---
license: apache-2.0
language:
- ko
pipeline_tag: question-answering
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This is a merged version from the trained QLoRa Adapter, [jangmin/qlora-llama2-7b-chat-hf-food-order-understanding-30K](jangmin/qlora-llama2-7b-chat-hf-food-order-understanding-30K).

Also the adapter was trained above the foundation model [meta-llama/Llama-2-7b-chat-hf](meta-llama/Llama-2-7b-chat-hf). 

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** [Jangmin Oh](https://huggingface.co/jangmin)
- **Model type:** llama2
- **Language(s) (NLP):** ko
- **License:** apche-2.0
- **Finetuned from model [optional]:** [meta-llama/Llama-2-7b-chat-hf](meta-llama/Llama-2-7b-chat-hf)

## Uses

Step 1. load the model and the tokenizer.

  ```python
  merged_model_hub_id = 'jangmin/merged-llama2-7b-chat-hf-food-order-understanding-30K'
  tokenizer = AutoTokenizer.from_pretrained(merged_model_hub_id)
  model = AutoModelForCausalLM.from_pretrained(merged_model_hub_id, device_map="auto", torch_dtype=torch.float16, cache_dir=cache_dir) 
  ```

Step 2. prepare auxiliary tools

  ```python
  instruction_prompt_template = """### 다음 주문 문장을 분석하여 음식명, 옵션명, 수량을 추출해줘.
  
  ### 명령: {0} ### 응답:
  """
  
  def generate_helper(pipeline, query):   
      prompt = instruction_prompt_template.format(query)
  
      out = pipeline(prompt, max_new_tokens=256, do_sample=False, eos_token_id=tokenizer.eos_token_id)
  
      generated_text = out[0]["generated_text"][len(prompt):]
  
      return generated_text

  pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

stat_dic = pd.DataFrame.from_records([generate_helper(pipe, query) for query in evaluation_queries])
  ```

Step 3. let's rock & roll.

  ```python
  print(generate_helpher(pipe, "아이스아메리카노 톨사이즈 한잔 하고요. 딸기스무디 한잔 주세요. 또, 콜드브루라떼 하나요."))
  ```

## Bias, Risks, and Limitations

Please refer [jangmin/qlora-llama2-7b-chat-hf-food-order-understanding-30K](jangmin/qlora-llama2-7b-chat-hf-food-order-understanding-30K) for the information about Bias, Risk, and Limitations.


## Training Details

### Training Procedure

Please refer [jangmin/qlora-llama2-7b-chat-hf-food-order-understanding-30K](jangmin/qlora-llama2-7b-chat-hf-food-order-understanding-30K). You can find the fine-tuning strategy.

### Merging Procedure

To merge the adapter on the pretrained model, I wrote following codes.

Step 1. initialize.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, AutoConfig, pipeline
from peft import PeftModel, PeftConfig, AutoPeftModelForCausalLM

peft_model_id = "jangmin/qlora-llama2-7b-chat-hf-food-order-understanding-30K"
config = PeftConfig.from_pretrained(peft_model_id)

IGNORE_INDEX = -100
DEFAULT_PAD_TOKEN = "[PAD]"
```

Step 2. load the fine-tuned model and the tokenzer.
```python
device_map = "cpu"
trained_model = AutoPeftModelForCausalLM.from_pretrained(
    peft_model_id,
    low_cpu_mem_usage=True,
    return_dict=True,
    torch_dtype=torch.float16,
    device_map=device_map,
    cache_dir=cache_dir
)

tokenizer = AutoTokenizer.from_pretrained(
    config.base_model_name_or_path,     
    padding_side='right',
    tokenizer_type="llama",
    trust_remote_code=True,
    cache_dir=cache_dir
)
```

Step 3. Modify the model and the tokenizer to treat the `PAD` token. (llama tokenizer needs to incorporate the pad token into the vocabulary. )

```python
def smart_tokenizer_and_embedding_resize(
    special_tokens_dict: Dict,
    tokenizer: transformers.PreTrainedTokenizer,
    model: transformers.PreTrainedModel,
):
    """Resize tokenizer and embedding.

    Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
    """
    num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
    model.resize_token_embeddings(len(tokenizer))

    if num_new_tokens > 0:
        input_embeddings_data = model.get_input_embeddings().weight.data

        input_embeddings_avg = input_embeddings_data[:-num_new_tokens].mean(
            dim=0, keepdim=True
        )

        input_embeddings_data[-num_new_tokens:] = input_embeddings_avg

if with_pad_token and tokenizer._pad_token is None:
    smart_tokenizer_and_embedding_resize(
        special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN),
        tokenizer=tokenizer,
        model=trained_model,
    )
    trained_model.config.pad_token_id = tokenizer.pad_token_id
```

Step 4. merge and push to hub.

```python
merged_model = trained_model.merge_and_unload()

hub_id = "jangmin/merged-llama2-7b-chat-hf-food-order-understanding-30K"

merged_model.push_to_hub(hub_id, max_shard_size="4GB", safe_serialization=True, commit_message='recommit after pad_token was treated.')
```