Speculative decoding #73
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This is a benchmark review for experiment This pull request was cloned from Experiment configurationreview_config:
# User configuration for the review
# - benchmark - use the user config from the benchmark reviews
# - <value> - use the value directly
user_config:
enable_ai_review: true
enable_rule_comments: false
enable_complexity_comments: benchmark
enable_docstring_comments: benchmark
enable_security_comments: benchmark
enable_tests_comments: benchmark
enable_comment_suggestions: benchmark
enable_functionality_review: benchmark
enable_approvals: true
ai_review_config:
# The model responses to use for the experiment
# - benchmark - use the model responses from the benchmark reviews
# - llm - call the language model to generate responses
model_responses:
comments_model: benchmark
comment_validation_model: benchmark
comment_suggestion_model: benchmark
complexity_model: benchmark
docstrings_model: benchmark
functionality_model: benchmark
security_model: benchmark
tests_model: benchmark
# The pull request dataset to run the experiment on
pull_request_dataset:
- https://github.com/Bilbottom/sql-problems/pull/1
- https://github.com/Bilbottom/sql-learning-materials/pull/13
- https://github.com/Bilbottom/python-template/pull/3
- https://github.com/Bilbottom/sql-problems/pull/2
- https://github.com/gdsfactory/kfactory/pull/304
- https://github.com/mslepko/wc-daily-logs-emailer/pull/2
- https://github.com/mslepko/wc-daily-logs-emailer/pull/3
- https://github.com/jquagga/ttt/pull/94
- https://github.com/mslepko/wc-daily-logs-emailer/pull/5
- https://github.com/rbanffy/pip-chill/pull/75
- https://github.com/yaitoo/sqle/pull/45
- https://github.com/Catrofe/AvaliationSystemECommerce/pull/5
- https://github.com/Catrofe/AvaliationSystemECommerce/pull/6
- https://github.com/ultralytics/JSON2YOLO/pull/87
- https://github.com/ultralytics/flickr_scraper/pull/24
- https://github.com/nikhilbadyal/docker-py-revanced/pull/512
- https://github.com/MusicalNinjaDad/FizzBuzz/pull/10
- https://github.com/petermcd/monzo-api/pull/71
- https://github.com/christian80gabi/RecycleAI/pull/3
- https://github.com/christian80gabi/RecycleAI/pull/4
- https://github.com/nbhirud/system_update/pull/22
- https://github.com/nbhirud/system_update/pull/23
- https://github.com/agatma/sprint1-http-server/pull/1
- https://github.com/mraniki/cefi/pull/464
- https://github.com/Idrinth/api-bench/pull/915
- https://github.com/Idrinth/api-bench/pull/921
- https://github.com/dashmug/glue-utils/pull/28
- https://github.com/Idrinth/api-bench/pull/917
- https://github.com/sett-and-hive/sarif-to-comment-action/pull/286
- https://github.com/uncscode/particula/pull/445
review_comment_labels:
- label: correct
question: Is this comment correct?
- label: helpful
question: Is this comment helpful?
- label: comment-type
question: Is the comment type correct?
- label: comment-area
question: Is the comment area correct?
- label: llm-test
question: |
What type of LLM test could this comment become?
- 👍 - this comment is really good/important and we should always make it
- 👎 - this comment is really bad and we should never make it
- no reaction - don't turn this comment into an LLM test
# Benchmark reviews generated by running
# python -m scripts.experiment benchmark <experiment_name>
benchmark_reviews: []
|
SourceryAI
approved these changes
Apr 29, 2024
There was a problem hiding this comment.
Hey @sourcery-ai-experiments-bot - I've reviewed your changes and they look great!
Here's what I looked at during the review
- 🟡 General issues: 2 issues found
- 🟢 Security: all looks good
- 🟢 Testing: all looks good
- 🟢 Complexity: all looks good
Help me be more useful! Please click 👍 or 👎 on each comment to tell me if it was helpful.
Comment on lines
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| from transformers import ( | ||
| pipeline, | ||
| AutoModelForCausalLM, | ||
| AutoModelForSpeechSeq2Seq, | ||
| AutoProcessor, | ||
| ) |
There was a problem hiding this comment.
suggestion (code_refinement): Consider consolidating imports for clarity and maintainability.
Grouping related imports in a single block can improve readability and make the codebase easier to manage.
Suggested change
| from transformers import ( | |
| pipeline, | |
| AutoModelForCausalLM, | |
| AutoModelForSpeechSeq2Seq, | |
| AutoProcessor, | |
| ) | |
| from transformers import ( | |
| AutoModelForCausalLM, | |
| AutoModelForSpeechSeq2Seq, | |
| AutoProcessor, | |
| pipeline, | |
| ) |
There was a problem hiding this comment.
What type of LLM test could this comment become?
- 👍 - this comment is really good/important and we should always make it
- 👎 - this comment is really bad and we should never make it
- no reaction - don't turn this comment into an LLM test
| calljson = {**calljson, **calltext} | ||
| return calljson | ||
| # If we set TTT_TRANSFORMERS_MODEL_ID, let's use that directly | ||
| if os.environ.get("TTT_TRANSFORMERS_MODEL_ID", False): |
There was a problem hiding this comment.
suggestion (code_clarification): The default value in os.environ.get should be consistent with expected data types.
Using False as a default for an environment variable that is expected to be a string can be misleading. Consider using None or a default string value.
Suggested change
| if os.environ.get("TTT_TRANSFORMERS_MODEL_ID", False): | |
| if os.environ.get("TTT_TRANSFORMERS_MODEL_ID", None): |
There was a problem hiding this comment.
What type of LLM test could this comment become?
- 👍 - this comment is really good/important and we should always make it
- 👎 - this comment is really bad and we should never make it
- no reaction - don't turn this comment into an LLM test
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