70b9fc95de
* returning stop str if exists from gen * added chat template for firefunctionv2 * pulling tool vars from template * adding parsing for tool inputs/outputs * passing tool data from endpoint to chat template, adding tool_start to the stop list * loosened typing on the response tool call, leaning more on the user supplying a quality schema if they want a particular format * non streaming generation prototype * cleaning template * Continued work with type, ingestion into template, and chat template for fire func * Correction - streaming toolcall comes back as delta obj not inside chatcomprespchoice per chat_completion_chunk.py inside OAI lib. * Ruff Formating * Moved stop string and tool updates out of prompt creation func Updated tool pydantic to match OAI Support for streaming Updated generate tool calls to use flag within chat_template and insert tool reminder * Llama 3.1 chat templates Updated fire func template * renamed llama3.1 to chatml_with_headers.. * update name of template * Support for calling a tool start token rather than the string. Simplified tool_params Warning when gen_settings are being overidden becuase user set temp to 0 Corrected schema and tools to correct types for function args. Str for some reason * draft groq tool use model template * changed headers to vars for readablity (but mostly because some models are weird about newlines after headers, so this is an easier way to change globally) * Clean up comments and code in chat comp * Post processed tool call to meet OAI spec rather than forcing model to write json in a string in the middle of the call. * changes example back to args as json rather than string of json * Standardize chat templates to each other * cleaning/rewording * stop elements can also be ints (tokens) * Cleaning/formatting * added special tokens for tools and tool_response as specified in description * Cleaning * removing aux templates - going to live in llm-promp-templates repo instead * Tree: Format Signed-off-by: kingbri <bdashore3@proton.me> * Chat Completions: Don't include internal tool variables in OpenAPI Use SkipJsonSchema to supress inclusion with the OpenAPI JSON. The location of these variables may need to be changed in the future. Signed-off-by: kingbri <bdashore3@proton.me> * Templates: Deserialize metadata on template load Since we're only looking for specific template variables that are static in the template, it makes more sense to render when the template is initialized. Signed-off-by: kingbri <bdashore3@proton.me> * Tools: Fix comments Adhere to the format style of comments in the rest of the project. Signed-off-by: kingbri <bdashore3@proton.me> --------- Co-authored-by: Ben Gitter <gitterbd@gmail.com> Signed-off-by: kingbri <bdashore3@proton.me> |
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| .github | ||
| backends | ||
| colab | ||
| common | ||
| docker | ||
| endpoints | ||
| loras | ||
| models | ||
| sampler_overrides | ||
| templates | ||
| tests | ||
| update_scripts | ||
| .gitignore | ||
| config_sample.yml | ||
| formatting.bat | ||
| formatting.sh | ||
| LICENSE | ||
| main.py | ||
| pyproject.toml | ||
| README.md | ||
| start.bat | ||
| start.py | ||
| start.sh | ||
TabbyAPI
Important
In addition to the README, please read the Wiki page for information about getting started!
Note
Need help? Join the Discord Server and get the
Tabbyrole. Please be nice when asking questions.
A FastAPI based application that allows for generating text using an LLM (large language model) using the Exllamav2 backend
TabbyAPI is also the official API backend server for ExllamaV2.
Disclaimer
This project is marked as rolling release. There may be bugs and changes down the line. Please be aware that you might need to reinstall dependencies if needed.
TabbyAPI is a hobby project made for a small amount of users. It is not meant to run on production servers. For that, please look at other solutions that support those workloads.
Getting Started
Important
This README does not have instructions for setting up. Please read the Wiki.
Read the Wiki for more information. It contains user-facing documentation for installation, configuration, sampling, API usage, and so much more.
Features
- OpenAI compatible API
- Loading/unloading models
- HuggingFace model downloading
- Embedding model support
- JSON schema + Regex + EBNF support
- AI Horde support
- Speculative decoding via draft models
- Multi-lora with independent scaling (ex. a weight of 0.9)
- Inbuilt proxy to override client request parameters/samplers
- Flexible Jinja2 template engine for chat completions that conforms to HuggingFace
- Concurrent inference with asyncio
- Utilizes modern python paradigms
- Continuous batching engine using paged attention
- Fast classifer-free guidance
And much more. If something is missing here, PR it in!
Supported Model Types
TabbyAPI uses Exllamav2 as a powerful and fast backend for model inference, loading, etc. Therefore, the following types of models are supported:
-
Exl2 (Highly recommended)
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GPTQ
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FP16 (using Exllamav2's loader)
In addition, TabbyAPI supports parallel batching using paged attention for Nvidia Ampere GPUs and higher.
Contributing
Use the template when creating issues or pull requests, otherwise the developers may not look at your post.
If you have issues with the project:
-
Describe the issue in detail
-
If you have a feature request, please indicate it as such.
If you have a Pull Request
- Describe the pull request in detail, what, and why you are changing something
Acknowldgements
TabbyAPI would not exist without the work of other contributors and FOSS projects:
Developers and Permissions
Creators/Developers: