- Notifications
You must be signed in to change notification settings - Fork68
PhoGPT: Generative Pre-training for Vietnamese (2023)
License
VinAIResearch/PhoGPT
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
We open-source a state-of-the-art 4B-parameter generative model series for Vietnamese, which includes the base pre-trained monolingual model PhoGPT-4B and its chat variant, PhoGPT-4B-Chat. The base model, PhoGPT-4B, with exactly 3.7B parameters, is pre-trained from scratch on a Vietnamese corpus of 102B tokens, with an 8192 context length, employing a vocabulary of 20K token types. The chat variant, PhoGPT-4B-Chat, is the modeling output obtained by fine-tuning PhoGPT-4B on a dataset of 70K instructional prompts and their responses, along with an additional 290K conversations. We demonstrate its superior performance compared to previous open-source models.
More details about the general architecture and experimental results of PhoGPT can be found in ourtechnical report. All output responses of PhoGPT and baselines are availableHERE for readers' self-evaluation.Please CITE our technical report when PhoGPT is used to help produce published results or is incorporated into other software:
@article{PhoGPT,title = {{PhoGPT: Generative Pre-training for Vietnamese}},author = {Dat Quoc Nguyen and Linh The Nguyen and Chi Tran and Dung Ngoc Nguyen and Dinh Phung and Hung Bui},journal = {arXiv preprint},volume = {arXiv:2311.02945},year = {2023}}| Model | Type | Model Size | Context length | Vocab size | Training data size | Note |
|---|---|---|---|---|---|---|
vinai/PhoGPT-4B | Base | 3.7B | 8192 | 20K | 2 training epochs on 482GB of texts | Loading "PhoGPT-4B" or "PhoGPT-4B-Chat" in float16 takes 7GB of GPU memory |
vinai/PhoGPT-4B-Chat | Instruction following & Chat | 3.7B | 8192 | 20K | 70K instructional prompt and response pairs & 290K conversations | PROMPT_TEMPLATE = "### Câu hỏi: {instruction}\n### Trả lời:" |
PhoGPT can run with inference engines, such asvLLM,Text Generation Inference andllama.cpp.
- Compilellama.cpp
- Install Python dependencies from llama.cpp
cd llama.cpppython3 -m pip install -r requirements.txt- Convert the model to gguf FP16 format:
python3 convert-hf-to-gguf.py <path_to_PhoGPT-4B-Chat_model> --outfile ./PhoGPT-4B-Chat.gguf - (Optional) Quantize the model to 4/8-bits:
./quantize ./PhoGPT-4B-Chat.gguf ./PhoGPT-4B-Chat-Q4_K_M.gguf Q4_K_M./quantize ./PhoGPT-4B-Chat.gguf ./PhoGPT-4B-Chat-Q8_0.gguf Q8_0
- Start inference on a gguf model:
./main -m ./PhoGPT-4B-Chat-Q4_K_M.gguf -n 1024 -p "### Câu hỏi: Viết bài văn nghị luận xã hội về an toàn giao thông\n### Trả lời:"
Converted gguf files are available at:vinai/PhoGPT-4B-Chat-gguf. Note thatphogpt_4b_chat_preset.json might be needed for LM Studio to work properly with our gguf files.
# coding: utf8importtorchfromtransformersimportAutoConfig,AutoModelForCausalLM,AutoTokenizermodel_path="vinai/PhoGPT-4B-Chat"config=AutoConfig.from_pretrained(model_path,trust_remote_code=True)config.init_device="cuda"# config.attn_config['attn_impl'] = 'flash' # If installed: this will use either Flash Attention V1 or V2 depending on what is installedmodel=AutoModelForCausalLM.from_pretrained(model_path,config=config,torch_dtype=torch.bfloat16,trust_remote_code=True)# If your GPU does not support bfloat16:# model = AutoModelForCausalLM.from_pretrained(model_path, config=config, torch_dtype=torch.float16, trust_remote_code=True)model.eval()tokenizer=AutoTokenizer.from_pretrained(model_path,trust_remote_code=True)PROMPT_TEMPLATE="### Câu hỏi: {instruction}\n### Trả lời:"# Some instruction examples# instruction = "Viết bài văn nghị luận xã hội về {topic}"# instruction = "Viết bản mô tả công việc cho vị trí {job_title}"# instruction = "Sửa lỗi chính tả:\n{sentence_or_paragraph}"# instruction = "Dựa vào văn bản sau đây:\n{text}\nHãy trả lời câu hỏi: {question}"# instruction = "Tóm tắt văn bản:\n{text}"instruction="Viết bài văn nghị luận xã hội về an toàn giao thông"# instruction = "Sửa lỗi chính tả:\nTriệt phá băng nhóm kướp ô tô, sử dụng \"vũ khí nóng\""input_prompt=PROMPT_TEMPLATE.format_map({"instruction":instruction})input_ids=tokenizer(input_prompt,return_tensors="pt")outputs=model.generate(inputs=input_ids["input_ids"].to("cuda"),attention_mask=input_ids["attention_mask"].to("cuda"),do_sample=True,temperature=1.0,top_k=50,top_p=0.9,max_new_tokens=1024,eos_token_id=tokenizer.eos_token_id,pad_token_id=tokenizer.pad_token_id )response=tokenizer.batch_decode(outputs,skip_special_tokens=True)[0]response=response.split("### Trả lời:")[1]
messages= [ {"role":"user","content":"Kể tên một môn thể thao mạo hiểm"}, {"role":"assistant","content":"Nhảy Bungee."}, {"role":"user","content":"Bạn đã bao giờ đi nhảy bungee chưa"}]# Using apply_chat_templatetokenizer=AutoTokenizer.from_pretrained("vinai/PhoGPT-4B-Chat",trust_remote_code=True)input_prompt=tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)
importtorchfromtransformersimportBitsAndBytesConfig,AutoConfig,AutoModelForCausalLM,AutoTokenizerconfig=AutoConfig.from_pretrained("vinai/PhoGPT-4B-Chat",trust_remote_code=True)config.init_device="cuda"# 8-bit quantizationmodel_8bit=AutoModelForCausalLM.from_pretrained("vinai/PhoGPT-4B-Chat",config=config,load_in_8bit=True)
Seellm-foundry docs for details. To fully fine-tune PhoGPT, users can find an example of model finetuning YAML configuration atfine-tuning-phogpt.yaml. Users can also find thesample_instruction_following_dataset folder as an example of an instruction-following dataset.
- To install
llm-foundry, see Section "Installation" inhttps://github.com/mosaicml/llm-foundry. - Run:
cd llm-foundry/scripts/train/and thencomposer --world_size <number_of_GPUs> train.py <path_to_yaml_configuration_file>(e.g.composer --world_size 1 train.py fine-tuning-phogpt.yaml).
Other fine-tuning options may include the use oftransformers's Trainer (e.g. seestanford_alpaca as an example),lit-gpt orLLaMA-Factory.
PhoGPT has certain limitations. For example, it is not good at tasks involving reasoning, coding or mathematics. PhoGPT may generate harmful, hate speech, biased responses, or answer unsafe questions. Users should be cautious when interacting with PhoGPT that can produce factually incorrect output.