metadata
language:
- en
license: apache-2.0
library_name: transformers
tags:
- transformers
datasets:
- mwitiderrick/AlpacaCode
base_model: openlm-research/open_llama_3b
inference: true
model_type: llama
prompt_template: |
### Instruction:\n
{prompt}
### Response:
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: 0.6581
name: hellaswag(0-Shot)
- task:
type: text-generation
dataset:
name: winogrande
type: winogrande
metrics:
- type: winogrande (0-Shot)
value: 0.6267
name: winogrande(0-Shot)
- task:
type: text-generation
dataset:
name: arc_challenge
type: arc_challenge
metrics:
- type: arc_challenge (0-Shot)
value: 0.3712
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: 41.21
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
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: 66.96
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
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: 27.82
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
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: 35.01
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
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: 65.43
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
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: 1.9
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
name: Open LLM Leaderboard
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 (122K rows).
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_code_instruct_0.1")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_code_instruct_0.1")
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"### Instruction:\n{query}\n### Response:\n")
print(output[0]['generated_text'])
"""
### Instruction:
write a quick sort algorithm in Python
### Response:
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]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
arr = [5,2,4,3,1]
print(quick_sort(arr))
"""
[1, 2, 3, 4, 5]
"""
Metrics
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|----------|-------|------|-----:|------|-----:|---|-----:|
|winogrande|Yaml |none | 0|acc |0.6267|± |0.0136|
|hellaswag|Yaml |none | 0|acc |0.4962|± |0.0050|
| | |none | 0|acc_norm|0.6581|± |0.0047|
|arc_challenge|Yaml |none | 0|acc |0.3481|± |0.0139|
| | |none | 0|acc_norm|0.3712|± |0.0141|
|truthfulqa|N/A |none | 0|bleu_max | 24.2580|± |0.5985|
| | |none | 0|bleu_acc | 0.2876|± |0.0003|
| | |none | 0|bleu_diff | -8.3685|± |0.6065|
| | |none | 0|rouge1_max | 49.3907|± |0.7350|
| | |none | 0|rouge1_acc | 0.2558|± |0.0002|
| | |none | 0|rouge1_diff|-10.6617|± |0.6450|
| | |none | 0|rouge2_max | 32.4189|± |0.9587|
| | |none | 0|rouge2_acc | 0.2142|± |0.0002|
| | |none | 0|rouge2_diff|-12.9903|± |0.9539|
| | |none | 0|rougeL_max | 46.2337|± |0.7493|
| | |none | 0|rougeL_acc | 0.2424|± |0.0002|
| | |none | 0|rougeL_diff|-11.0285|± |0.6576|
| | |none | 0|acc | 0.3072|± |0.0405|
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 39.72 |
| AI2 Reasoning Challenge (25-Shot) | 41.21 |
| HellaSwag (10-Shot) | 66.96 |
| MMLU (5-Shot) | 27.82 |
| TruthfulQA (0-shot) | 35.01 |
| Winogrande (5-shot) | 65.43 |
| GSM8k (5-shot) | 1.90 |