- remove disconnect_task
- move disconnect logic to a per-request handler that wraps cleanup operation and directly polls the request state with throttling
- exclusively signal disconnect with CancelledError
- rework completions endpoint to follow same approach as chat completions, share some code
- refactor OAI endpoints a bit
- correct behavior for batched completion requests
- make sure logprobs work for completion and streaming completion requests
- more tests
- remove ToolConfig, reduce to a single `tool_format` argument and hard-code extra args like start/end tokens
- dispatch to short, self-contained (and probably easily vibe coded) parser for each model type
- remove autodetection (seems infeasible since parsing effectively starts during streaming, and there is overlap between tool formats for different models)
- streamline xml parser and dedicate to qwen3_coder models
- add parsers for glm4.x, minimax-m2.x and mistral (seems shaky, probably because mistralai don't validate against hf)
- update docs
- move tool config from template_vars to separate yml config
- new per-gen stream collector used for both streaming and non-streaming requests to ensure logic is consistent for both
- move responsibility for switching between phases to stream collector
- collect tool calls during streaming and parse at the end of each gen
- prevent streaming empty content spans (be nice to clients)
- correctly aggregate usage stats for n>1 requests, always emit with last chunk in last gen to finish
- collect logprobs in model wrapper and correctly handle logprobs for multi-token chars etc.
- respect top_logprobs argument in request
- handle a number of edge cases like <think> tag being part of held string, etc.
- retain tool parsing and inference-abort fixes from #413, apply similar fix to non-stream request as well
Still TODO:
- testing and validation with more models and tool schemas (tested on Qwen so far)
- enable JSON constraint for JSON tool models
- possibly some pydantification
- documentation
When revisiting tool calls, the formats have more or less become standard.
For greater compatibility with templates, primarily use the message.tools
parameter and remove the extra custom metadata that is no longer required.
However, unlike other backends, tabbyAPI still uses template metadata
to declare what the tool start string is. This allows for template-level
customization along with giving more power to the user while the server
exists to consume rather than work on a case-by-case basis.
Signed-off-by: kingbri <8082010+kingbri1@users.noreply.github.com>
This stub fetches the add_eos_token field from the HF tokenizer config.
Ideally, this should be in the backend rather than tabby.
Signed-off-by: kingbri <8082010+kingbri1@users.noreply.github.com>
Granite3 default template uses strftime_now function.
Currently Jinja2 raises an exception because strftime_now is undefined and /v1/chat/completions endpoint doesn't work with these models when a template from the model metadata is used.
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>
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>
Having many utility functions for initialization doesn't make much sense.
Instead, handle anything regarding template creation inside the
class which reduces the amount of function imports.
Signed-off-by: kingbri <bdashore3@proton.me>
HuggingFace updated transformers to provide templates in a list for
tokenizers. Update to support this new format. Providing the name
of a template for the "prompt_template" value in config.yml will also
look inside the template list.
In addition, log if there's a template exception, but continue model
loading since it shouldn't shut down the application.
Signed-off-by: kingbri <bdashore3@proton.me>
Template modules grab all set vars, including ones that use runtime
vars. If a template var is set to a runtime var and a module is created,
an UndefinedError fires.
Use make_module instead to pass runtime vars when creating a template
module.
Resolves#92
Signed-off-by: kingbri <bdashore3@proton.me>
Adding the stop_strings var to chat templates will allow for the
template creator to specify stopping strings to add onto chat completions.
Thes get appended with existing stopping strings that are passed
in the API request. However, a sampler override with force: true will
override all stopping strings.
Signed-off-by: kingbri <bdashore3@proton.me>
Move common functions into their own folder and refactor the backends
to use their own folder as well.
Also cleanup imports and alphabetize import statments themselves.
Finally, move colab and docker into their own folders as well.
Signed-off-by: kingbri <bdashore3@proton.me>
