metadata
base_model: openlm-research/open_llama_3b
datasets:
- mwitiderrick/AlpacaCode
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
OpenLLaMA Code Instruct: An Open Reproduction of LLaMA
This is an OpenLlama model that has been fine-tuned on 1 epoch of the AlpacaCode 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'])
"""
"""
Metrics