cappr
v0.9.6 - fix Llama 3 tokenizer
從選擇列表中挑選您的LLM。
或計算給定提示的完成的概率，這可能很有用。
從開源LLM中擠出更多。
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有關使用GGGUF模型的更多信息，請參見文檔的此頁面。
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有關使用transformers模型的更多信息，請參見文檔的此頁面。
許多提示從相同的指令開始，例如係統提示以及少數示例輸入輸出對。與其在通用指令上反復運行模型，不如將其緩存，以使未來的計算更快。
這是一個使用cappr.huggingface.classify.cache_model的示例。
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']這是一個使用cappr.huggingface.classify.log_probs_conditional的示例。
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)]]使用cappr.utils.classify.agg_log_probs有效地匯總這些日誌概況。
有關稍微高級的演示，請參見./demos/huggingface/dpo.ipynb 。
逐步和經過思考的提示是使LLM``理性''更複雜的任務的高效方法。但是，如果您需要結構化的輸出，則逐步完成是笨拙的。給定可能的答案列表，使用CAPPR從這些類型的完成中提取最終答案。
在文檔中查看此想法。
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']同樣，讓我們預測概率。
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']有關更難的分類任務的演示，請參見demos 。
對於CAPPR，GPTQ模型是最具計算機的性能。這些模型與cappr.huggingface.classify兼容。有關使用這些模型的更多信息，請參見文檔的此頁面。
https://cappr.readthedocs.io
請參閱文檔的此頁面。
請參閱文檔的此頁面。
降低工程複雜性。
有關更多信息，請參見文檔的此頁面。
統計表現
計算性能
您輸入一個prompt字符串， end_of_prompt字符串（一個空格或空）和一組候選completion字符串，使字符串 -
{ prompt }{ end_of_prompt }{ completion } - 自然流動的思想。 CAPPR選擇completion ，該完成大多可能通過prompt -
c ompletion
後
迅速的
公關的可行性
- 正如我關於Cross驗證的問題的充實。
請參閱文檔的此頁面。
我在這裡傾倒毒品：
代碼更改
研究實驗
隨時提出問題
