* Add 'models' endpoint to server
* Add test for new 'models' server endpoint
* Check hf_cache for mlx models
* update tests to check hf_cache for models
* simplify test
* doc
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* Predict stop sequence matches during streaming
Check for overlap of stop sequences and the tokens array for potential sequence matches after more tokens get generated. Generate tokens until we can confirm that the stop sequence is not met.
* fix typo
* Change sequence_overlap logic
* range isn't inclusive, add 1 to max_overlap
* Add test_server.py
Added a test for the sequence_overlap method
* nits
* eos sequence
* finalize
---------
Co-authored-by: Y4hL <43219534+Y4hL@users.noreply.github.com>
Co-authored-by: Awni Hannun <awni@apple.com>
* Added functionality to load in adapters through post-requests so you do not need to restart the server
* ran pre-commit
* nits
* fix test
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* Initial implementation
* Fix handling of return_step_logits in return
* Fixed OpenAI parameter expectations and logprob structure and datatypes
* pre-commit black formatting
* Remove unused parameter
* fix log probs
* fix colorize
* nits in server
* nits in server
* Fix top_logprobs structure (a dict) and include tokens in logprobs response
* nits
* fix types
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* Tweaks to run dspy-produced calls to the server, with gemma template.
following comment https://github.com/stanfordnlp/dspy/issues/385#issuecomment-1998939936
can try it out with:
```sh
python -m server --model mlx-community/gemma-1.1-7b-it-4bit --port 1143
```
modulo patching the relative imports in server.py
```
-from .tokenizer_utils import TokenizerWrapper
-from .utils import generate_step, load
+from mlx_lm.tokenizer_utils import TokenizerWrapper
+from mlx_lm.utils import generate_step, load
```
and then, ont the dspy side:
```python
import dspy
lm = dspy.OpenAI(model_type="chat", api_base="http://localhost:11434/v1/", api_key="not_needed", max_tokens=250)
lm("hello")
```
* simpler way to validate float or int
* remove logic that works around incompatible templates, too gemma specific
* tweak messages for common denominator
* use generate.py workaround for DBXR
* put behind flag
* oops
* Solution to chat template issue: pass in a custom template!
The template should likely adhere to the OpenAI chat model.
Here is such a template for Gemma.
--chat-template "{{ bos_token }}{% set extra_system = '' %}{% for message in messages %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{% if role == 'system' %}{% set extra_system = extra_system + message['content'] %}{% else %}{% if role == 'user' and extra_system %}{% set message_system = 'System: ' + extra_system %}{% else %}{% set message_system = '' %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message_system + message['content'] | trim + '<end_of_turn>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}"
* remove convoluted solution
* Tweak for when None is provided explicitly, and must be set to [] too.
For example, the outlines library provides None explicitly.
* style
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* Add support for setting MLX cache limit in GB
* Add support for setting MLX cache limit in GB in mlx_lm.server
* format
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* Use named tuple from typing for typehints
* Add type hints
* Simplify expression
* Type hint fix
* Improved do_POST logic
Use a map of endpoints to methods to reduce redundancy in code
* Fix format
* Improve redundancy
Call method dynamically instead of writing out all arguments twice
* Send response instead of returning
* Fix typo
* Revert change
* Make adapter_file as Optional
* Mark formatter as optional
* format
* Create message generator
Store response data that stays static for the duration of the response inside of the object:
system_fingerprint
request_id
object_type
requested_model
Created a message generator, that dynamically creates messages from the metadata stored inside of the object, and the data from the model pipeline
* Remove leftover
* Update parameters to reflect new object structure
No longer pass all arguments between functions, but use the stores values inside of the object
* Parse body before calling request specific methods
* Call super init
* Update server.py
* Fixed outdated documentation parameter name
* Add documentation
* Fix sending headers twice
During testing I found that when using the streaming option, headers have always been sent twice. This should fix that
* Simplify streaming code by using guard clauses
Don't wrap wfile writes in try blocks, the server class has its own try block to prevent crashing
* Bug fix
* Use Content-Length header
Let the completion type specific methods finish sending the headers. This allows us to send the Content-Length header as the model returns a completion.
* Update utils.py
* Add top_p documentation
* Type hint model and tokenizer as required
* Use static system fingerprint
System fingerprint now stays the same across requests
* Make type hint more specific
* Bug Fix
Supplying less than 2 models to merge would raise ValueError and calls len on unbound "models". Should be "model_paths" instead.
Mark upload_repo as optional
* Move more of the shared code into do_POST
Processing stop_id_sequences is done no matter the request endpoint or type, move it into the shared section. handle_ methods now just return the prompt in mx.array form.
* Store stop_id_sequences as lists instead of np
During testing I found that letting the tokenizer return values as python lists and converting them to mlx arrays was around 20% faster than having the tokenizer convert them to np, and from np to mlx. This allows makes it so numpy no longer needs to be imported.
* Update stop_id_sequences docs
* Turn if check to non-inclusive
Only continue if buffer is smaller
* Documentation fix
* Cleared method names
Instead of handle_stream and generate_competion, we should name it handle_completion.
Instead of handle_completions and handle_chat_completions, we should name it handle_text_completions, since both are completions, calling it text completions should make it more descriptive
* Make comment clearer
* fix format
* format
* Don't serve local directory
BaseHTTPRequestHandler serves the current directory by default. Definitely not intended behaviour. Remove the "do_HEAD" and "do_GET" methods.
* Fix typo in method name
I assume hanlde_stream was intended to be called handle_stream
* Fix outdated typehint
load_model returns nn.Module, however fetch_from_hub was not updated to reflect the change
* Add some more type hints
* Add warnings for using in prod
Add a warning to README and runtime, discouraging use in production. The warning is the same as on the python docs for HTTPServer https://docs.python.org/3/library/http.server.html
* format
* nits
---------
Co-authored-by: Awni Hannun <awni@apple.com>
