README.md

---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- transformers
datasets:
- mwitiderrick/AlpacaCode
base_model: mwitiderrick/open_llama_3b_code_instruct_0.1
inference: true
model_type: llama
prompt_template: "<s>[INST] \n{prompt}\n[/INST]\n"
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: 0.66
      name: hellaswag(0-Shot)
  - task:
      type: text-generation
    dataset:
      name: winogrande
      type: winogrande
    metrics:
    - type: winogrande (0-Shot)
      value: 0.6322
      name: winogrande(0-Shot)
  - task:
      type: text-generation
    dataset:
      name: arc_challenge
      type: arc_challenge
    metrics:
    - type: arc_challenge (0-Shot)
      value: 0.3447
      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: 40.7
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_glaive_assistant_v0.1
      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: 67.45
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_glaive_assistant_v0.1
      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: 27.74
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_glaive_assistant_v0.1
      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: 35.86
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_glaive_assistant_v0.1
      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: 64.72
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_glaive_assistant_v0.1
      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: 1.97
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_glaive_assistant_v0.1
      name: Open LLM Leaderboard
---
# 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
[Detailed metrics](https://huggingface.co/datasets/open-llm-leaderboard/details_mwitiderrick__open_llama_3b_glaive_assistant_v0.1)
```
|  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|
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mwitiderrick__open_llama_3b_glaive_assistant_v0.1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |39.74|
|AI2 Reasoning Challenge (25-Shot)|40.70|
|HellaSwag (10-Shot)              |67.45|
|MMLU (5-Shot)                    |27.74|
|TruthfulQA (0-shot)              |35.86|
|Winogrande (5-shot)              |64.72|
|GSM8k (5-shot)                   | 1.97|