--- 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: ``` ## 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 ``` | Tasks |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| | - truthfulqa_gen|Yaml |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| | - truthfulqa_mc1|Yaml |none | 0|acc | 0.2534|± |0.0152| | - truthfulqa_mc2|Yaml |none | 0|acc | 0.3798|± |0.0139| | 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| ```