Model Card for Model ID

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This is an experimental product that can be used to create new LLM bassed on Chinese language.

Model Details

Model Description

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• Developed by: yjf9966
• Model type: LLaMA with enhanced tokenizer-size-49964
• Language(s) (NLP): Chinese
• License: Apache-2.0
• Finetuned from model: Chinese-LLaMA-Alpaca

Model Sources [optional]

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• Repository: https://huggingface.co/BlueWhaleX/bwx-13B-HF

Uses

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You can use the raw model for next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering.

Bias, Risks, and Limitations

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Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. It also inherits some of the bias of its dataset model.

Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import LlamaForCausalLM, LlamaTokenizer
import torch

base_model_name = "BlueWhaleX/bwx-13B-hf"
load_type = torch.float16
device = None

generation_config = dict(
    temperature=0.2,
    top_k=40,
    top_p=0.9,
    do_sample=True,
    num_beams=1,
    repetition_penalty=1.3,
    max_new_tokens=400
    )

prompt_input = (
    "Below is an instruction that describes a task. "
    "Write a response that appropriately completes the request.\n\n"
    "### Instruction:\n\n{instruction}\n\n### Response:\n\n"
)
if torch.cuda.is_available():
    device = torch.device(0)
else:
    device = torch.device('cpu')

def generate_prompt(instruction, input=None):
    if input:
        instruction = instruction + '\n' + input
    return prompt_input.format_map({'instruction': instruction})

tokenizer = LlamaTokenizer.from_pretrained(base_model_name)
model = LlamaForCausalLM.from_pretrained(
        base_model_name,
        load_in_8bit=False,
        torch_dtype=load_type,
        low_cpu_mem_usage=True,
        device_map='auto',
        )

model_vocab_size = model.get_input_embeddings().weight.size(0)
tokenzier_vocab_size = len(tokenizer)
if model_vocab_size != tokenzier_vocab_size:
    model.resize_token_embeddings(tokenzier_vocab_size)

raw_input_text = input("Input:")
input_text = generate_prompt(instruction=raw_input_text)
inputs = tokenizer(input_text, return_tensors="pt") 
generation_output = model.generate(
input_ids=inputs["input_ids"].to(device),
    attention_mask=inputs['attention_mask'].to(device),
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.pad_token_id,
    **generation_config
)
s = generation_output[0]
output = tokenizer.decode(s, skip_special_tokens=True)
response = output.split("### Response:")[1].strip()
print("Response: ", response)
print("\n")

Training Details

Training Data

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BAAI/COIG-PC

Training Procedure

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Preprocessing [optional]

80% for train dataset and 20% for test dataset

Training Hyperparameters

• Training regime: fp16 mixed precision, lr=1e-4, lora_rank=8, lora_alpha=32 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Evaluation

Testing Data

<!-- This should link to a Data Card if possible. --> 20% of the BAAI/COIG-PC dataset.

Citation

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@software{bwx-13B-HF,
      title={An Enchanced Chinese Language Model based on the Chinese-Alpaca}, 
      url={https://huggingface.co/BlueWhaleX/bwx-13B-HF},
      year={2023}
}