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Model: vonjack/Qwen-LLaMAfied-HFTok-7B-Chat
Source: Original Platform

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LICENSE

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+Tongyi Qianwen LICENSE AGREEMENT
+
+Tongyi Qianwen Release Date: August 3, 2023
+
+By clicking to agree or by using or distributing any portion or element of the Tongyi Qianwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
+
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NOTICE

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+------------- LICENSE FOR NVIDIA Megatron-LM code  --------------
+
+Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+  * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+  * Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in the
+    documentation and/or other materials provided with the distribution.
+  * Neither the name of NVIDIA CORPORATION nor the names of its
+    contributors may be used to endorse or promote products derived
+    from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+------------- LICENSE FOR OpenAI tiktoken code  --------------
+
+MIT License
+
+Copyright (c) 2022 OpenAI, Shantanu Jain
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+language:
+- en
+- zh
+tags:
+- qwen
+- llama
+- llama-2
+license: apache-2.0
+---
+[WIP]
+
+Origin repository [JosephusCheung/Qwen-LLaMAfied-7B-Chat](https://huggingface.co/JosephusCheung/Qwen-LLaMAfied-7B-Chat).
+
+
+This is the LLaMAfied version of [Qwen/Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat), 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.
+
+I converted the tokenizer from tiktoken format to huggingface format, so you do not need to allow external codes when loading anymore.
+
+The model has been edited to be white-labelled, meaning the model will no longer call itself a Qwen.
+
+SPOILOR: Further finetuning is in progress, the current version is a work-in-progress, some knowledge may be biased and illusory due to structural changes. Will be updated very, very sooooooooooon.
+
+PROMPT FORMAT: [chatml](https://github.com/openai/openai-python/blob/main/chatml.md)
+
+CURRENT MMLU: 50.36
+
+Issue: Compared to the original Qwen-Chat scoring 53.9, the MMLU score dropped slightly (-3.54) due to insufficient realignment.

config.json

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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
+}

eval/evaluate_chatml_mmlu.py

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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)

generation_config.json

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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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tokenizer_config.json

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