| Name | cappr JSON |
| Version |
0.9.1
JSON |
| download |
| home_page | None |
| Summary | Completion After Prompt Probability. Make your LLM make a choice |
| upload_time | 2024-06-30 20:53:34 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
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# CAPPr: Completion After Prompt Probability
[](https://www.python.org/downloads/release/python-380/)
[](https://github.com/kddubey/cappr/actions/workflows/test.yml)
[](https://codecov.io/gh/kddubey/cappr)
[](https://pypi.org/project/cappr/)
[](https://opensource.org/licenses/Apache-2.0)
<!-- [](https://cappr.readthedocs.io/en/latest/?badge=latest) -->
Make your LLM pick from a list of choices. <br>
Or compute the probability of a completion given a prompt, which may be
[useful](https://cappr.readthedocs.io/en/latest/related_work.html). <br>
Squeeze [more](https://cappr.readthedocs.io/en/latest/statistical_performance.html) out
of open source LLMs.
## Usage
<details>
<summary>Use a GGUF model</summary>
```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.
</details>
<details>
<summary>Use a Hugging Face transformers model</summary>
```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.
</details>
<details>
<summary>Cache instructions to save time</summary>
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 = ["a, b, c =>", "X, Y =>"]
completions = ["d", "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)
# ['d', 'Z']
```
</details>
<details>
<summary>Use a model from the OpenAI API</summary>
This model must be compatible with the
[/v1/completions](https://platform.openai.com/docs/models/model-endpoint-compatibility)
endpoint
([excluding](https://cappr.readthedocs.io/en/latest/select_a_language_model.html#openai)
``gpt-3.5-turbo-instruct``).
```python
from cappr.openai.classify import predict
prompt = """
Tweet about a movie: "Oppenheimer was pretty good. But 3 hrs...cmon Nolan."
This tweet contains the following criticism:
""".strip("\n")
completions = ("bad message", "too long", "unfunny")
pred = predict(prompt, completions, model="text-ada-001")
print(pred)
# too long
```
See [this page of the
documentation](https://cappr.readthedocs.io/en/latest/select_a_language_model.html#openai)
for more info on using OpenAI models.
</details>
<details>
<summary>Extract the final answer from a step-by-step completion</summary>
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).
</details>
<details>
<summary>Run in batches, predict probabilities</summary>
```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=32, # 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']
```
</details>
<details>
<summary>Run in batches, where each prompt has a different set of possible completions
</summary>
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']
```
</details>
See the [`demos`](https://github.com/kddubey/cappr/blob/main/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](https://cappr.readthedocs.io/en/latest/select_a_language_model.html#hugging-face)
for more info on using these models.
## Documentation
https://cappr.readthedocs.io
## Installation
See [this page of the
documentation](https://cappr.readthedocs.io/en/latest/installation.html).
## Related work
See [this page of the
documentation](https://cappr.readthedocs.io/en/latest/related_work.html).
## Motivation
Reduce engineering complexity.
See [this page of the
documentation](https://cappr.readthedocs.io/en/latest/motivation.html) for more info.
## Performance
[Statistical performance](https://cappr.readthedocs.io/en/latest/statistical_performance.html)
[Computational performance](https://cappr.readthedocs.io/en/latest/computational_performance.html)
## 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—
> **C**ompletion<br>
**A**fter<br>
**P**rompt<br>
**Pr**obability<br>
—as fleshed out in my [question on Cross
Validated](https://stats.stackexchange.com/q/601159/337906).
## Local development
See [this page of the documentation](https://cappr.readthedocs.io/en/latest/local.html).
## Todo
I'm dumping todos here:
[Code changes](https://github.com/users/kddubey/projects/1/views/1)
[Reseach experiments](https://github.com/users/kddubey/projects/2)
Feel free to raise issues ofc
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"description": "# CAPPr: Completion After Prompt Probability\n\n[](https://www.python.org/downloads/release/python-380/)\n[](https://github.com/kddubey/cappr/actions/workflows/test.yml)\n[](https://codecov.io/gh/kddubey/cappr)\n[](https://pypi.org/project/cappr/)\n[](https://opensource.org/licenses/Apache-2.0)\n\n<!-- [](https://cappr.readthedocs.io/en/latest/?badge=latest) -->\n\nMake your LLM pick from a list of choices. <br>\nOr compute the probability of a completion given a prompt, which may be\n[useful](https://cappr.readthedocs.io/en/latest/related_work.html). <br>\nSqueeze [more](https://cappr.readthedocs.io/en/latest/statistical_performance.html) out\nof open source LLMs.\n\n\n## Usage\n\n<details>\n<summary>Use a GGUF model</summary>\n\n```python\nfrom llama_cpp import Llama\nfrom cappr.llama_cpp.classify import predict\n\nmodel = Llama(\"./TinyLLama-v0.Q8_0.gguf\", verbose=False)\n\nprompt = \"\"\"Gary told Spongebob a story:\nThere once was a man from Peru; who dreamed he was eating his shoe. He\nwoke with a fright, in the middle of the night, to find that his dream\nhad come true.\n\nThe moral of the story is to\"\"\"\n\ncompletions = (\n \"look at the bright side\",\n \"use your imagination\",\n \"eat shoes\",\n)\n\npred = predict(prompt, completions, model)\nprint(pred)\n# use your imagination\n```\n\nSee [this page of the\ndocumentation](https://cappr.readthedocs.io/en/latest/select_a_language_model.html#llama-cpp)\nfor more info on using GGUF models.\n</details>\n\n\n<details>\n<summary>Use a Hugging Face transformers model</summary>\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nfrom cappr.huggingface.classify import predict\n\nmodel_name = \"gpt2\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name)\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\nprompt = \"Which planet is closer to the Sun: Mercury or Earth?\"\ncompletions = (\"Mercury\", \"Earth\")\n\npred = predict(prompt, completions, model_and_tokenizer=(model, tokenizer))\nprint(pred)\n# Mercury\n```\n\nSee [this page of the\ndocumentation](https://cappr.readthedocs.io/en/latest/select_a_language_model.html#hugging-face)\nfor more info on using ``transformers`` models.\n</details>\n\n\n<details>\n<summary>Cache instructions to save time</summary>\n\nMany prompts start with the same set of instructions, e.g., a system prompt plus a\nhandful of example input-output pairs. Instead of repeatedly running the model on common\ninstructions, cache them so that future computations are faster.\n\nHere's an\nexample using\n[`cappr.huggingface.classify.cache_model`](https://cappr.readthedocs.io/en/latest/cappr.huggingface.classify.html#cappr.huggingface.classify.cache_model).\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nfrom cappr.huggingface.classify import cache_model, predict\n\n# Load model and tokenizer\nmodel = AutoModelForCausalLM.from_pretrained(\"gpt2\")\ntokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\nmodel_and_tokenizer = (model, tokenizer)\n\n# Create data\nprompt_prefix = '''Instructions: complete the sequence.\nHere are examples:\nA, B, C => D\n1, 2, 3 => 4\n\nComplete this sequence:'''\n\nprompts = [\"a, b, c =>\", \"X, Y =>\"]\ncompletions = [\"d\", \"Z\", \"Hi\"]\n\n# Cache prompt_prefix because it's used for all prompts\ncached_model_and_tokenizer = cache_model(\n model_and_tokenizer, prompt_prefix\n)\n\n# Compute\npreds = predict(\n prompts, completions, cached_model_and_tokenizer\n)\nprint(preds)\n# ['d', 'Z']\n```\n</details>\n\n\n<details>\n<summary>Use a model from the OpenAI API</summary>\n\nThis model must be compatible with the\n[/v1/completions](https://platform.openai.com/docs/models/model-endpoint-compatibility)\nendpoint\n([excluding](https://cappr.readthedocs.io/en/latest/select_a_language_model.html#openai)\n``gpt-3.5-turbo-instruct``).\n\n```python\nfrom cappr.openai.classify import predict\n\nprompt = \"\"\"\nTweet about a movie: \"Oppenheimer was pretty good. But 3 hrs...cmon Nolan.\"\nThis tweet contains the following criticism:\n\"\"\".strip(\"\\n\")\n\ncompletions = (\"bad message\", \"too long\", \"unfunny\")\n\npred = predict(prompt, completions, model=\"text-ada-001\")\nprint(pred)\n# too long\n```\n\nSee [this page of the\ndocumentation](https://cappr.readthedocs.io/en/latest/select_a_language_model.html#openai)\nfor more info on using OpenAI models.\n</details>\n\n\n<details>\n<summary>Extract the final answer from a step-by-step completion</summary>\n\nStep-by-step and chain-of-thought prompts are highly effective ways to get an LLM to\n\"reason\" about more complex tasks. But if you need a structured output, a step-by-step\ncompletion is unwieldy. Use CAPPr to extract the final answer from these types of\ncompletions, given a list of possible answers.\n\nSee this idea in action [here in the\ndocumentation](https://cappr.readthedocs.io/en/latest/select_a_prompt_completion_format.html#wrangle-step-by-step-completions).\n</details>\n\n\n<details>\n<summary>Run in batches, predict probabilities</summary>\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nfrom cappr.huggingface.classify import predict_proba\n\n# Load a model and its tokenizer\nmodel_name = \"gpt2\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name)\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\nprompts = [\n \"Stephen Curry is a\",\n \"Martina Navratilova was a\",\n \"Dexter, from the TV Series Dexter's Laboratory, is a\",\n \"LeBron James is a\",\n]\n\n# Each of the prompts could be completed with one of these:\nclass_names = (\"basketball player\", \"tennis player\", \"scientist\")\nprior = ( 1/6, 1/6, 2/3 )\n# Say I expect most of my data to have scientists\n\n# Run CAPPr\npred_probs = predict_proba(\n prompts=prompts,\n completions=class_names,\n model_and_tokenizer=(model, tokenizer),\n batch_size=32, # whatever fits on your CPU/GPU\n prior=prior,\n)\n\n# pred_probs[i,j] = probability that prompts[i] is classified as class_names[j]\nprint(pred_probs.round(1))\n# [[0.5 0.3 0.2]\n# [0.3 0.6 0.2]\n# [0.1 0.1 0.8]\n# [0.8 0.2 0. ]]\n\n# For each prompt, which completion is most likely?\npred_class_idxs = pred_probs.argmax(axis=-1)\npreds = [class_names[pred_class_idx] for pred_class_idx in pred_class_idxs]\nprint(preds)\n# ['basketball player',\n# 'tennis player',\n# 'scientist',\n# 'basketball player']\n```\n</details>\n\n\n<details>\n<summary>Run in batches, where each prompt has a different set of possible completions\n</summary>\n\nAgain, let's predict probabilities.\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nfrom cappr.huggingface.classify import predict_proba_examples\nfrom cappr import Example\n\n# Load a model and its tokenizer\nmodel_name = \"gpt2\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name)\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\n# Create a sequence of Example objects representing your classification tasks\nexamples = [\n Example(\n prompt=\"Jodie Foster played\",\n completions=(\"Clarice Starling\", \"Trinity in The Matrix\"),\n ),\n Example(\n prompt=\"Batman, from Batman: The Animated Series, was played by\",\n completions=(\"Pete Holmes\", \"Kevin Conroy\", \"Spongebob!\"),\n prior= ( 1/3 , 2/3 , 0 ),\n ),\n]\n\n# Run CAPPr\npred_probs = predict_proba_examples(\n examples, model_and_tokenizer=(model, tokenizer)\n)\n\n# pred_probs[i][j] = probability that examples[i].prompt is classified as\n# examples[i].completions[j]\nprint([example_pred_probs.round(2) for example_pred_probs in pred_probs])\n# [array([0.7, 0.3]),\n# array([0.03, 0.97, 0. ])]\n\n# For each example, which completion is most likely?\npred_class_idxs = [\n example_pred_probs.argmax() for example_pred_probs in pred_probs\n]\npreds = [\n example.completions[pred_class_idx]\n for example, pred_class_idx in zip(examples, pred_class_idxs)\n]\nprint(preds)\n# ['Clarice Starling',\n# 'Kevin Conroy']\n```\n</details>\n\n\nSee the [`demos`](https://github.com/kddubey/cappr/blob/main/demos/) for demonstrations\nof slightly harder classification tasks.\n\nFor CAPPr, GPTQ models are the most computationally performant. These models are\ncompatible with `cappr.huggingface.classify`. See [this page of the\ndocumentation](https://cappr.readthedocs.io/en/latest/select_a_language_model.html#hugging-face)\nfor more info on using these models.\n\n\n## Documentation\n\nhttps://cappr.readthedocs.io\n\n\n## Installation\n\nSee [this page of the\ndocumentation](https://cappr.readthedocs.io/en/latest/installation.html).\n\n\n## Related work\n\nSee [this page of the\ndocumentation](https://cappr.readthedocs.io/en/latest/related_work.html).\n\n\n## Motivation\n\nReduce engineering complexity.\n\nSee [this page of the\ndocumentation](https://cappr.readthedocs.io/en/latest/motivation.html) for more info.\n\n\n## Performance\n\n[Statistical performance](https://cappr.readthedocs.io/en/latest/statistical_performance.html)\n\n[Computational performance](https://cappr.readthedocs.io/en/latest/computational_performance.html)\n\n\n## How it works\n\nYou input a `prompt` string, a `end_of_prompt` string (a whitespace or empty) and a set\nof candidate `completion` strings such that the string\u2014\n\n```python\n{prompt}{end_of_prompt}{completion}\n```\n\n\u2014is a naturally flowing thought. CAPPr picks the `completion` which is mostly likely to\nfollow `prompt` by computing the\u2014\n\n> **C**ompletion<br>\n **A**fter<br>\n **P**rompt<br>\n **Pr**obability<br>\n\n\u2014as fleshed out in my [question on Cross\nValidated](https://stats.stackexchange.com/q/601159/337906).\n\n\n## Local development\n\nSee [this page of the documentation](https://cappr.readthedocs.io/en/latest/local.html).\n\n\n## Todo\n\nI'm dumping todos here:\n\n[Code changes](https://github.com/users/kddubey/projects/1/views/1)\n\n[Reseach experiments](https://github.com/users/kddubey/projects/2)\n\nFeel free to raise issues ofc\n",
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