to Wikipedia).
If not already done, install Node-RED as described on their "Get Started" page.
GGML is a "Tensor library for machine learning" written in C
From all of the examples in that project, we only need GPT-NeoX. Install it as described in its README file, but skip the conversion step - we will later download an already converted model instead:
git clone https://github.com/ggerganov/ggml
cd ggml
mkdir build && cd build
cmake ..
make -j
Afterwards, copy the executable ./bin/gpt-neox
into the Node-RED "User Directory" (by default, this folder is located at $HOME/.node-red
).
If not already done, download the 8-bit quantization of the StableCode-Completion-Alpha-3B-4K model into the same folder that already contains your executables.
Nota bene: right now, the flows from this repository support the given model file only - if you prefer another one, you may simply change the model file name in the function nodes for text completion, tokenization and embeddings calculation.
Now import the desired nodes and flows - if you want them all, just import file StableCode-Completion-Alpha-3B-4K-Flows.json.
If you are new to Node-RED, just follow the instructions from their docs.
By default, all StableCode-Completion-Alpha-3B-4K function nodes expect their executables (and the model itself) in the folder $HOME/.node-red
(where $HOME
is the user's home directory)
If you prefer to store everything in a different folder, simply edit the "configure Settings" node from StableCode-Completion-Alpha-3B-4K-Flows.json and set globals.UserDir
to the fully qualified path of the directory you chose.
All function nodes expect their parameters as properties of the msg object. The prompt itself (or the input text to tokenize or calculate embeddings from) is expected in msg.payload
and will later be replaced by the function result.
All properties (except prompt or input text) are optional. If given, they should be strings (even if they contain numbers), this makes it simpler to extract them from an HTTP request.
Text completion supports the following properties:
payload
- this is the actual promptseed
- seed value for the internal pseudo random number generator (integer, default: -1, use random seed for <= 0)threads
- number of threads to use during computation (integer ≧ 1, default: 4)context
- size of the prompt context (0...4096, default: 512)predict
- number of tokens to predict (integer ≧ -1, default: 128, -1 = infinity)topk
- top-k sampling limit (integer ≧ 1, default: 40)topp
- top-p sampling limit (0.0...1.0, default: 0.9)temperature
- temperature (0.0...2.0, default: 0.8)batches
- batch size for prompt processing (integer ≧ 1, default: 8)
Tokenization supports the following properties:
payload
- this is the actual input textthreads
- number of threads to use during computation (integer ≧ 1, default: 4)context
- size of the prompt context (0...4096, default: 512)
Besides the sole function nodes for
this repository also contains example flows which answer incoming HTTP requests. The prompt itself and any desired parameters have to be passed as query parameters, the result of the called function will then be returned in the body of the HTTP response.
For inferencing, the following parameters are supported (most of them will be copied into a msg
property of the same name):
prompt
- will be copied intomsg.payload
seed
- will be copied intomsg.seed
threads
- will be copied intomsg.threads
context
- will be copied intomsg.context
predict
- will be copied intomsg.predict
topk
- will be copied intomsg.topk
topp
- will be copied intomsg.topp
temperature
- will be copied intomsg.temperature
batches
- will be copied intomsg.batches
The tokenization endpoint supports a subset of these parameters - as required by the respective function node.
If you have cURL installed (if not - but you want it - just follow the instructions found in their docs) (and assuming that your Node-RED installation is listening at port 1880) you may use the following commands to "smoke test" the imported flows:
curl "http://127.0.0.1:1880/stablecode?prompt=reverse%20the%20order%20of%20elements%20in%20a%20JavaScript%20array"
curl "http://127.0.0.1:1880/stablecode-tokenization?prompt=reverse%20the%20order%20of%20elements%20in%20a%20JavaScript%20array"