Repository: LinkSoul-AI/LLaSM
Branch: main
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Directory structure:
gitextract_2906ffjf/
├── .gitignore
├── LICENSE
├── README.md
├── examples/
│ ├── 0.txt
│ ├── 1.txt
│ └── 2.txt
├── infer.py
├── infer_tokenize.py
├── llasm.py
├── logger.py
└── pyproject.toml
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FILE: README.md
================================================
# LLaSM: Large Language and Speech Model
[](https://huggingface.co/spaces/LinkSoul/LLaSM) [](https://huggingface.co/spaces/LinkSoul/LLaSM) [](https://github.com/LinkSoul-AI/LLaSM/blob/main/LICENSE) [](https://arxiv.org/abs/2308.15930) [](https://huggingface.co/spaces/LinkSoul/LLaSM) [](https://huggingface.co/datasets/LinkSoul/LLaSM-Audio-Instructions)
开源，可商用的**中英文双语语音-语言助手 LLaSM 以及中英文语音 SFT 数据集 LLaSM-Audio-Instructions**，第一个支持中英文语音-文本多模态对话的开源可商用对话模型。
## 模型框架
## 基础演示
## 在线试玩
> Talk is cheap, Show you the Demo.
- [Demo 地址 / Hugging Face Spaces](https://huggingface.co/spaces/LinkSoul/LLaSM)
## 论文
- arXiv 链接：https://arxiv.org/abs/2308.15930
## 资源下载
- Hugging Face模型下载：
- [LLaSM-Chinese-Llama-2-7B](https://huggingface.co/LinkSoul/LLaSM-Cllama2)
- [LLaSM-Baichuan-7B](https://huggingface.co/LinkSoul/LLaSM-Baichuan)
- 百度网盘下载:
- [LLaSM-Chinese-Llama-2-7B](https://pan.baidu.com/s/1PaipNDfqV7f3W1-tl5rwzA?pwd=2549)
- [LLaSM-Baichuan-7B](https://pan.baidu.com/s/1QZrXA8IJXclN77T4jM7tEw?pwd=y2p7)
- 语言模型:
- [Chinese-Llama-2-7b](https://github.com/LinkSoul-AI/Chinese-Llama-2-7b)
- [Baichuan-7B](https://huggingface.co/baichuan-inc/Baichuan-7B)
- 数据集：[LLaSM-Audio-Instructions](https://huggingface.co/datasets/LinkSoul/LLaSM-Audio-Instructions)
## 环境安装
```shell
# clone the repository
git clone https://github.com/LinkSoul-AI/LLaSM
cd LLaSM
# install package
conda create -n llasm python=3.10 -y
conda activate llasm
pip install --upgrade pip
pip install -e .
```
## 快速测试
- 下载 Whisper large v2 模型：https://huggingface.co/openai/whisper-large-v2
```shell
export LLASM_DEVICE="cuda:0"
python infer.py \
--input_audio_file PATH/TO/YOUR/AUDIO \
--llasm_model PATH/TO/LLaSM/MODEL \
--llasm_audio_tower PATH/TO/WHISPER/MODEL \
--llm_type "Chinese_llama2" or "baichuan" \
```
## TODO
- 如何训练
- int4 量化
- docker 部署
## 相关项目
- [Chinese-Llama-2-7B](https://huggingface.co/LinkSoul/Chinese-Llama-2-7b)
- [Whisper](https://ai.meta.com/llama/)
- [baichuan-inc/Baichuan-7B](https://huggingface.co/baichuan-inc/Baichuan-7B)
## 项目协议
[Apache-2.0 license](https://github.com/LinkSoul-AI/LLaSM/blob/main/LICENSE)
## Citation
如果您发现我们的工作和此仓库有用，欢迎给一个星星 :star: 鼓励我们一下 :beer::
```bibtex
@misc{shu2023llasm,
title={LLaSM: Large Language and Speech Model},
author={Yu Shu and Siwei Dong and Guangyao Chen and Wenhao Huang and Ruihua Zhang and Daochen Shi and Qiqi Xiang and Yemin Shi},
year={2023},
eprint={2308.15930},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## 微信交流群
================================================
FILE: examples/0.txt
================================================
请介绍一下勾股定理
================================================
FILE: examples/1.txt
================================================
世界上人口最多的是哪个国家
================================================
FILE: examples/2.txt
================================================
请介绍一下北京
================================================
FILE: infer.py
================================================
import os
import librosa
import argparse
import torch
from transformers import AutoTokenizer
from transformers import (
WhisperProcessor,
WhisperModel,
)
from llasm import LlaaaLlamaForCausalLM
from infer_tokenize import tokenize
from logger import print_signature
DEFAULT_AUDIO_PATCH_TOKEN = ""
DEFAULT_AUDIO_START_TOKEN = ""
DEFAULT_AUDIO_END_TOKEN = ""
class Setting:
def __init__(self):
self.device = os.environ.get("LLASM_DEVICE", "cuda")
self.llasm_context_len = 2048
self.sampling_rate = 16000
self.audio_token_len = 64
self.stop = ""
CONFIG = Setting()
def main(args):
input_audio_file = args.input_audio_file
temperature = args.temperature
max_new_tokens = args.max_new_tokens
# step0: load tokenizer
tokenizer = AutoTokenizer.from_pretrained(args.llasm_model)
# step0-1: add special token //
tokenizer.add_tokens([DEFAULT_AUDIO_PATCH_TOKEN], special_tokens=True)
tokenizer.add_tokens([DEFAULT_AUDIO_START_TOKEN, DEFAULT_AUDIO_END_TOKEN], special_tokens=True)
# step1: load model
model = LlaaaLlamaForCausalLM.from_pretrained(
args.llasm_model,
torch_dtype=torch.float16,
low_cpu_mem_usage=True).to(CONFIG.device)
# step2: load audio processor
audio_processor = WhisperProcessor.from_pretrained(args.llasm_audio_tower, torch_dtype=torch.float16)
# step3: load audio tower
audio_tower = WhisperModel.from_pretrained(
args.llasm_audio_tower,
torch_dtype=torch.float16,
low_cpu_mem_usage=True).to(CONFIG.device)
# step3-1: update audio_tower config for setting special tokens
audio_config = audio_tower.config
audio_config.audio_patch_token = tokenizer.convert_tokens_to_ids([DEFAULT_AUDIO_PATCH_TOKEN])[0]
audio_config.audio_start_token, audio_config.audio_end_token = tokenizer.convert_tokens_to_ids([DEFAULT_AUDIO_START_TOKEN, DEFAULT_AUDIO_END_TOKEN])
model.get_model().audio_tower[0] = audio_tower
# step4 preprocessing input audio
audio, _ = librosa.load(input_audio_file, sr=CONFIG.sampling_rate)
audio_feat = audio_processor(audio, sampling_rate=CONFIG.sampling_rate, return_tensors="pt").input_features
audio_feat = audio_feat.unsqueeze(0).unsqueeze(0).to(CONFIG.device, dtype=torch.float16)
# step5: tokenize
qs = DEFAULT_AUDIO_START_TOKEN + DEFAULT_AUDIO_PATCH_TOKEN * CONFIG.audio_token_len + DEFAULT_AUDIO_END_TOKEN
input_qs = {
"conversations": [{
"from": "human",
"value": qs,
},{
"from": "gpt",
"value": ""
}]
}
input_ids = torch.tensor([tokenize(input_qs, tokenizer, args.llm_type)]).to(CONFIG.device)
# step6: infer run
stop_str = CONFIG.stop
output_ids = model.generate(input_ids,audios=audio_feat,do_sample=True,temperature=temperature,max_new_tokens=max_new_tokens)
input_token_len = input_ids.shape[1]
n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
if n_diff_input_output > 0:
print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
outputs = outputs.strip()
if outputs.endswith(stop_str):
outputs = outputs[:-len(stop_str)]
outputs = outputs.strip()
label = []
with open(input_audio_file[:-len('mp3')] + 'txt', 'r') as fh:
for ln in fh:
label.append(ln.strip())
text = ''.join(label)
print_signature()
print (f"Human: {input_audio_file} ({text})")
print (f"LLaSM: {outputs}")
print ("="*80)
print ("Go to the Demo page, and have a try!")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--input_audio_file', type=str, default='./examples/0.mp3')
parser.add_argument('--llasm_model', type=str, default='path/to/llasm_model')
parser.add_argument('--llasm_audio_tower', type=str, default='path/to/whisper_large_v2')
parser.add_argument('--llm_type', type=str, default='Chinese_llama2')
parser.add_argument('--temperature', type=float, default=0.2)
parser.add_argument('--max_new_tokens', type=int, default=1024)
args = parser.parse_args()
main(args)
================================================
FILE: infer_tokenize.py
================================================
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<>\n", "\n<>\n\n"
simple_audio_conv_multimodal = {
"system": "You are a helpful language and speech assistant. You are able to understand the speech content that the user provides, and assist the user with a variety of tasks using natural language.",
"roles": {"human": "USER", "gpt": "ASSISTANT"},
}
def tokenize_baichuan(item, tokenizer):
roles = simple_audio_conv_multimodal["roles"]
input_ids = []
if "instruction" in item and len(item["instruction"]) > 0:
system = item["instruction"]
else:
system = simple_audio_conv_multimodal["system"]
system_ids = tokenizer.encode(system, add_special_tokens=False)
input_ids += system_ids
for i, turn in enumerate(item["conversations"]):
role = roles.get(turn['from'], 'USER')
content = turn['value']
content = content.strip()
if role == 'ASSISTANT' and content != '':
content += ''
role_ids = tokenizer.encode(role + ":", add_special_tokens=False)
content_ids = tokenizer.encode(content, add_special_tokens=False, truncation=True,
max_length=tokenizer.model_max_length)
input_ids += role_ids + content_ids
if tokenizer.add_bos_token:
input_ids = [tokenizer.bos_token_id] + input_ids
input_ids = input_ids[-tokenizer.model_max_length:]
return input_ids
def tokenize_Cllama2(item, tokenizer):
input_ids = []
if "instruction" in item and len(item["instruction"]) > 0:
system = item["instruction"]
else:
system = simple_audio_conv_multimodal["system"]
system = B_SYS + system + E_SYS
system_ids = tokenizer.encode(system, add_special_tokens=False)
input_ids += system_ids
item["conversations"][0]['value'] = system + item["conversations"][0]['value']
for i, turn in enumerate(item["conversations"]):
role = turn['from']
content = turn['value']
content = content.strip()
if role == 'human':
content = f"{B_INST} {content} {E_INST} "
content_ids = tokenizer.encode(content)
else:
# assert role == "gpt"
if content == "":
content_ids = []
else:
content = f"{content} "
content_ids = tokenizer.encode(content, add_special_tokens=False) + [tokenizer.eos_token_id] # add_special_tokens=False remove bos token, and add eos at the end
input_ids += content_ids
input_ids = input_ids[-tokenizer.model_max_length:]
return input_ids
def tokenize(item, tokenizer, llm_type):
if llm_type == "Chinese_llama2":
return tokenize_Cllama2(item, tokenizer)
elif llm_type == "baichuan":
return tokenize_baichuan(item, tokenizer)
else:
raise ValueError (f"Invalid llm type {llm_type}, please choose in ['Chinese_llama2', 'baichuan']")
================================================
FILE: llasm.py
================================================
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
from transformers import AutoConfig, AutoModelForCausalLM, \
LlamaConfig, LlamaModel, LlamaForCausalLM
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers import (
WhisperProcessor,
WhisperModel,
)
DEFAULT_AUDIO_PATCH_TOKEN = ""
DEFAULT_AUDIO_START_TOKEN = ""
DEFAULT_AUDIO_END_TOKEN = ""
class LlaaaConfig(LlamaConfig):
model_type = "llaaa"
def load_whisper(audio_tower_name):
model = WhisperModel.from_pretrained(audio_tower_name)
model.config.forced_decoder_ids = None
return model
class LlaaaLlamaModel(LlamaModel):
config_class = LlaaaConfig
def __init__(self, config: LlamaConfig):
super(LlaaaLlamaModel, self).__init__(config)
if hasattr(config, "mm_audio_tower"):
# HACK: for FSDP
self.audio_tower = [load_whisper(config.mm_audio_tower)]
if hasattr(config, "use_mm_proj"):
self.mm_projector = nn.Linear(config.mm_hidden_size, config.hidden_size)
def initialize_audio_modules(self, audio_tower, audio_token_len, pretrain_mm_mlp_adapter=None):
self.config.mm_audio_tower = audio_tower
processor = WhisperProcessor.from_pretrained(audio_tower)
if not hasattr(self, 'audio_tower'):
audio_tower = load_whisper(audio_tower)
else:
audio_tower = self.audio_tower[0]
audio_tower.requires_grad_(False)
audio_tower = audio_tower.to(torch.float16)
self.audio_tower = [audio_tower]
self.config.use_mm_proj = True
self.config.mm_hidden_size = 1280
self.config.audio_token_len = audio_token_len
if not hasattr(self, 'mm_projector'):
self.mm_projector = nn.Linear(self.config.mm_hidden_size, self.config.hidden_size)
if pretrain_mm_mlp_adapter is not None:
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
self.mm_projector.load_state_dict({k.split('.')[-1]: v for k, v in mm_projector_weights.items()})
return dict(
processor=processor,
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
audios: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
# HACK: replace back original embeddings for LLaAA pretraining
orig_embeds_params = getattr(self, 'orig_embeds_params', None)
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
audio_tower = getattr(self, 'audio_tower', None)
if audio_tower is not None and (input_ids.shape[1] != 1 or self.training) and audios is not None:
audio_tower = audio_tower[0] # HACK: for FSDP
with torch.no_grad():
bs_audio_features = []
for audios_list in audios:
if len(audios_list) == 0:
dummy_audio_feature = torch.zeros(self.config.audio_token_len, self.config.mm_hidden_size, device=inputs_embeds.device, dtype=inputs_embeds.dtype)
audio_features = [dummy_audio_feature]
else:
audio_features = []
for audio in audios_list:
decoder_input_ids = torch.ones((1, self.config.audio_token_len)) * audio_tower.config.decoder_start_token_id
decoder_input_ids = decoder_input_ids.to(audio.device).to(torch.long)
audio_feature = audio_tower(audio, decoder_input_ids=decoder_input_ids).last_hidden_state
audio_features.append(audio_feature)
bs_audio_features.append(audio_features)
audio_config = audio_tower.config
new_input_embeds = []
for cur_input_ids, cur_input_embeds, cur_audio_features in zip(input_ids, inputs_embeds, bs_audio_features):
if (cur_input_ids == audio_config.audio_patch_token).sum() == 0:
# multimodal LLM, but the current sample is not multimodal, for using both language and audio data
dummy_audio_features = self.mm_projector(cur_audio_features[0])
cur_input_embeds = cur_input_embeds + (0. * dummy_audio_features).sum()
new_input_embeds.append(cur_input_embeds)
continue
if (cur_input_ids == audio_config.audio_start_token).sum() != (cur_input_ids == audio_config.audio_end_token).sum():
raise ValueError("The number of audio start tokens and audio end tokens should be the same.")
audio_start_tokens = torch.where(cur_input_ids == audio_config.audio_start_token)[0]
if len(audio_start_tokens) != len(cur_audio_features):
raise ValueError(f"The number of audio start tokens ({len(audio_start_tokens)}) and audio features ({len(cur_audio_features)}) should be the same.")
for audio_start_token_pos, cur_audio_feature in zip(audio_start_tokens, cur_audio_features):
cur_audio_feature = self.mm_projector(cur_audio_feature)[0]
cur_audio_feature = cur_audio_feature.to(device=cur_input_embeds.device)
num_patches = cur_audio_feature.shape[0]
if cur_input_ids[audio_start_token_pos + num_patches + 1] != audio_config.audio_end_token:
raise ValueError("The audio end token should follow the audio start token.")
if orig_embeds_params is not None:
cur_new_input_embeds = torch.cat(
(cur_input_embeds[:audio_start_token_pos].detach(),
cur_input_embeds[audio_start_token_pos:audio_start_token_pos+1],
cur_audio_feature,
cur_input_embeds[audio_start_token_pos + num_patches + 1:audio_start_token_pos + num_patches + 2],
cur_input_embeds[audio_start_token_pos + num_patches + 2:].detach()), dim=0)
else:
cur_new_input_embeds = torch.cat((
cur_input_embeds[:audio_start_token_pos+1],
cur_audio_feature,
cur_input_embeds[audio_start_token_pos + num_patches + 1:]), dim=0)
new_input_embeds.append(cur_new_input_embeds)
inputs_embeds = torch.stack(new_input_embeds, dim=0)
return super(LlaaaLlamaModel, self).forward(
input_ids=None, attention_mask=attention_mask, past_key_values=past_key_values,
inputs_embeds=inputs_embeds, use_cache=use_cache,
output_attentions=output_attentions, output_hidden_states=output_hidden_states,
return_dict=return_dict
)
class LlaaaLlamaForCausalLM(LlamaForCausalLM):
config_class = LlaaaConfig
def __init__(self, config):
super(LlamaForCausalLM, self).__init__(config)
self.model = LlaaaLlamaModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_model(self):
return self.model
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
audios: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
audios=audios
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model/pipeline parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
):
if past_key_values:
input_ids = input_ids[:, -1:]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
"audios": kwargs.get("audios", None),
}
)
return model_inputs
def initialize_audio_tokenizer(self, tokenizer, device,
tune_mm_mlp_adapter=False, pretrain_mm_mlp_adapter=None):
num_new_tokens = tokenizer.add_tokens([DEFAULT_AUDIO_PATCH_TOKEN], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
num_new_tokens += tokenizer.add_tokens([DEFAULT_AUDIO_START_TOKEN, DEFAULT_AUDIO_END_TOKEN], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
if num_new_tokens > 0:
input_embeddings = self.get_input_embeddings().weight.data
output_embeddings = self.get_output_embeddings().weight.data
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
input_embeddings[-num_new_tokens:] = input_embeddings_avg
output_embeddings[-num_new_tokens:] = output_embeddings_avg
if tune_mm_mlp_adapter:
self.get_model().orig_embeds_params = [self.get_input_embeddings().weight.data.clone().to(device=device)]
for p in self.get_input_embeddings().parameters():
p.requires_grad = True
for p in self.get_output_embeddings().parameters():
p.requires_grad = False
if pretrain_mm_mlp_adapter and num_new_tokens > 0:
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
assert num_new_tokens == 3
if input_embeddings.shape == embed_tokens_weight.shape:
input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
elif embed_tokens_weight.shape[0] == num_new_tokens:
input_embeddings[-num_new_tokens:] = embed_tokens_weight
else:
raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
audio_patch_token = tokenizer.convert_tokens_to_ids([DEFAULT_AUDIO_PATCH_TOKEN])[0]
audio_start_token, audio_end_token = tokenizer.convert_tokens_to_ids([DEFAULT_AUDIO_START_TOKEN, DEFAULT_AUDIO_END_TOKEN])
self.model.audio_tower[0].config.audio_patch_token = audio_patch_token
self.model.audio_tower[0].config.audio_start_token = audio_start_token
self.model.audio_tower[0].config.audio_end_token = audio_end_token
AutoConfig.register("llaaa", LlaaaConfig)
AutoModelForCausalLM.register(LlaaaConfig, LlaaaLlamaForCausalLM)
================================================
FILE: logger.py
================================================
def print_signature():
llasm = """\
__ __ __
/ / / / __ _/ _\ /\/\
/ / / / / _` \ \ / \
/ /___/ /__| (_| |\ \/ /\/\ \\
\____/\____/\__,_\__/\/ \/
"""
logo = """\
__ _ _ __ _
/ /(_)_ __ | | __/ _\ ___ _ _| |
/ / | | '_ \| |/ /\ \ / _ \| | | | |
/ /__| | | | | < _\ \ (_) | |_| | |
\____/_|_| |_|_|\_\\\__/\___/ \__,_|_|
"""
print ("="*80)
print (llasm)
print (logo)
print ("-"*80)
print ("Demo/HuggingFace: https://huggingface.co/spaces/LinkSoul/LLaSM")
print ("欢迎点一点 Star ^_^")
print ("="*80)
if __name__ == '__main__':
print_signature()
================================================
FILE: pyproject.toml
================================================
[build-system]
requires = ["setuptools>=61.0"]
build-backend = "setuptools.build_meta"
[project]
name = "llasm"
version = "1.0.0"
description = "LLaSM: Large Language and Speech Model."
readme = "README.md"
requires-python = ">=3.8"
classifiers = [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: Apache Software License",
]
dependencies = [
"numpy", "requests",
"librosa", "protobuf", "accelerate",
"tokenizers>=0.12.1",
"torch", "torchvision",
"transformers==4.31.0",
"sentencepiece==0.1.99",
]
[project.urls]
"Homepage" = "https://huggingface.co/spaces/LinkSoul/LLaSM"
"Bug Tracker" = "https://github.com/LinkSoul-AI/LLaSM/issues"
[tool.setuptools.packages.find]
exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]
[tool.wheel]
exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]