---
license: apache-2.0
model-index:
- name: open-llama-3b-v2-instruct
  results:
  - 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: 38.48
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-instruct
      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: 70.24
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-instruct
      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: 39.69
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-instruct
      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: 37.96
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-instruct
      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: 65.75
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-instruct
      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: 0.0
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-instruct
      name: Open LLM Leaderboard
---
## Prerequisites
In addition to pytorch and transformers, install required packages:
```
pip install accelerate sentencepiece
```
## Usage
To use, copy the following script:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = 'mediocredev/open-llama-3b-v2-instruct'
tokenizer_id = 'mediocredev/open-llama-3b-v2-instruct'
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)

pipeline = transformers.pipeline(
  'text-generation',
  model=model_id,
  tokenizer=tokenizer,
  torch_dtype=torch.bfloat16,
  device_map='auto',
)

system_message = 'You are a helpful assistant, who always provide explanation.'
user_message = 'How many days are there in a leap year?'

prompt = f'### System:\n{system_message}<|endoftext|>\n### User:\n{user_message}<|endoftext|>\n### Assistant:\n'
response = pipeline(
   prompt,
   max_length=1000,
   repetition_penalty=1.05,
)
response = response[0]['generated_text']
print(response)

# Assistant: A leap year has 366 days. It's an extra day added to the calendar every four years to account for the extra time it takes for Earth to complete one full orbit around the Sun.
```
## Limitations

mediocredev/open-llama-3b-v2-instruct is based on LLaMA 3B v2. It can struggle with factual accuracy, particularly when presented with conflicting information or nuanced topics. Its outputs are not deterministic and require critical evaluation to avoid relying solely on its assertions. Additionally, its generative capabilities, while promising, can sometimes produce factually incorrect or offensive content, necessitating careful curation and human oversight. As an evolving model, LLaMA is still under development, and its limitations in areas like bias mitigation and interpretability are being actively addressed. By using this model responsibly and being aware of its shortcomings, we can unlock its potential while mitigating its risks.

## Contact
Welcome any feedback, questions, and discussions. Feel free to reach out: mediocredev@outlook.com
# [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_mediocredev__open-llama-3b-v2-instruct)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |42.02|
|AI2 Reasoning Challenge (25-Shot)|38.48|
|HellaSwag (10-Shot)              |70.24|
|MMLU (5-Shot)                    |39.69|
|TruthfulQA (0-shot)              |37.96|
|Winogrande (5-shot)              |65.75|
|GSM8k (5-shot)                   | 0.00|