Repository: LinkSoul-AI/LLaSM
Branch: main
Commit: 3443b79c372f
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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。
<p align="center">
<img src=".github/preview.jpg" width="40%">
</p>
## 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
<img src=".github/QRcode.jpg" alt="微信交流群" width="300"/>
================================================
FILE: examples/0.txt
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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 = "<au_patch>"
DEFAULT_AUDIO_START_TOKEN = "<au_start>"
DEFAULT_AUDIO_END_TOKEN = "<au_end>"
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 = "</s>"
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 <au_patch>/<au_start>/<au_end>
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 = "<<SYS>>\n", "\n<</SYS>>\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 += '</s>'
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 = "<au_patch>"
DEFAULT_AUDIO_START_TOKEN = "<au_start>"
DEFAULT_AUDIO_END_TOKEN = "<au_end>"
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*"]
gitextract_2906ffjf/ ├── .gitignore ├── LICENSE ├── README.md ├── examples/ │ ├── 0.txt │ ├── 1.txt │ └── 2.txt ├── infer.py ├── infer_tokenize.py ├── llasm.py ├── logger.py └── pyproject.toml
SYMBOL INDEX (19 symbols across 4 files)
FILE: infer.py
class Setting (line 22) | class Setting:
method __init__ (line 23) | def __init__(self):
function main (line 33) | def main(args):
FILE: infer_tokenize.py
function tokenize_baichuan (line 10) | def tokenize_baichuan(item, tokenizer):
function tokenize_Cllama2 (line 37) | def tokenize_Cllama2(item, tokenizer):
function tokenize (line 68) | def tokenize(item, tokenizer, llm_type):
FILE: llasm.py
class LlaaaConfig (line 23) | class LlaaaConfig(LlamaConfig):
function load_whisper (line 27) | def load_whisper(audio_tower_name):
class LlaaaLlamaModel (line 33) | class LlaaaLlamaModel(LlamaModel):
method __init__ (line 36) | def __init__(self, config: LlamaConfig):
method initialize_audio_modules (line 46) | def initialize_audio_modules(self, audio_tower, audio_token_len, pretr...
method forward (line 74) | def forward(
class LlaaaLlamaForCausalLM (line 155) | class LlaaaLlamaForCausalLM(LlamaForCausalLM):
method __init__ (line 158) | def __init__(self, config):
method get_model (line 167) | def get_model(self):
method forward (line 170) | def forward(
method prepare_inputs_for_generation (line 230) | def prepare_inputs_for_generation(
method initialize_audio_tokenizer (line 252) | def initialize_audio_tokenizer(self, tokenizer, device,
FILE: logger.py
function print_signature (line 1) | def print_signature():
Condensed preview — 11 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (43K chars).
[
{
"path": ".gitignore",
"chars": 3078,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": "LICENSE",
"chars": 11357,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 3086,
"preview": "# LLaSM: Large Language and Speech Model\n\n[](https://huggingface.co/"
},
{
"path": "examples/0.txt",
"chars": 9,
"preview": "请介绍一下勾股定理"
},
{
"path": "examples/1.txt",
"chars": 14,
"preview": "世界上人口最多的是哪个国家\n"
},
{
"path": "examples/2.txt",
"chars": 8,
"preview": "请介绍一下北京\n"
},
{
"path": "infer.py",
"chars": 4388,
"preview": "import os\nimport librosa\nimport argparse\n\nimport torch\n\nfrom transformers import AutoTokenizer\nfrom transformers import "
},
{
"path": "infer_tokenize.py",
"chars": 2959,
"preview": "\n\nB_INST, E_INST = \"[INST]\", \"[/INST]\"\nB_SYS, E_SYS = \"<<SYS>>\\n\", \"\\n<</SYS>>\\n\\n\"\nsimple_audio_conv_multimodal = {\n "
},
{
"path": "llasm.py",
"chars": 13943,
"preview": "from typing import List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom"
},
{
"path": "logger.py",
"chars": 982,
"preview": "def print_signature():\n llasm = \"\"\"\\\n __ __ __ \n "
},
{
"path": "pyproject.toml",
"chars": 914,
"preview": "[build-system]\nrequires = [\"setuptools>=61.0\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[project]\nname = \"llasm\"\nversion"
}
]
About this extraction
This page contains the full source code of the LinkSoul-AI/LLaSM GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 11 files (39.8 KB), approximately 9.7k tokens, and a symbol index with 19 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.