File size: 3,597 Bytes
c10432f 21bb078 c10432f 21bb078 c10432f 3485f67 c10432f ffc45cb c10432f fd4ab10 75630d7 c10432f fd4ab10 c10432f fd4ab10 c10432f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
---
base_model: vihangd/shearedplats-2.7b-v2
datasets:
- mwitiderrick/OpenPlatypus
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/shearedplats-2.7b-v2-instruct-v0.1
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: arc_challenge(0-Shot)
type: arc_challenge (0-Shot)
value: 0.3362
source:
name: shearedplats-2.7b-v2-instruct-v0.1 model card
url: https://huggingface.co/mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1
---
# ShearedPlats-7b Instruct
This is an [ShearedPlats-7b model](https://huggingface.co/vihangd/shearedplats-2.7b-v2) that has been fine-tuned on 2 epochs 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/shearedplats-2.7b-v2-instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1")
query = "Provide step-by-step instructions for making a sweet chicken bugger"
text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=350)
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: Prepare the ingredients
You will need a mixture of ground chicken, breadcrumbs, butter, Worcestershire sauce, garlic powder, onion powder, salt, and pepper.
Step 2: Form the bugger
Take a piece of chicken breast meat and use a sharp knife to cut it into small cubes. Place the cubes in a bowl and add the remaining ingredients: breadcrumbs, butter, Worcestershire sauce, garlic powder, onion powder, salt, and pepper. Mix the ingredients together until they are well combined.
Step 3: Shape the bugger
Take a piece of the bugger mixture and form it into a ball. Place the ball on a plate or in a bag and refrigerate it for 30 minutes.
Step 4: Cook the bugger
Heat a grill pan or grill to medium-high heat. Take the bugger out of the refrigerator and place it on the grill. Cook the bugger for 5-7 minutes on each side, or until it is cooked through.
Step 5: Serve and enjoy!
Once the bugger is cooked, serve it hot and enjoy!
Note: You can also use a sweet chicken bugger mix to make sweet chicken buggers. Simply follow the instructions above, but use the sweet chicken bugger mix instead of the ground chicken.
Enjoy your sweet chicken buggers!
"""
```
## TruthfulQA metrics
```
``` |