README.md

---
language:
- en
license: apache-2.0
base_model: meta-llama/Llama-2-7b-hf
datasets:
- screevoai/abbvie
model-index:
- name: Abbvie-llama2-7b
  results:
  - task:
      name: Causal Language Modeling
      type: causal-language-modeling
    dataset:
      name: Abbvie Dataset
      type: string
      config: None
      split: train, validation, test
      args: None
    metrics:
    - name: Training loss
      type: None
      value: null
---


## Model Details
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the screevoai/abbvie dataset.


### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4318        | 7     | 1100 | 1.4409          | 

### Libraries to Install

- pip install transformers datasets safetensors huggingface-hub accelerator
- pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

### Authentication needed before running the script

Run the following command in the terminal/jupyter_notebook:

- Terminal: huggingface-cli login
- Jupyter_notebook:
  
  ```python
  >>> from huggingface_hub import notebook_login
  >>> notebook_login()
  ```

**NOTE:** Copy and Paste the token from your Huggingface Account Settings > Access Tokens > Create a new token / Copy the existing one.


### Script 

```python
>>>from datasets import load_dataset
>>>from transformers import AutoModelForCausalLM, AutoTokenizer

>>> # Load model and Tokenizer
>>> model = AutoModelForCausalLM.from_pretrained("screevoai/abbvie-llama2-7b", device_map = "auto")
>>>  tokenizer = AutoTokenizer.from_pretrained("screevoai/abbvie-llama2-7b")
>>>  tokenizer.padding_side='right'
>>>  tokenizer.pad_token = tokenizer.eos_token

>>> # Load the dataset
>>> ds = load_dataset("screevoai/abbvie", split="test", use_auth_token=True)
>>> sample_prompt = ds["Prompt"][0]  # change the row number for testing different prompts

>>> # Generate answer to the prompt using the model
>>> encoded_input = tokenizer(sample_prompt,  return_tensors="pt", add_special_tokens=True)
>>> model_inputs = encoded_input.to('auto')
>>> generated_ids = model.generate(**model_inputs, max_new_tokens=500, do_sample=True, pad_token_id=tokenizer.eos_token_id)
>>> decoded_output = tokenizer.batch_decode(generated_ids)

>>> print(decoded_output[0].replace(prompt, ""))

```