mwitiderrick_-_open_llama_3b_instruct_v_0.2-gguf

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open_llama_3b_instruct_v_0.2 - GGUF

• Model creator: https://huggingface.co/mwitiderrick/
• Original model: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2/

Original model description:

language:

• en license: apache-2.0 library_name: transformers tags:

• transformers datasets:

• mwitiderrick/OpenPlatypus base_model: openlm-research/open_llama_3b inference: true model_type: llama prompt_template: '### Instruction:\n

  {prompt}

  Response:

  ' created_by: mwitiderrick 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:
    • type: hellaswag (0-Shot) value: 0.4882 name: hellaswag(0-Shot)
  • task: type: text-generation dataset: name: winogrande type: winogrande metrics:
    • type: winogrande (0-Shot) value: 0.6133 name: winogrande(0-Shot)
  • task: type: text-generation dataset: name: arc_challenge type: arc_challenge metrics:
    • type: arc_challenge (0-Shot) value: 0.3362 name: arc_challenge(0-Shot) source: url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2 name: open_llama_3b_instruct_v_0.2 model card
  • task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
    • type: acc_norm value: 38.48 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_instruct_v_0.2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
    • type: acc_norm value: 66.77 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_instruct_v_0.2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
    • type: acc value: 25.34 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_instruct_v_0.2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
    • type: mc2 value: 38.16 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_instruct_v_0.2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
    • type: acc value: 63.46 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_instruct_v_0.2 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
    • type: acc value: 1.59 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_instruct_v_0.2 name: Open LLM Leaderboard

---

OpenLLaMA Instruct: An Open Reproduction of LLaMA

This is an OpenLlama model that has been fine-tuned on 1 epoch of the Open-Platypus dataset.

The modified version of the dataset can be found here

Prompt Template

### Instruction:

{query}

### Response:
<Leave new line for model to respond> 

Usage

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")
query = "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
"""

TruthfulQA 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|

Open LLM Leaderboard Evaluation Results

Detailed results can be found here