open_llama_3b_glaive_code_v0.1

OpenLLaMA Glaive: An Open Reproduction of LLaMA

This is an OpenLlama model Code Instruct that has been fine-tuned on 1 epoch of the Glaive Assistsnt dataset.

Prompt Template

<s>[INST] {{ user_msg }} [/INST]

Usage

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

|  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

Detailed results can be found here