There are two ways to load a model:
1. Via the load endpoint
2. Inline with a completion
The defaults were not applying on the inline load, so rewrite to fix
that. However, while doing this, set up a defaults dictionary rather
than comparing it at runtime and remove the pydantic default lambda
on all the model load fields.
This makes the code cleaner and establishes a clear config tree for
loading models.
Signed-off-by: kingbri <bdashore3@proton.me>
Using aiofiles, there's no longer a possiblity of blocking file operations
that can hang up the event loop. In addition, partially migrate
classes to use asynchronous init instead of the normal python magic method.
The only exception is config, since that's handled in the synchonous
init before the event loop starts.
Signed-off-by: kingbri <bdashore3@proton.me>
Like tabby_config.yml in the model's folder, a custom template can
also be provided via tabby_template.yml in addition to the existing
templates folder. The config.yml always takes priority.
Signed-off-by: kingbri <bdashore3@proton.me>
Adds DRY support based on the current exl2 dev API. Only change for
optimization is dry_max_ngram instead of using a closed range.
Currently, DRY range is aliased to dry_max_ngram.
Signed-off-by: kingbri <bdashore3@proton.me>
- add models for config options
- add function to regenerate config.yml
- replace references to config with pydantic compatible references
- remove unnecessary unwrap() statements
TODO:
- auto generate env vars
- auto generate argparse
- test loading a model
The config categories can have defined separation, but preserve
the dynamic nature of adding new config options by making all the
internal class vars as dictionaries.
This was necessary since storing global callbacks stored a state
of the previous global_config var that wasn't populated.
Signed-off-by: kingbri <bdashore3@proton.me>
Using "auto" for rope alpha removes ambiguity on how to explicitly
enable automatic rope calculation. The same behavior of None -> auto
calculate still exists, but can be overwritten if a model's tabby_config.yml
includes `rope_alpha`.
Signed-off-by: kingbri <bdashore3@proton.me>
It's best to pass them down the config stack.
API/User config.yml -> model config.yml -> model config.json -> fallback.
Doing this allows for seamless flow and yielding control to each
member in the stack.
Signed-off-by: kingbri <bdashore3@proton.me>
Storing a pathlib type makes it easier to manipulate the model
directory path in the long run without constantly fetching it
from the config.
Signed-off-by: kingbri <bdashore3@proton.me>
Use the tensor parallel loader when the flag is enabled. The new loader
has its own autosplit implementation, so gpu_split_auto isn't valid
here.
Also make it easier to determine which cache type to use rather than
multiple if/else statements.
Signed-off-by: kingbri <bdashore3@proton.me>
Metadata is generated via a template's module. This requires a single
iteration through the template. If a template tries to access a passed
variable that doesn't exist, it will error.
Therefore, generate the metadata at runtime to prevent these errors
from happening. To optimize further, cache the metadata after the
first generation to prevent the expensive call of making a template
module.
Signed-off-by: kingbri <bdashore3@proton.me>
* 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>
Previously, when a SIGINT was emitted and a model load is running,
the API didn't shut down until the load finished due to waitng for
the lock. However, when shutting down, the lock doesn't matter since
the process is being killed anyway.
Signed-off-by: kingbri <bdashore3@proton.me>
The async signal exit function should be the internal for exiting
the program. In addition, prevent the handler from being called
twice by adding a boolean. May become an asyncio event later on.
In addition, make sure to skip_wait when running model.unload.
Signed-off-by: kingbri <bdashore3@proton.me>
Embedding models are managed on a separate backend, but are run
in parallel with the model itself. Therefore, manage this in a separate
container with separate routes.
Signed-off-by: kingbri <bdashore3@proton.me>
