metadata
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
| Metric | Value |
|---|---|
| Avg. | 38.97 |
| AI2 Reasoning Challenge (25-Shot) | 38.48 |
| HellaSwag (10-Shot) | 66.77 |
| MMLU (5-Shot) | 25.34 |
| TruthfulQA (0-shot) | 38.16 |
| Winogrande (5-shot) | 63.46 |
| GSM8k (5-shot) | 1.59 |