README.md

---
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/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: arc_challenge(0-Shot)
            type: arc_challenge (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

          
---
# ShearedPlats-7b Instruct

This is an [ShearedPlats-7b model](https://huggingface.co/vihangd/shearedplats-2.7b-v2) 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/shearedplats-2.7b-v2-instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/shearedplats-2.7b-v2-instruct-v0.1")
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'])
"""

"""
```
## TruthfulQA metrics
```

```