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Model: shareAI/llama3.1-8b-instruct-dpo-zh Source: Original Platform
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README.md
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README.md
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---
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frameworks:
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- Pytorch
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license: Apache License 2.0
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tasks:
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- chatbot
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language:
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- cn
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tags:
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- RL-tuned
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tools:
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- vllm
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---
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# llama3.1-8b-instruct 中文DPO版
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### 模型介绍
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Github：[llama3中文仓库](https://github.com/CrazyBoyM/llama3-Chinese-chat)
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像原版instruct一样，喜欢用有趣中文和表情符号回答问题。
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特点：偏好中文和emoji表情，且不损伤原instruct版模型能力。实测中文DPO版问答性能体验超过现在市面上任何llama3.1中文微调版 （微调会大面积破坏llama3.1原版能力，导致遗忘）
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### 训练细节
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DPO(beta 0.5) + lora rank128, alpha256 + 打开"lm_head", "input_layernorm", "post_attention_layernorm", "norm"层训练.
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### 模型部署
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网页脚本文件：https://github.com/CrazyBoyM/llama3-Chinese-chat/blob/main/deploy/web_streamlit_for_instruct_v2.py
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已经在模型文件夹里内置了一份web.py，可以直接使用。
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```
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pip install streamlit
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pip install transformers==4.40.1
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streamlit run web.py ./llama3.1-8b-instruct-dpo-zh
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```
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### 模型下载
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('shareAI/llama3.1-8b-instruct-dpo-zh')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/shareAI/llama3.1-8b-instruct-dpo-zh.git
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```
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config.json
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"architectures": [
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"LlamaForCausalLM"
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],
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"max_position_embeddings": 32768,
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"model_type": "llama",
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"num_attention_heads": 32,
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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
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||||
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||||
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||||
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|
||||
410504
tokenizer.json
Normal file
410504
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
308
web.py
Normal file
308
web.py
Normal file
@@ -0,0 +1,308 @@
|
||||
# copied from https://github.com/SmartFlowAI/Llama3-XTuner-CN/blob/main/web_demo.py
|
||||
# isort: skip_file
|
||||
import copy
|
||||
import warnings
|
||||
from dataclasses import asdict, dataclass
|
||||
from typing import Callable, List, Optional
|
||||
|
||||
import streamlit as st
|
||||
import torch
|
||||
from torch import nn
|
||||
from transformers.generation.utils import (LogitsProcessorList,
|
||||
StoppingCriteriaList)
|
||||
from transformers.utils import logging
|
||||
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig # isort: skip
|
||||
from peft import PeftModel
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
st.set_page_config(page_title="Llama3-Chinese")
|
||||
|
||||
import argparse
|
||||
|
||||
@dataclass
|
||||
class GenerationConfig:
|
||||
# this config is used for chat to provide more diversity
|
||||
max_length: int = 8192
|
||||
max_new_tokens: int = 600
|
||||
top_p: float = 0.8
|
||||
temperature: float = 0.8
|
||||
do_sample: bool = True
|
||||
repetition_penalty: float = 1.05
|
||||
|
||||
@torch.inference_mode()
|
||||
def generate_interactive(
|
||||
model,
|
||||
tokenizer,
|
||||
prompt,
|
||||
generation_config: Optional[GenerationConfig] = None,
|
||||
logits_processor: Optional[LogitsProcessorList] = None,
|
||||
stopping_criteria: Optional[StoppingCriteriaList] = None,
|
||||
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor],
|
||||
List[int]]] = None,
|
||||
additional_eos_token_id: Optional[int] = None,
|
||||
**kwargs,
|
||||
):
|
||||
inputs = tokenizer([prompt], return_tensors='pt')
|
||||
input_length = len(inputs['input_ids'][0])
|
||||
for k, v in inputs.items():
|
||||
inputs[k] = v.cuda()
|
||||
input_ids = inputs['input_ids']
|
||||
_, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
|
||||
if generation_config is None:
|
||||
generation_config = model.generation_config
|
||||
generation_config = copy.deepcopy(generation_config)
|
||||
model_kwargs = generation_config.update(**kwargs)
|
||||
bos_token_id, eos_token_id = ( # noqa: F841 # pylint: disable=W0612
|
||||
generation_config.bos_token_id,
|
||||
generation_config.eos_token_id,
|
||||
)
|
||||
if isinstance(eos_token_id, int):
|
||||
eos_token_id = [eos_token_id]
|
||||
if additional_eos_token_id is not None:
|
||||
eos_token_id.append(additional_eos_token_id)
|
||||
has_default_max_length = kwargs.get(
|
||||
'max_length') is None and generation_config.max_length is not None
|
||||
if has_default_max_length and generation_config.max_new_tokens is None:
|
||||
warnings.warn(
|
||||
f"Using 'max_length''s default ({repr(generation_config.max_length)}) \
|
||||
to control the generation length. "
|
||||
'This behaviour is deprecated and will be removed from the \
|
||||
config in v5 of Transformers -- we'
|
||||
' recommend using `max_new_tokens` to control the maximum \
|
||||
length of the generation.',
|
||||
UserWarning,
|
||||
)
|
||||
elif generation_config.max_new_tokens is not None:
|
||||
generation_config.max_length = generation_config.max_new_tokens + \
|
||||
input_ids_seq_length
|
||||
if not has_default_max_length:
|
||||
logger.warn( # pylint: disable=W4902
|
||||
f"Both 'max_new_tokens' (={generation_config.max_new_tokens}) "
|
||||
f"and 'max_length'(={generation_config.max_length}) seem to "
|
||||
"have been set. 'max_new_tokens' will take precedence. "
|
||||
'Please refer to the documentation for more information. '
|
||||
'(https://huggingface.co/docs/transformers/main/'
|
||||
'en/main_classes/text_generation)',
|
||||
UserWarning,
|
||||
)
|
||||
|
||||
if input_ids_seq_length >= generation_config.max_length:
|
||||
input_ids_string = 'input_ids'
|
||||
logger.warning(
|
||||
f"Input length of {input_ids_string} is {input_ids_seq_length}, "
|
||||
f"but 'max_length' is set to {generation_config.max_length}. "
|
||||
'This can lead to unexpected behavior. You should consider'
|
||||
" increasing 'max_new_tokens'.")
|
||||
|
||||
# 2. Set generation parameters if not already defined
|
||||
logits_processor = logits_processor if logits_processor is not None \
|
||||
else LogitsProcessorList()
|
||||
stopping_criteria = stopping_criteria if stopping_criteria is not None \
|
||||
else StoppingCriteriaList()
|
||||
|
||||
logits_processor = model._get_logits_processor(
|
||||
generation_config=generation_config,
|
||||
input_ids_seq_length=input_ids_seq_length,
|
||||
encoder_input_ids=input_ids,
|
||||
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
|
||||
logits_processor=logits_processor,
|
||||
)
|
||||
|
||||
stopping_criteria = model._get_stopping_criteria(
|
||||
generation_config=generation_config,
|
||||
stopping_criteria=stopping_criteria)
|
||||
logits_warper = model._get_logits_warper(generation_config)
|
||||
|
||||
unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
|
||||
scores = None
|
||||
while True:
|
||||
model_inputs = model.prepare_inputs_for_generation(
|
||||
input_ids, **model_kwargs)
|
||||
# forward pass to get next token
|
||||
outputs = model(
|
||||
**model_inputs,
|
||||
return_dict=True,
|
||||
output_attentions=False,
|
||||
output_hidden_states=False,
|
||||
)
|
||||
|
||||
next_token_logits = outputs.logits[:, -1, :]
|
||||
|
||||
# pre-process distribution
|
||||
next_token_scores = logits_processor(input_ids, next_token_logits)
|
||||
next_token_scores = logits_warper(input_ids, next_token_scores)
|
||||
|
||||
# sample
|
||||
probs = nn.functional.softmax(next_token_scores, dim=-1)
|
||||
if generation_config.do_sample:
|
||||
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
|
||||
else:
|
||||
next_tokens = torch.argmax(probs, dim=-1)
|
||||
|
||||
# update generated ids, model inputs, and length for next step
|
||||
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
|
||||
model_kwargs = model._update_model_kwargs_for_generation(
|
||||
outputs, model_kwargs, is_encoder_decoder=False)
|
||||
unfinished_sequences = unfinished_sequences.mul(
|
||||
(min(next_tokens != i for i in eos_token_id)).long())
|
||||
|
||||
output_token_ids = input_ids[0].cpu().tolist()
|
||||
output_token_ids = output_token_ids[input_length:]
|
||||
for each_eos_token_id in eos_token_id:
|
||||
if output_token_ids[-1] == each_eos_token_id:
|
||||
output_token_ids = output_token_ids[:-1]
|
||||
response = tokenizer.decode(output_token_ids, skip_special_tokens=True)
|
||||
|
||||
yield response
|
||||
# stop when each sentence is finished
|
||||
# or if we exceed the maximum length
|
||||
if unfinished_sequences.max() == 0 or stopping_criteria(
|
||||
input_ids, scores):
|
||||
break
|
||||
|
||||
|
||||
def on_btn_click():
|
||||
del st.session_state.messages
|
||||
|
||||
|
||||
@st.cache_resource
|
||||
def load_model(model_name_or_path, adapter_name_or_path=None, load_in_4bit=False):
|
||||
if load_in_4bit:
|
||||
quantization_config = BitsAndBytesConfig(
|
||||
load_in_4bit=True,
|
||||
bnb_4bit_compute_dtype=torch.float16,
|
||||
bnb_4bit_use_double_quant=True,
|
||||
bnb_4bit_quant_type="nf4",
|
||||
llm_int8_threshold=6.0,
|
||||
llm_int8_has_fp16_weight=False,
|
||||
)
|
||||
else:
|
||||
quantization_config = None
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_name_or_path,
|
||||
load_in_4bit=load_in_4bit,
|
||||
trust_remote_code=True,
|
||||
low_cpu_mem_usage=True,
|
||||
torch_dtype=torch.float16,
|
||||
device_map='auto',
|
||||
quantization_config=quantization_config
|
||||
)
|
||||
if adapter_name_or_path is not None:
|
||||
model = PeftModel.from_pretrained(model, adapter_name_or_path)
|
||||
model.eval()
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
|
||||
|
||||
return model, tokenizer
|
||||
|
||||
|
||||
def prepare_generation_config():
|
||||
with st.sidebar:
|
||||
st.title('超参数面板')
|
||||
# 大输入框
|
||||
system_prompt_content = st.text_area('系统提示词',
|
||||
"你是一个调皮活泼的中文智者，名字叫shareAI-Llama3，喜欢用有趣的语言和适当的表情回答问题。注意在编码任务中仍使用英文",
|
||||
height=200,
|
||||
key='system_prompt_content'
|
||||
)
|
||||
max_new_tokens = st.slider('最大回复长度', 100, 8192, 1020, step=8)
|
||||
top_p = st.slider('Top P', 0.0, 1.0, 0.8, step=0.01)
|
||||
temperature = st.slider('温度系数', 0.0, 1.0, 0.6, step=0.01)
|
||||
repetition_penalty = st.slider("重复惩罚系数", 1.0, 2.0, 1.07, step=0.01)
|
||||
st.button('重置聊天', on_click=on_btn_click)
|
||||
|
||||
generation_config = GenerationConfig(max_new_tokens=max_new_tokens,
|
||||
top_p=top_p,
|
||||
temperature=temperature,
|
||||
repetition_penalty=repetition_penalty,
|
||||
)
|
||||
|
||||
return generation_config
|
||||
|
||||
system_prompt = '<|begin_of_text|><<SYS>>\n{content}\n<</SYS>>\n\n'
|
||||
user_prompt = '<|start_header_id|>user<|end_header_id|>\n\n{user}<|eot_id|>'
|
||||
robot_prompt = '<|start_header_id|>assistant<|end_header_id|>\n\n{robot}<|eot_id|>'
|
||||
cur_query_prompt = '<|start_header_id|>user<|end_header_id|>\n\n{user}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'
|
||||
|
||||
|
||||
def combine_history(prompt):
|
||||
messages = st.session_state.messages
|
||||
total_prompt = ''
|
||||
for message in messages:
|
||||
cur_content = message['content']
|
||||
if message['role'] == 'user':
|
||||
cur_prompt = user_prompt.format(user=cur_content)
|
||||
elif message['role'] == 'robot':
|
||||
cur_prompt = robot_prompt.format(robot=cur_content)
|
||||
else:
|
||||
raise RuntimeError
|
||||
total_prompt += cur_prompt
|
||||
|
||||
system_prompt_content = st.session_state.system_prompt_content
|
||||
system = system_prompt.format(content=system_prompt_content)
|
||||
total_prompt = system + total_prompt + cur_query_prompt.format(user=prompt)
|
||||
|
||||
return total_prompt
|
||||
|
||||
|
||||
def main(model_name_or_path, adapter_name_or_path):
|
||||
# torch.cuda.empty_cache()
|
||||
print('load model...')
|
||||
model, tokenizer = load_model(model_name_or_path, adapter_name_or_path=adapter_name_or_path, load_in_4bit=False)
|
||||
print('load model end.')
|
||||
|
||||
st.title('Llama3-Chinese')
|
||||
|
||||
generation_config = prepare_generation_config()
|
||||
|
||||
# Initialize chat history
|
||||
if 'messages' not in st.session_state:
|
||||
st.session_state.messages = []
|
||||
|
||||
# Display chat messages from history on app rerun
|
||||
for message in st.session_state.messages:
|
||||
with st.chat_message(message['role']):
|
||||
st.markdown(message['content'])
|
||||
|
||||
# Accept user input
|
||||
if prompt := st.chat_input('解释一下Vue的原理'):
|
||||
# Display user message in chat message container
|
||||
with st.chat_message('user'):
|
||||
st.markdown(prompt)
|
||||
real_prompt = combine_history(prompt)
|
||||
# Add user message to chat history
|
||||
st.session_state.messages.append({
|
||||
'role': 'user',
|
||||
'content': prompt,
|
||||
})
|
||||
|
||||
with st.chat_message('robot'):
|
||||
message_placeholder = st.empty()
|
||||
for cur_response in generate_interactive(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
prompt=real_prompt,
|
||||
additional_eos_token_id=128009,
|
||||
**asdict(generation_config),
|
||||
):
|
||||
# Display robot response in chat message container
|
||||
message_placeholder.markdown(cur_response + '▌')
|
||||
message_placeholder.markdown(cur_response)
|
||||
# Add robot response to chat history
|
||||
st.session_state.messages.append({
|
||||
'role': 'robot',
|
||||
'content': cur_response, # pylint: disable=undefined-loop-variable
|
||||
})
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
import sys
|
||||
model_name_or_path = sys.argv[1]
|
||||
if len(sys.argv) >= 3:
|
||||
adapter_name_or_path = sys.argv[2]
|
||||
else:
|
||||
adapter_name_or_path = None
|
||||
main(model_name_or_path, adapter_name_or_path)
|
||||
Reference in New Issue
Block a user