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---
base_model: mwitiderrick/open_llama_3b_code_instruct_0.1
datasets:
- mwitiderrick/AlpacaCode
inference: true
model_type: llama
prompt_template: |
<s>[INST]
{prompt}
[/INST]
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: 0.6600
- task:
type: text-generation
dataset:
name: winogrande
type: winogrande
metrics:
- name: winogrande(0-Shot)
type: winogrande (0-Shot)
value: 0.6322
- task:
type: text-generation
dataset:
name: arc_challenge
type: arc_challenge
metrics:
- name: arc_challenge(0-Shot)
type: arc_challenge (0-Shot)
value: 0.3447
source:
name: open_llama_3b_instruct_v_0.2 model card
url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2
---
# OpenLLaMA Glaive: An Open Reproduction of LLaMA
This is an [OpenLlama model Code Instruct](https://huggingface.co/mwitiderrick/open_llama_3b_code_instruct_0.1) that has been fine-tuned on 1 epoch of the
[Glaive Assistsnt](https://huggingface.co/datasets/mwitiderrick/glaive-code-assistant) dataset.
## Prompt Template
```
<s>[INST] {{ user_msg }} [/INST]
```
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_glaive_code_v0.1")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_glaive_v0.1")
query = "Write a quick sort algorithm in Python"
text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
output = text_gen(f"<s>[INST]{query}[/INST]")
print(output[0]['generated_text'])
"""
<s>[INST]Write a quick sort algorithm in Python[/INST]
Quick sort is a divide and conquer algorithm that sorts an array in-place.
It works by repeatedly dividing the array into two sub-arrays, sorting
them, and then merging them back together.
Here's a Python implementation of the quick sort algorithm:
def quick_sort(arr):
if len(arr) <= 1:
return arr
else:
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + [pivot] + quick_sort
"""
```
## Metrics
```
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|---------|-------|------|-----:|--------|-----:|---|-----:|
|hellaswag|Yaml |none | 0|acc |0.4974|± |0.0050|
| | |none | 0|acc_norm|0.6600|± |0.0047|
| Groups |Version|Filter|n-shot| Metric | Value | |Stderr|
|----------|-------|------|-----:|-----------|-------:|---|-----:|
|truthfulqa|N/A |none | 0|bleu_max | 23.5771|± |0.5407|
| | |none | 0|bleu_acc | 0.2754|± |0.0002|
| | |none | 0|bleu_diff | -8.1019|± |0.5137|
| | |none | 0|rouge1_max | 49.5707|± |0.6501|
| | |none | 0|rouge1_acc | 0.2607|± |0.0002|
| | |none | 0|rouge1_diff| -9.8962|± |0.5492|
| | |none | 0|rouge2_max | 33.0399|± |0.8237|
| | |none | 0|rouge2_acc | 0.2313|± |0.0002|
| | |none | 0|rouge2_diff|-11.9054|± |0.7963|
| | |none | 0|rougeL_max | 46.3168|± |0.6705|
| | |none | 0|rougeL_acc | 0.2521|± |0.0002|
| | |none | 0|rougeL_diff|-10.1301|± |0.5669|
| | |none | 0|acc | 0.3191|± |0.0405|
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|----------|-------|------|-----:|------|-----:|---|-----:|
|winogrande|Yaml |none | 0|acc |0.6322|± |0.0136|
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------|-------|------|-----:|--------|-----:|---|-----:|
|arc_challenge|Yaml |none | 0|acc |0.3234|± |0.0137|
| | |none | 0|acc_norm|0.3447|± |0.0139|
``` |