You can also use the ``--listen`` argument to make the server available on the networ, and/or the ```--share``` argument to enable a public Cloudflare endpoint.
To enable the basic image generation support (txt2img) set the environment variable SD_WEBUI_URL to point to your Stable Diffusion API ([Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui)).
This has been successfully tested with Alpaca, Koala, Vicuna, WizardLM and their variants, (ex. gpt4-x-alpaca, GPT4all-snoozy, stable-vicuna, wizard-vicuna, etc.) and many others. Models that have been trained for **Instruction Following** work best. If you test with other models please let me know how it goes. Less than satisfying results (so far) from: RWKV-4-Raven, llama, mpt-7b-instruct/chat.
For best results across all API endpoints, a model like [vicuna-13b-v1.3-GPTQ](https://huggingface.co/TheBloke/vicuna-13b-v1.3-GPTQ), [stable-vicuna-13B-GPTQ](https://huggingface.co/TheBloke/stable-vicuna-13B-GPTQ) or [airoboros-13B-gpt4-1.3-GPTQ](https://huggingface.co/TheBloke/airoboros-13B-gpt4-1.3-GPTQ) is a good start.
For good results with the [Completions](https://platform.openai.com/docs/api-reference/completions) API endpoint, in addition to the above models, you can also try using a base model like [falcon-7b](https://huggingface.co/tiiuae/falcon-7b) or Llama.
For good results with the [ChatCompletions](https://platform.openai.com/docs/api-reference/chat) or [Edits](https://platform.openai.com/docs/api-reference/edits) API endpoints you can use almost any model trained for instruction following - within the limits of the model. Be sure that the proper instruction template is detected and loaded or the results will not be good.
For the proper instruction format to be detected you need to have a matching model entry in your ```models/config.yaml``` file. Be sure to keep this file up to date.
A matching instruction template file in the characters/instruction-following/ folder will loaded and applied to format messages correctly for the model - this is critical for good results.
For example, the Wizard-Vicuna family of models are trained with the Vicuna 1.1 format. In the models/config.yaml file there is this matching entry:
```
.*wizard.*vicuna:
mode: 'instruct'
instruction_template: 'Vicuna-v1.1'
```
This refers to ```characters/instruction-following/Vicuna-v1.1.yaml```, which looks like this:
context: "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\n"
```
For most common models this is already setup, but if you are using a new or uncommon model you may need add a matching entry to the models/config.yaml and possibly create your own instruction-following template and for best results.
If you see this in your logs, it probably means that the correct format could not be loaded:
```
Warning: Loaded default instruction-following template for model.
Embeddings requires ```sentence-transformers``` installed, but chat and completions will function without it loaded. The embeddings endpoint is currently using the HuggingFace model: ```sentence-transformers/all-mpnet-base-v2``` for embeddings. This produces 768 dimensional embeddings (the same as the text-davinci-002 embeddings), which is different from OpenAI's current default ```text-embedding-ada-002``` model which produces 1536 dimensional embeddings. The model is small-ish and fast-ish. This model and embedding size may change in the future.
| model name | dimensions | input max tokens | speed | size | Avg. performance |
In short, the all-MiniLM-L6-v2 model is 5x faster, 5x smaller ram, 2x smaller storage, and still offers good quality. Stats from (https://www.sbert.net/docs/pretrained_models.html). To change the model from the default you can set the environment variable OPENEDAI_EMBEDDING_MODEL, ex. "OPENEDAI_EMBEDDING_MODEL=all-MiniLM-L6-v2".
Warning: You cannot mix embeddings from different models even if they have the same dimensions. They are not comparable.
Almost everything you use it with will require you to set a dummy OpenAI API key environment variable.
With the [official python openai client](https://github.com/openai/openai-python), you can set the OPENAI_API_BASE environment variable before you import the openai module, like so:
If needed, replace 127.0.0.1 with the IP/port of your server.
If using .env files to save the OPENAI_API_BASE and OPENAI_API_KEY variables, you can ensure compatibility by loading the .env file before loading the openai module, like so in python:
```
from dotenv import load_dotenv
load_dotenv()
import openai
```
With the [official Node.js openai client](https://github.com/openai/openai-node) it is slightly more more complex because the environment variables are not used by default, so small source code changes may be required to use the environment variables, like so:
```
const openai = OpenAI(Configuration({
apiKey: process.env.OPENAI_API_KEY,
basePath: process.env.OPENAI_API_BASE,
}));
```
For apps made with the [chatgpt-api Node.js client library](https://github.com/transitive-bullshit/chatgpt-api):
| /v1/embeddings | openai.Embedding.create() | Using Sentence Transformer, dimensions are different and may never be directly comparable to openai embeddings. |
| /v1/moderations | openai.Moderation.create() | does nothing. successfully. |
The model name setting is ignored in completions, but you may need to adjust the maximum token length to fit the model (ie. set to <2048tokensinsteadof4096,8k,etc).Tomitigatesomeofthis,themax_tokensvalueishalveduntilitislessthantruncation_lengthforthemodel(typically2k).
Streaming, temperature, top_p, max_tokens, stop, should all work as expected, but not all parameters are mapped correctly.
Some hacky mappings:
| OpenAI | text-generation-webui | note |
| --- | --- | --- |
| frequency_penalty | encoder_repetition_penalty | this seems to operate with a different scale and defaults, I tried to scale it based on range & defaults, but the results are terrible. hardcoded to 1.18 until there is a better way |
| presence_penalty | repetition_penalty | same issues as frequency_penalty, hardcoded to 1.0 |
| max_tokens | max_new_tokens | For Text Completions max_tokens is set smaller than the truncation_length minus the prompt length. This can cause no input to be generated if the prompt is too large. For ChatCompletions, the older chat messages may be dropped to fit the max_new_tokens requested |
defaults are mostly from openai, so are different. I use the openai defaults where I can and try to scale them to the webui defaults with the same intent.
| ✅❌ | openai-python (v0.25+) | https://github.com/openai/openai-python | only the endpoints from above are working. OPENAI_API_BASE=http://127.0.0.1:5001/v1 |
| ✅❌ | openai-node | https://github.com/openai/openai-node | only the endpoints from above are working. environment variables don't work by default, but can be configured (see above) |
| ✅❌ | chatgpt-api | https://github.com/transitive-bullshit/chatgpt-api | only the endpoints from above are working. environment variables don't work by default, but can be configured (see above) |
| ✅❌ | langchain | https://github.com/hwchase17/langchain | OPENAI_API_BASE=http://127.0.0.1:5001/v1 even with a good 30B-4bit model the result is poor so far. It assumes zero shot python/json coding. Some model tailored prompt formatting improves results greatly. |
| ✅❌ | Auto-GPT | https://github.com/Significant-Gravitas/Auto-GPT | OPENAI_API_BASE=http://127.0.0.1:5001/v1 Same issues as langchain. Also assumes a 4k+ context |
