Datasets:
danidarko
/
docs

Dataset card Data Studio Files Community
1
text
stringlengths
0
15.3k
continuation_logprobs = sum(tokens_logprobs[idx:-1])
for i in range(idx, len(tokens)):
token = tokens[i]
top_tokens = logprobs['top_logprobs'][i]
top_token = max(top_tokens.keys(), key=lambda x: top_tokens[x])
if top_token != token:
is_greedy = False
break
return (continuation_logprobs, is_greedy)
@register_model('gguf', 'ggml')
class GGUFLM(LM):
def __init__(self, base_url=None, max_length=2048, **kwargs):
super().__init__()
self.base_url = base_url
assert self.base_url, 'must pass `base_url` to use GGUF LM!'
self.logprobs = 10
self.temperature = 0.0
self.max_length = max_length
def gguf_completion(self, context, continuation=None, stop=None, retries=3, delay=5, **kwargs):
for _ in range(retries):
try:
prompt = context
request = {'prompt': prompt, 'logprobs': self.logprobs, 'temperature': self.temperature}
if continuation:
prompt += continuation
request.update({'prompt': prompt, 'max_tokens': 1, 'echo': True})
if stop is not None:
request['stop'] = stop
response = requests.post(f'{self.base_url}/v1/completions', json=request)
response.raise_for_status()
return response.json()
except RequestException as e:
logger.error(f'RequestException: {e}')
time.sleep(delay)
else:
raise Exception(f'Failed to get a valid response after {retries} retries.')
def loglikelihood(self, requests, disable_tqdm: bool=False):
if not requests:
return []
res = []
for (context, continuation) in tqdm([req.args for req in requests], disable=disable_tqdm):
response = self.gguf_completion(context=context, continuation=continuation)
if response and 'choices' in response and response['choices']:
choice = response['choices'][0]
logprobs = choice.get('logprobs')
if logprobs and 'token_logprobs' in logprobs and logprobs['token_logprobs']:
(logprob, is_greedy) = get_result(logprobs, len(context))
res.append((logprob, is_greedy))
else:
logger.warning("Invalid logprobs data. Expected 'logprobs' to contain 'token_logprobs' list.")
else:
logger.error(f'Invalid response for loglikelihood. Response: {response}')
assert False
return res
def generate_until(self, requests, disable_tqdm: bool=False):
if not requests:
return []
res = []
for request in tqdm([req.args for req in requests], disable=disable_tqdm):
inp = request[0]
request_args = request[1]
until = request_args.get('until', ['</s>'])
response = self.gguf_completion(context=inp, stop=until)
if response and 'choices' in response and response['choices']:
choice = response['choices'][0]
if 'text' in choice:
generated_text = choice['text'].strip()
res.append(generated_text)
else:
logger.error(f'Invalid response for greedy_until. Response: {response}')
res.append(None)
else:
logger.error(f'Invalid response for greedy_until. Response: {response}')
res.append(None)
return res
def loglikelihood_rolling(self, requests, disable_tqdm: bool=False):
raise NotImplementedError('loglikelihood_rolling not yet supported for GGUF models')
# File: lm-evaluation-harness-main/lm_eval/models/huggingface.py
import copy
import os
from datetime import timedelta
from pathlib import Path
from typing import Dict, List, Literal, Optional, Tuple, Union
import jinja2
import torch
import torch.nn.functional as F
import transformers
from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs, find_executable_batch_size
from huggingface_hub import HfApi
from packaging import version
from peft import PeftModel
from peft import __version__ as PEFT_VERSION
from tqdm import tqdm