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Model: JosephusCheung/Qwen-LLaMAfied-7B-Chat
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2026-05-19 21:42:55 +08:00
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---
language:
- en
- zh
tags:
- qwen
- llama
- llama-2
license: gpl-3.0
---
NEW VERSIONS: [https://huggingface.co/CausalLM/14B](https://huggingface.co/CausalLM/14B)
This is the LLaMAfied replica of [Qwen/Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat) (Original Version before 25.09.2023), recalibrated to fit the original LLaMA/LLaMA-2-like model structure.
You can use LlamaForCausalLM for model inference, which is the same as LLaMA/LLaMA-2 models (using GPT2Tokenizer converted from the original tiktoken, by [vonjack](https://huggingface.co/vonjack)).
The model has been edited to be white-labelled, meaning the model will no longer call itself a Qwen.
Up until now, the model has undergone numerical alignment of weights and preliminary reinforcement learning in order to align with the original model. Some errors and outdated knowledge have been addressed through model editing methods. This model remains completely equivalent to the original version, without having any dedicated supervised finetuning on downstream tasks or other extensive conversation datasets.
PROMPT FORMAT: [chatml](https://github.com/openai/openai-python/blob/main/chatml.md)
CURRENT MMLU: 53.48
CURRENT CEval (val): 54.13
```
MMLU - stem ACC: 46.40 Humanities ACC: 47.61 other ACC: 61.31 social ACC: 61.78 AVERAGE ACC:53.48
CEval (val) - STEM acc: 45.28 Social Science acc: 66.19 Humanities acc: 58.76 Other acc: 54.62 Hard acc:28.64 AVERAGE acc:54.13
```
Issue: Compared to the original Qwen-7B-Chat scoring 53.90 in MMLU and 54.18 in CEval (val), the our scores dropped slightly [-0.42 in MMLU, -0.05 in CEval (val)] due to insufficient realignment.
这是 [通义千问 Qwen/Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat) (在 25.09.2023 之前的原始版本) 的 LLaMA 化版本，经过重新校准以适应原始的类似 LLaMA/LLaMA-2 的模型结构。
您可以使用 LlamaCausalLM 进行模型推理，和 LLaMA/LLaMA-2 保持一致（使用由 [vonjack](https://huggingface.co/vonjack) 从原始 tiktoken 转换而来的 GPT2Tokenizer 分词器）。
模型已经被编辑实现白标化，不再自称通义千问。
到目前为止，该模型已经进行了权重的数值对齐和初步的强化学习，以与原始模型保持一致。 一些错误和过时的知识已通过模型编辑方法得到解决。 该模型与原始版本完全等效，尚未对下游任务或其他广泛的对话数据集进行任何专门的监督微调。
PROMPT 格式: [chatml](https://github.com/openai/openai-python/blob/main/chatml.md)
当前的 MMLU: 53.48
当前的 CEval (val): 54.13
```
MMLU - stem ACC: 46.40 Humanities ACC: 47.61 other ACC: 61.31 social ACC: 61.78 AVERAGE ACC:53.48
CEval (val) - STEM acc: 45.28 Social Science acc: 66.19 Humanities acc: 58.76 Other acc: 54.62 Hard acc:28.64 AVERAGE acc:54.13
```
问题：相比原本的 Qwen-7B-Chat 的 MMLU 分数 53.90 和 CEval (val) 分数 54.18，由于不够充分的重新对齐，分数都略有下降 [-0.42 in MMLU, -0.05 in CEval (val)]。

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{
"_name_or_path": "/notebooks/qwen",
"architectures": [
"LlamaForCausalLM"
],
"bos_token_id": 151643,
"eos_token_id": 151643,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 11008,
"max_position_embeddings": 6144,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
"pad_token_id": 0,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.32.0.dev0",
"use_cache": true,
"vocab_size": 151936
}

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import os
import pandas as pd
import numpy as np
import argparse
import datasets
import torch
import re
from thefuzz import process
from typing import List
from tqdm import tqdm
from transformers.trainer_utils import set_seed
'''
wget https://people.eecs.berkeley.edu/~hendrycks/data.tar
mkdir data/mmlu
mv data.tar data/mmlu
cd data/mmlu; tar xf data.tar
cd ../../
pip install thefuzz
python eval/evaluate_chat_mmlu.py -d data/mmlu/data/
'''
from typing import Tuple, List, Union, Iterable
import numpy as np
import torch
import torch.nn.functional as F
from transformers import PreTrainedTokenizer
from transformers import logging
from transformers.generation import LogitsProcessor
from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List
HistoryType = List[Tuple[str, str]]
TokensType = List[int]
BatchTokensType = List[List[int]]
def make_context(
tokenizer: PreTrainedTokenizer,
query: str,
history: List[Tuple[str, str]] = None,
system: str = "",
max_window_size: int = 6144,
chat_format: str = "chatml",
):
if history is None:
history = []
im_start, im_end = "<|im_start|>", "<|im_end|>"
im_start_tokens = [tokenizer.im_start_id]
im_end_tokens = [tokenizer.im_end_id]
nl_tokens = tokenizer.encode("\n")
def _tokenize_str(role, content):
return f"{role}\n{content}", tokenizer.encode(
role
) + nl_tokens + tokenizer.encode(content)
system_text, system_tokens_part = _tokenize_str("system", system)
system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
raw_text = ""
context_tokens = []
for turn_query, turn_response in reversed(history):
query_text, query_tokens_part = _tokenize_str("user", turn_query)
query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
response_text, response_tokens_part = _tokenize_str(
"assistant", turn_response
)
response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
prev_chat = (
f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
)
current_context_size = (
len(system_tokens) + len(next_context_tokens) + len(context_tokens)
)
if current_context_size < max_window_size:
context_tokens = next_context_tokens + context_tokens
raw_text = prev_chat + raw_text
else:
break
context_tokens = system_tokens + context_tokens
raw_text = f"{im_start}{system_text}{im_end}" + raw_text
context_tokens += (
nl_tokens
+ im_start_tokens
+ _tokenize_str("user", query)[1]
+ im_end_tokens
+ nl_tokens
+ im_start_tokens
+ tokenizer.encode("assistant")
+ nl_tokens
)
raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n"
return raw_text, context_tokens
def chat(
model,
tokenizer: PreTrainedTokenizer,
query: str,
history: Optional[HistoryType],
system: str = "You are a helpful assistant.",
append_history: bool = True
) -> Tuple[str, HistoryType]:
if history is None:
history = []
raw_text, context_tokens = make_context(
tokenizer,
query,
history=history,
system=system,
max_window_size=6144,
chat_format = "chatml",
)
stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
input_ids = torch.tensor([context_tokens]).cuda()
outputs = model.generate(
input_ids,
stop_words_ids = stop_words_ids,
return_dict_in_generate = False,
)
response = decode_tokens(
outputs[0],
tokenizer,
raw_text_len=len(raw_text),
context_length=len(context_tokens),
chat_format='chatml',
verbose=False,
)
if append_history:
history.append((query, response))
return response, history
def decode_tokens(
tokens: Union[torch.LongTensor, TokensType],
tokenizer: PreTrainedTokenizer,
raw_text_len: int,
context_length: int,
chat_format: str = "chatml",
verbose: bool = False,
return_end_reason: bool = False,
) -> str:
if torch.is_tensor(tokens):
tokens = tokens.cpu().numpy().tolist()
return _decode_chatml(
tokens,
stop_words=[],
eod_token_ids=[tokenizer.im_start_id, tokenizer.im_end_id],
tokenizer=tokenizer,
raw_text_len=raw_text_len,
context_length=context_length,
verbose=verbose,
return_end_reason=return_end_reason,
)
def _decode_chatml(
tokens: List[int],
*,
stop_words: List[str],
eod_token_ids: List[int],
tokenizer: PreTrainedTokenizer,
raw_text_len: int,
context_length: int,
verbose: bool = False,
return_end_reason: bool = False,
chat_format = "chatml",
):
end_reason = f"Gen length {len(tokens)}"
eod_token_idx = context_length
for eod_token_idx in range(context_length, len(tokens)):
if tokens[eod_token_idx] in eod_token_ids:
end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
break
trim_decode_tokens = tokenizer.decode(tokens[:eod_token_idx])[raw_text_len:]
if verbose:
print("\nRaw Generate w/o EOD:", tokenizer.decode(tokens)[raw_text_len:])
print("\nRaw Generate:", trim_decode_tokens)
print("\nEnd Reason:", end_reason)
for stop_word in stop_words:
trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
trim_decode_tokens = trim_decode_tokens.strip()
if verbose:
print("\nGenerate:", trim_decode_tokens)
if return_end_reason:
return trim_decode_tokens, end_reason
else:
return trim_decode_tokens
def load_models_tokenizer(args):
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(args.checkpoint_path, device_map="auto", trust_remote_code=True).eval()
model.generation_config = GenerationConfig.from_pretrained(args.checkpoint_path, trust_remote_code=True)
model.generation_config.do_sample = False # use greedy decoding
return model, tokenizer
def format_example(line):
example = 'The following is a multiple-choice question. Please choose the most suitable one among A, B, C and D as the answer to this question.\n\n' + line['question'] + "\n"
for choice in choices:
example += f'{choice}. {line[f"{choice}"]}\n'
return example
def process_before_extraction(gen, choice_dict):
# replace the choice by letter in the generated sentence
# from longest one to shortest one
for key, val in sorted(choice_dict.items(), key=lambda x: len(x[1]), reverse=True):
pattern = re.compile(re.escape(val.rstrip(".")), re.IGNORECASE)
gen = pattern.sub(key, gen)
return gen
def extract_choice(gen, choice_list):
# answer is A | choice is A | choose A
res = re.search(r"(?:(?:[Cc]hoose)|(?:(?:[Aa]nswer|[Cc]hoice)(?![^ABCD]{0,20}?(?:n't|not))[^ABCD]{0,10}?\b(?:|is|:|be))\b)[^ABCD]{0,20}?\b(A|B|C|D)\b", gen)
# A is correct | A is right
if res is None:
res = re.search(r"\b(A|B|C|D)\b(?![^ABCD]{0,8}?(?:n't|not)[^ABCD]{0,5}?(?:correct|right))[^ABCD]{0,10}?\b(?:correct|right)\b", gen)
# straight answer: A
if res is None:
res = re.search(r"^(A|B|C|D)(?:\.|,|:|$)", gen)
# simply extract the first appearred letter
if res is None:
res = re.search(r"(?<![a-zA-Z])(A|B|C|D)(?![a-zA-Z=])", gen)
if res is None:
return choices[choice_list.index(process.extractOne(gen, choice_list)[0])]
else:
return res.group(1)
def extract_answer(response, row):
gen = process_before_extraction(response, {choice: row[choice] for choice in choices})
pred = extract_choice(gen, [row[choice] for choice in choices])
return pred
@torch.no_grad()
def eval_subject(
model,
tokenizer,
subject_name,
test_df,
save_result_dir=None,
overwrite=False,
**kwargs
):
result_path = os.path.join(save_result_dir, f'{subject_name}_result.csv')
if not overwrite and os.path.exists(result_path):
print(f"{result_path} existed, skip!")
score = []
for (_, datarow), (_, resultrow) in zip(test_df.iterrows(), pd.read_csv(result_path).iterrows()):
# pred = extract_answer(resultrow['model_response'], datarow)
pred = resultrow['model_output']
correct = 1 if pred == datarow['answer'] else 0
score.append(correct)
return score
result = []
score = []
for _, row in tqdm(test_df.iterrows(), total=len(test_df)):
question = format_example(row)
response, history = chat(
model,
tokenizer,
question,
history=None,
)
print(question)
print(response)
pred = extract_answer(response, row)
print(pred)
print("======================")
if 'answer' in row:
correct = 1 if pred == row['answer'] else 0
score.append(correct)
if args.debug: print(f'{question} pred: {pred} ref: {row["answer"]}')
result.append(pred)
if save_result_dir:
test_df['model_output'] = result
test_df['model_response'] = response
if score:
test_df["correctness"] = score
os.makedirs(save_result_dir, exist_ok=True)
test_df.to_csv(os.path.join(
save_result_dir, f'{subject_name}_result.csv'), encoding="utf-8", index=False)
return score
def cal_mmlu(res):
acc_sum_dict = dict()
acc_norm_sum_dict = dict()
cnt_dict = dict()
acc_sum = 0.
cnt = 0
hard_cnt = 0
hard_acc_sum = 0.
for class_ in TASK_NAME_MAPPING.keys():
acc_sum_dict[class_] = 0.
acc_norm_sum_dict[class_] = 0.
cnt_dict[class_] = 0.
for tt in TASK_NAME_MAPPING[class_]:
acc_sum += sum(res[tt])
cnt += len(res[tt])
acc_sum_dict[class_] += sum(res[tt])
cnt_dict[class_] += len(res[tt])
print('\n\n\n')
for k in TASK_NAME_MAPPING.keys():
if k in cnt_dict:
print('%s ACC: %.2f ' % (
k, acc_sum_dict[k] * 100 / cnt_dict[k]))
print('AVERAGE ACC:%.2f ' % (acc_sum *100 / cnt))
def main(args):
print("loading model weights")
if args.checkpoint_path is not None:
model, tokenizer = load_models_tokenizer(args)
else:
model, tokenizer = None, None
print("model loaded")
dev_result = {}
for subject_name in tqdm(SUBJECTS):
# val_file_path = os.path.join(args.eval_data_path, 'val', f'{subject_name}_val.csv')
# dev_file_path = os.path.join(args.eval_data_path, 'dev', f'{subject_name}_dev.csv')
test_file_path = os.path.join(args.eval_data_path, 'test', f'{subject_name}_test.csv')
# val_df = pd.read_csv(val_file_path, names=['question','A','B','C','D','answer'])
# dev_df = pd.read_csv(dev_file_path, names=['question','A','B','C','D','answer'])
test_df = pd.read_csv(test_file_path, names=['question','A','B','C','D','answer'])
score = eval_subject(model, tokenizer, subject_name, test_df, save_result_dir=f"outs_chat/mmlu_eval_result", overwrite=args.overwrite)
dev_result[subject_name] = score
cal_mmlu(dev_result)
TASK_NAME_MAPPING = {'stem': ['abstract_algebra', 'anatomy', 'astronomy', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_physics', 'computer_security', 'conceptual_physics', 'electrical_engineering', 'elementary_mathematics', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_mathematics', 'high_school_physics', 'high_school_statistics', 'machine_learning'],
'Humanities': ['formal_logic', 'high_school_european_history', 'high_school_us_history', 'high_school_world_history', 'international_law', 'jurisprudence', 'logical_fallacies', 'moral_disputes', 'moral_scenarios', 'philosophy', 'prehistory', 'professional_law', 'world_religions'],
'other': ['business_ethics', 'college_medicine', 'human_aging', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'nutrition', 'professional_accounting', 'professional_medicine', 'virology', 'global_facts', 'clinical_knowledge'],
'social': ['econometrics', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_microeconomics', 'high_school_psychology', 'human_sexuality', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy']}
SUBJECTS = [v for vl in TASK_NAME_MAPPING.values() for v in vl]
choices = ["A", "B", "C", "D"]
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Test HF checkpoint.')
parser.add_argument('-c', '--checkpoint-path', type=str, help='Checkpoint path', default="Qwen/Qwen-7B-Chat")
parser.add_argument('-s', '--seed', type=int, default=1234, help='Random seed')
"""Provide extra arguments required for tasks."""
group = parser.add_argument_group(title='Evaluation options')
group.add_argument('-d', '--eval_data_path', type=str,
help='Path to eval data')
group.add_argument("--debug", action='store_true', default=False,
help='Print infos.')
group.add_argument("--overwrite", action='store_true', default=False,
help='Overwrite existed results')
args = parser.parse_args()
set_seed(args.seed)
main(args)

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{
"chat_format": "chatml",
"decay_bound": 0.0,
"decay_factor": 1.0,
"do_sample": true,
"eos_token_id": 151643,
"factual_nucleus_sampling": false,
"max_context_size": 1024,
"max_generate_size": 512,
"max_new_tokens": 512,
"pad_token_id": 151643,
"stop_words_ids": [
[
151643
]
],
"top_k": 0,
"top_p": 0.8,
"transformers_version": "4.32.0.dev0"
}

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214
special_tokens_map.json Normal file
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9
tokenizer_config.json Normal file
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151853
vocab.json Normal file
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