https://github.com/rozek/node-red-flow-stablecode-completion-alpha-3b-4k
Node-RED Flows for the StableCode-Completion-Alpha-3B-4K AI model
https://github.com/rozek/node-red-flow-stablecode-completion-alpha-3b-4k
node-red node-red-flow stability-ai stablecode
Last synced: about 13 hours ago
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Node-RED Flows for the StableCode-Completion-Alpha-3B-4K AI model
- Host: GitHub
- URL: https://github.com/rozek/node-red-flow-stablecode-completion-alpha-3b-4k
- Owner: rozek
- License: mit
- Created: 2023-11-23T06:40:15.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-23T07:48:48.000Z (about 2 years ago)
- Last Synced: 2025-09-08T09:46:02.231Z (5 months ago)
- Topics: node-red, node-red-flow, stability-ai, stablecode
- Homepage:
- Size: 76.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# node-red-flow-stablecode-completion-alpha-3b-4k #
Node-RED Flows for the StableCode-Completion-Alpha-3B-4K AI model
This repository contains a function node for [Node-RED](https://nodered.org/) which can be used to run the [Stability AI](https://huggingface.co/stabilityai) [StableCode-Completion-Alpha-3B-4K model](https://huggingface.co/stabilityai/stablecode-completion-alpha-3b-4k) 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](https://github.com/ggerganov/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](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GGML) (use file [stablecode-completion-alpha-3b-4k.ggmlv1.q8_0.bin](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GGML/blob/main/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.
## Installation ##
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 ###
"_[Node.js](https://nodejs.org/en) 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](https://en.wikipedia.org/wiki/Node.js))
Start by [installing Node.js](https://nodejs.org/en) as described on their web page.
### Node-RED ###
"_[Node-RED](https://nodered.org/) is a flow-based, low-code development tool for visual programming developed originally by IBM..._" (according to [Wikipedia](https://en.wikipedia.org/wiki/Node-RED)).
If not already done, install Node-RED as described on their "[Get Started](https://nodered.org/#get-started)" page.
### GGML ###
[GGML](https://github.com/ggerganov/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](https://github.com/ggerganov/ggml/tree/master/examples/gpt-neox) 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 `$HOME/.node-red`).
### StableCode-Completion-Alpha-3B-4K Flows ###
If not already done, download the [8-bit quantization of the StableCode-Completion-Alpha-3B-4K model](https://huggingface.co/TheBloke/stablecode-completion-alpha-3b-4k-GGML/blob/main/stablecode-completion-alpha-3b-4k.ggmlv1.q8_0.bin) into the same folder that already contains your executables.
> Nota bene: right now, the flows from this repository support the given model file only - if you prefer another one, you may simply change the model file name in the function nodes for text completion, tokenization and embeddings calculation.
Now import the desired nodes and flows - if you want them all, just import file [StableCode-Completion-Alpha-3B-4K-Flows.json](https://raw.githubusercontent.com/rozek/node-red-flow-stablecode-completion-alpha-3b-4k/master/StableCode-Completion-Alpha-3B-4K-Flows.json).
If you are new to Node-RED, [just follow the instructions from their docs](https://nodered.org/docs/user-guide/editor/workspace/import-export).
## Configuration ##
By default, all StableCode-Completion-Alpha-3B-4K function nodes expect their executables (and the model itself) in the folder `$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](https://raw.githubusercontent.com/rozek/node-red-flow-stablecode-completion-alpha-3b-4k/master/StableCode-Completion-Alpha-3B-4K-Flows.json) and set `globals.UserDir` to the fully qualified path of the directory you chose.
## Function Node Usage ##
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 Node ###
Text completion supports the following properties:
* `payload` - this is the actual prompt
* `seed` - 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 Node ###
Tokenization supports the following properties:
* `payload` - this is the actual input text
* `threads` - number of threads to use during computation (integer ≧ 1, default: 4)
* `context` - size of the prompt context (0...4096, default: 512)
## HTTP Endpoint Usage ##
Besides the sole function nodes for
* [text completion](https://raw.githubusercontent.com/rozek/node-red-flow-stablecode-completion-alpha-3b-4k/master/StableCode-Completion-Alpha-3B-4K-Completion-Function.json),
* [tokenization](https://raw.githubusercontent.com/rozek/node-red-flow-stablecode-completion-alpha-3b-4k/master/StableCode-Completion-Alpha-3B-4K-Tokenization-Function.json) and
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 into `msg.payload`
* `seed` - will be copied into `msg.seed`
* `threads` - will be copied into `msg.threads`
* `context` - will be copied into `msg.context`
* `predict` - will be copied into `msg.predict`
* `topk` - will be copied into `msg.topk`
* `topp` - will be copied into `msg.topp`
* `temperature` - will be copied into `msg.temperature`
* `batches` - will be copied into `msg.batches`
The tokenization endpoint supports a subset of these parameters - as required by the respective function node.
## Examples ##
If you have [cURL](https://curl.se/) installed (if not - but you want it - just [follow the instructions found in their docs](https://everything.curl.dev/get)) (and assuming that your Node-RED installation is listening at port 1880) you may use the following commands to "smoke test" the imported flows:
### Text Completion ###
```
curl "http://127.0.0.1:1880/stablecode?prompt=reverse%20the%20order%20of%20elements%20in%20a%20JavaScript%20array"
```
### Tokenization ###
```
curl "http://127.0.0.1:1880/stablecode-tokenization?prompt=reverse%20the%20order%20of%20elements%20in%20a%20JavaScript%20array"
```
## License ##
[MIT License](LICENSE.md)