cappr
Completion After Prompt Probability. Make your LLM make a choice
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.9%) to scientific vocabulary
Keywords
huggingface
kv-cache
llamacpp
llm-inference
probability
prompt-engineering
text-classification
zero-shot
Last synced: 6 months ago
·
JSON representation
·
Repository
Completion After Prompt Probability. Make your LLM make a choice
Basic Info
- Host: GitHub
- Owner: kddubey
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://cappr.readthedocs.io
- Size: 5.14 MB
Statistics
- Stars: 80
- Watchers: 1
- Forks: 3
- Open Issues: 0
- Releases: 39
Topics
huggingface
kv-cache
llamacpp
llm-inference
probability
prompt-engineering
text-classification
zero-shot
Created almost 3 years ago
· Last pushed over 1 year ago
Metadata Files
Readme
License
Citation
README.md
CAPPr: Completion After Prompt Probability
Make your LLM pick from a list of choices.
Or compute the probability of a completion given a prompt, which may be
useful.
Squeeze more out
of open source LLMs.
Usage
Use a GGUF model
```python from llama_cpp import Llama from cappr.llama_cpp.classify import predict model = Llama("./TinyLLama-v0.Q8_0.gguf", verbose=False) prompt = """Gary told Spongebob a story: There once was a man from Peru; who dreamed he was eating his shoe. He woke with a fright, in the middle of the night, to find that his dream had come true. The moral of the story is to""" completions = ( "look at the bright side", "use your imagination", "eat shoes", ) pred = predict(prompt, completions, model) print(pred) # use your imagination ``` See [this page of the documentation](https://cappr.readthedocs.io/en/latest/select_a_language_model.html#llama-cpp) for more info on using GGUF models.Use a Hugging Face transformers model
```python from transformers import AutoModelForCausalLM, AutoTokenizer from cappr.huggingface.classify import predict model_name = "gpt2" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Which planet is closer to the Sun: Mercury or Earth?" completions = ("Mercury", "Earth") pred = predict(prompt, completions, model_and_tokenizer=(model, tokenizer)) print(pred) # Mercury ``` See [this page of the documentation](https://cappr.readthedocs.io/en/latest/select_a_language_model.html#hugging-face) for more info on using ``transformers`` models.Cache instructions to save time
Many prompts start with the same set of instructions, e.g., a system prompt plus a handful of example input-output pairs. Instead of repeatedly running the model on common instructions, cache them so that future computations are faster. Here's an example using [`cappr.huggingface.classify.cache_model`](https://cappr.readthedocs.io/en/latest/cappr.huggingface.classify.html#cappr.huggingface.classify.cache_model). ```python from transformers import AutoModelForCausalLM, AutoTokenizer from cappr.huggingface.classify import cache_model, predict # Load model and tokenizer model = AutoModelForCausalLM.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("gpt2") model_and_tokenizer = (model, tokenizer) # Create data prompt_prefix = '''Instructions: complete the sequence. Here are examples: A, B, C => D 1, 2, 3 => 4 Complete this sequence:''' prompts = ["X, Y =>", "10, 9, 8 =>"] completions = ["7", "Z", "Hi"] # Cache prompt_prefix because it's used for all prompts cached_model_and_tokenizer = cache_model( model_and_tokenizer, prompt_prefix ) # Compute preds = predict( prompts, completions, cached_model_and_tokenizer ) print(preds) # ['Z', '7'] ```Compute token-level log-probabilities
Here's an example using [`cappr.huggingface.classify.log_probs_conditional`](https://cappr.readthedocs.io/en/latest/cappr.huggingface.classify.html#cappr.huggingface.classify.log_probs_conditional). ```python from transformers import AutoModelForCausalLM, AutoTokenizer from cappr.huggingface.classify import log_probs_conditional # Load model and tokenizer model = AutoModelForCausalLM.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("gpt2") # Create data prompts = ["x y", "a b c"] completions = ["z", "d e"] # Compute log_probs_completions = log_probs_conditional( prompts, completions, model_and_tokenizer=(model, tokenizer) ) # Outputs (rounded) next to their symbolic representation print(log_probs_completions[0]) # [[-4.5], [[log Pr(z | x, y)], # [-5.6, -3.2]] [log Pr(d | x, y), log Pr(e | x, y, d)]] print(log_probs_completions[1]) # [[-9.7], [[log Pr(z | a, b, c)], # [-0.2, -0.03]] [log Pr(d | a, b, c), log Pr(e | a, b, c, d)]] ``` Efficiently aggregate these log-probabilities using [`cappr.utils.classify.agg_log_probs`](https://cappr.readthedocs.io/en/latest/cappr.utils.classify.html#cappr.utils.classify.agg_log_probs). For a slightly more advanced demo, see [`./demos/huggingface/dpo.ipynb`](./demos/huggingface/dpo.ipynb).Extract the final answer from a step-by-step completion
Step-by-step and chain-of-thought prompts are highly effective ways to get an LLM to "reason" about more complex tasks. But if you need a structured output, a step-by-step completion is unwieldy. Use CAPPr to extract the final answer from these types of completions, given a list of possible answers. See this idea in action [here in the documentation](https://cappr.readthedocs.io/en/latest/select_a_prompt_completion_format.html#wrangle-step-by-step-completions).Run in batches, predict probabilities
```python from transformers import AutoModelForCausalLM, AutoTokenizer from cappr.huggingface.classify import predict_proba # Load a model and its tokenizer model_name = "gpt2" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) prompts = [ "Stephen Curry is a", "Martina Navratilova was a", "Dexter, from the TV Series Dexter's Laboratory, is a", "LeBron James is a", ] # Each of the prompts could be completed with one of these: class_names = ("basketball player", "tennis player", "scientist") prior = ( 1/6, 1/6, 2/3 ) # Say I expect most of my data to have scientists # Run CAPPr pred_probs = predict_proba( prompts=prompts, completions=class_names, model_and_tokenizer=(model, tokenizer), batch_size=2, # whatever fits on your CPU/GPU prior=prior, ) # pred_probs[i,j] = probability that prompts[i] is classified as class_names[j] print(pred_probs.round(1)) # [[0.5 0.3 0.2] # [0.3 0.6 0.2] # [0.1 0.1 0.8] # [0.8 0.2 0. ]] # For each prompt, which completion is most likely? pred_class_idxs = pred_probs.argmax(axis=-1) preds = [class_names[pred_class_idx] for pred_class_idx in pred_class_idxs] print(preds) # ['basketball player', # 'tennis player', # 'scientist', # 'basketball player'] ```Run in batches, where each prompt has a different set of possible completions
Again, let's predict probabilities. ```python from transformers import AutoModelForCausalLM, AutoTokenizer from cappr.huggingface.classify import predict_proba_examples from cappr import Example # Load a model and its tokenizer model_name = "gpt2" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Create a sequence of Example objects representing your classification tasks examples = [ Example( prompt="Jodie Foster played", completions=("Clarice Starling", "Trinity in The Matrix"), ), Example( prompt="Batman, from Batman: The Animated Series, was played by", completions=("Pete Holmes", "Kevin Conroy", "Spongebob!"), prior= ( 1/3 , 2/3 , 0 ), ), ] # Run CAPPr pred_probs = predict_proba_examples( examples, model_and_tokenizer=(model, tokenizer) ) # pred_probs[i][j] = probability that examples[i].prompt is classified as # examples[i].completions[j] print([example_pred_probs.round(2) for example_pred_probs in pred_probs]) # [array([0.7, 0.3]), # array([0.03, 0.97, 0. ])] # For each example, which completion is most likely? pred_class_idxs = [ example_pred_probs.argmax() for example_pred_probs in pred_probs ] preds = [ example.completions[pred_class_idx] for example, pred_class_idx in zip(examples, pred_class_idxs) ] print(preds) # ['Clarice Starling', # 'Kevin Conroy'] ```See the demos for demonstrations
of slightly harder classification tasks.
For CAPPr, GPTQ models are the most computationally performant. These models are
compatible with cappr.huggingface.classify. See this page of the
documentation
for more info on using these models.
Documentation
https://cappr.readthedocs.io
Installation
See this page of the documentation.
Related work
See this page of the documentation.
Motivation
Reduce engineering complexity.
See this page of the documentation for more info.
Performance
How it works
You input a prompt string, a end_of_prompt string (a whitespace or empty) and a set
of candidate completion strings such that the string
python
{prompt}{end_of_prompt}{completion}
is a naturally flowing thought. CAPPr picks the completion which is mostly likely to
follow prompt by computing the
Completion
After
Prompt
Probability
as fleshed out in my question on Cross Validated.
Local development
See this page of the documentation.
Todo
I'm dumping todos here:
Feel free to raise issues ofc
Owner
- Login: kddubey
- Kind: user
- Location: My parent's house
- Company: UnEmployed.io (We're hiring!)
- Repositories: 2
- Profile: https://github.com/kddubey
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: CAPPr
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Kush
family-names: Dubey
email: kushdubey63@gmail.com
repository-code: 'https://github.com/kddubey/cappr'
url: 'https://cappr.readthedocs.io/'
abstract: >-
Completion After Prompt Probability. Make your LLM make a
choice
license: Apache-2.0
commit: cdf8275
version: 0.9.6
date-released: '2024-11-01'
GitHub Events
Total
- Release event: 2
- Watch event: 10
- Push event: 13
- Fork event: 1
- Create event: 2
Last Year
- Release event: 2
- Watch event: 10
- Push event: 13
- Fork event: 1
- Create event: 2
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 3
- Total pull requests: 1
- Average time to close issues: 3 months
- Average time to close pull requests: 14 minutes
- Total issue authors: 3
- Total pull request authors: 1
- Average comments per issue: 5.0
- Average comments per pull request: 2.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- galonpy (1)
- dnhkng (1)
- iamshnoo (1)
Pull Request Authors
- kddubey (1)
Top Labels
Issue Labels
question (1)
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 231 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 39
- Total maintainers: 1
pypi.org: cappr
Completion After Prompt Probability. Make your LLM make a choice
- Homepage: https://github.com/kddubey/cappr
- Documentation: https://cappr.readthedocs.io/en/latest/
- License: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2023-2024 Kush Dubey Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
-
Latest release: 0.9.6
published over 1 year ago
Rankings
Dependent packages count: 7.0%
Average: 18.7%
Dependent repos count: 30.5%
Maintainers (1)
Last synced:
6 months ago
Dependencies
.github/workflows/python-publish.yml
actions
- actions/checkout v3 composite
- actions/setup-python v3 composite
- pypa/gh-action-pypi-publish 27b31702a0e7fc50959f5ad993c78deac1bdfc29 composite
.github/workflows/test.yml
actions
- actions/checkout v3 composite
- actions/setup-python v4 composite
- codecov/codecov-action v3 composite
pyproject.toml
pypi
setup.py
pypi