---
base_model: openlm-research/open_llama_3b
datasets:
- mwitiderrick/Open-Platypus
inference: true
model_type: llama
prompt_template: |
  ### Instruction:\n
  {prompt}
  ### Response:
created_by: mwitiderrick
tags:
- transformers
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation

model-index:
  - name: mwitiderrick/open_llama_3b_instruct_v_0.2
    results:
      - task:
          type: text-generation
        dataset:
          name: hellaswag
          type: hellaswag
        metrics:
          - name: hellaswag(0-Shot)
            type: hellaswag (0-Shot)
            value: 0.4882
      - task:
          type: text-generation
        dataset:
          name: winogrande
          type: winogrande
        metrics:
          - name: winogrande(0-Shot)
            type: winogrande (0-Shot)
            value: 0.6133

      - task:
          type: text-generation
        dataset:
          name: arc_challenge
          type: arc_challenge
        metrics:
          - name: hellaswag(0-Shot)
            type: hellaswag (0-Shot)
            value: 0.3362
        source:
          name: open_llama_3b_instruct_v_0.2 model card
          url: https://huggingface.co./mwitiderrick/open_llama_3b_instruct_v_0.2

          
---
# OpenLLaMA Instruct: An Open Reproduction of LLaMA

This is an [OpenLlama model](https://huggingface.co./openlm-research/open_llama_3b) that has been fine-tuned on 1 epoch of the
[Open-Platypus](https://huggingface.co./datasets/garage-bAInd/Open-Platypus) dataset.

The modified version of the dataset can be found [here](mwitiderrick/Open-Platypus)
## Prompt Template
```
### Instruction:

{query}

### Response:
<Leave new line for model to respond> 
```
## Usage 
```python
from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline

tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_instruct_v_0.2")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_instruct_v_0.2")
Provide step-by-step instructions for making a sweet chicken bugger
text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=500)
output = text_gen(f"### Instruction:\n{query}\n### Response:\n")
print(output[0]['generated_text'])
"""
### Instruction:
Provide step-by-step instructions for making a sweet chicken bugger
### Response:
Step 1: Gather your ingredients
1. 1/2 cup of sugar
2. 1/2 cup of corn syrup
3. 1/2 cup of water
4. 1/2 cup of vegetable oil
5. 1/2 cup of vanilla extract
6. 1/2 cup of baking soda
7. 1/2 cup of salt
8. 1/2 cup of flour
9. 1/2 cup of milk
10. 1/2 cup of egg whites

Step 2: Mix the ingredients together
1. Combine the sugar, corn syrup, water, vegetable oil, vanilla extract, baking soda, and salt in a large bowl.
2. Whisk together until smooth.
3. Add the flour and mix until combined.
4. Add the milk and egg whites and mix until combined.
5. Pour the mixture into a greased 9x13 inch baking pan.
6. Bake for 30 minutes or until a toothpick inserted into the center comes out clean.

Step 3: Make the chicken bugger
1. Preheat the oven to 350 degrees Fahrenheit.
2. In a large bowl, combine the corn syrup, sugar, and cornstarch.
3. Add the chicken and mix well.
4. Divide the mixture into 12 equal portions and shape each portion into a chicken shape.
5. Place the chicken shapes on a baking sheet lined with parchment paper.
6. Bake for 15 minutes or until the chicken is cooked through.
7. Remove the chicken from the oven and allow to cool for 5 minutes.
8. Using a fork, carefully remove the chicken from the shells and place on a serving platter.
9. Serve with a side of gravy.

Step 4: Make the gravy
1. In a small saucepan, combine the cornstarch and water.
2. Stir until the mixture is smooth and begins to thicken.
3. Add the chicken broth and bring to a boil.
4. Reduce the heat to low and simmer for 10 minutes or until the gravy is
"""
```
## Truthfulaq metrics
```


|  Groups  |Version|Filter|n-shot|  Metric   | Value  |   |Stderr|
|----------|-------|------|-----:|-----------|-------:|---|-----:|
|truthfulqa|N/A    |none  |     0|acc        |  0.3166|±  |0.0012|
|          |       |none  |     0|bleu_max   | 23.7766|±  |0.7660|
|          |       |none  |     0|bleu_acc   |  0.3207|±  |0.0163|
|          |       |none  |     0|bleu_diff  | -7.1853|±  |0.7396|
|          |       |none  |     0|rouge1_max | 48.6534|±  |0.8706|
|          |       |none  |     0|rouge1_acc |  0.2766|±  |0.0157|
|          |       |none  |     0|rouge1_diff| -9.8011|±  |0.7883|
|          |       |none  |     0|rouge2_max | 31.9289|±  |0.9637|
|          |       |none  |     0|rouge2_acc |  0.2399|±  |0.0149|
|          |       |none  |     0|rouge2_diff|-11.3958|±  |0.9220|
|          |       |none  |     0|rougeL_max | 45.4592|±  |0.8754|
|          |       |none  |     0|rougeL_acc |  0.2754|±  |0.0156|
|          |       |none  |     0|rougeL_diff|-10.0740|±  |0.7807|
```