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

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

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.

python

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

BAAI/COIG-PC

Training Procedure

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

Evaluation

Testing Data

20% of the BAAI/COIG-PC dataset.

Citation

@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}
}

ZX9966/bwx-13B-hf

Model Card for Model ID

Model Details

Model Description

Model Sources [optional]

Uses