opendata-chinese-llama2-chat

opendata-chinese-llama2

The opendata-chinese-llama2 project is based on the Llama-2 released by Meta. We open-source three 13B models in this project.

• opendata-chinese-llama2-sft, a instructions finetune version based on Llama-2 13B base
• opendata-chinese-llama2-reward, a reward model trained from the above sft model
• opendata-chinese-llama2-chat, a ppo model based on the above sft model and reward model

We trained all of our models on a completely opensource datasets.

• stage-sft: training data collected from QingyiSi/Alpaca-CoT The final dataset is over 5m instructions.
• stage-reward: training data including opensource data from Anthropic/hh-rlhf and data from OpenAssistant/oasst1 and the translated version of both two datasets.
• stage-ppo: training prompts were sampled 50k from stage-sft and stage-reward(only the positive/top output) , training unsupervised data is sampled from the above two stages.

We fully finetuned all our models on DeepSpeed-Chat .

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Models

Training Details

For supervised fine-tuning, we use a cosine learning rate schedule with an initial learning rate of 1e−5, a weight decay of 0.1, a batch size of 64, and a sequence length of 4096 tokens. We train for 2 epochs over the training dataset. We also concat multiple instructions-ouputs pairs to fill the 4096 sequence.

For reward model, we use a cosine learning rate schedule with an initial learning rate of 1e−5, a weight decay of 0.1, a batch size of 128 (128 pairs, 256 rows), and a sequence length of 2048 tokens. We train for 2 epochs over the training dataset.

For ppo stage, we use a constant learning rate of 1e−6, a batch size of 128, a mini batch size of 32, a ppo-epoch of 1, and a sequence length of 2048 tokens. We train for one epoch over the training dataset.

We try to stay close to the training parameters in the original Llama 2 paper.

For the sample format, we format our data to a Human-Assistant template, with Human prefix from Human,USER,[HM] and Assistant prefix from Assistant,AI,[AI], and we add bos token and eos token for each turns.

Model Download

Below are the full models, which can be used directly.

Usage

sft/chat model usage

from transformers import AutoConfig, AutoModelForCausalLM, LlamaTokenizer

def load_model(model_path):
    model_config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(
            model_path,
            config=model_config,
            trust_remote_code=True)
    model = model.eval().cuda()
    tokenizer = LlamaTokenizer.from_pretrained(model_path, fast_tokenizer=True)
    return model, tokenizer
model, tokenizer = load_model("pandaExplosion/opendata-chinese-llama2-chat-13B")

prefix_human = "Human"
prefix_bot = "AI"
query = "给出10个暴富的建议"
text = f"<s>{prefix_human}\n{query}</s><s>{prefix_bot}\n"
input_ids = tokenizer(text, return_tensors="pt", add_special_tokens=False).input_ids
with torch.no_grad():
    outputs = model.generate(
            input_ids, 
            do_sample = False,
            max_new_tokens = 512,
            eos_token_id= tokenizer.eos_token_id,
            pad_token_id= tokenizer.pad_token_id,
            num_return_sequences = 1,
            return_dict_in_generate=True,
            repetition_penalty=1.1,
            top_p=0.95)
    out = outputs.sequences[:, input_ids.shape[-1]:]
    out_text = tokenizer.batch_decode(out, skip_special_tokens=True)
print(out_text)

reward model usage

from transformers import LlamaTokenizer
from reward_model import create_critic_model # modified from deepspeedChat

def load_reward_model(model_path):
    tokenizer = LlamaTokenizer.from_pretrained(model_path, fast_tokenizer=True)
    model = create_critic_model(model_path, tokenizer)
    model = model.eval().cuda()
    return model, tokenizer
model, tokenizer = load_reward_model("pandaExplosion/opendata-chinese-llama2-reward-13B")

prefix_human = "Human"
prefix_bot = "AI"
query = "给出10个暴富的建议"
response = "不给"
prompt = f"<s>{prefix_human}\n{query}</s>" + "\n" +  f"<s>{prefix_bot}\n" 
text = f"<s>{prefix_human}\n{query}</s>" + "\n" +  f"<s>{prefix_bot}\n{response}</s>"
prompt_len = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.shape[-1]
inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False)
with torch.no_grad():
    reward = model.forward_value(
        inputs['input_ids'].cuda(), 
        attention_mask = inputs['attention_mask'].cuda(),
        prompt_length=prompt_len)
    print(reward['chosen_end_scores']) # [-2.5229]

the reward_model.py is in [reward model dir]

Evaluation

Generation Performance

to be continue

NLU Performance Evaluation

We report the C-Eval dataset reuslt of our models.

LLaMA-2 were tested in our own enviroment

Acknowledgments

We would like to express our gratitude to the related projects.

• Llama-2
• llama.cpp
• DeepSpeed-Chat
• QingyiSi/Alpaca-CoT
• Anthropic/hh-rlhf
• OpenAssistant/oasst1