README.md

---
language:
- en
license: other
tags:
- code
datasets:
- reciprocate/dpo_ultra-capybara-code_filtered-best
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/LICENSE
pipeline_tag: text-generation
model-index:
- name: Coder1.8-ORPO-TEST
  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: 38.82
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Coder1.8-ORPO-TEST
      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: 60.48
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Coder1.8-ORPO-TEST
      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: 46.7
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Coder1.8-ORPO-TEST
      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: 41.38
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Coder1.8-ORPO-TEST
      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: 59.75
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Coder1.8-ORPO-TEST
      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: 27.45
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Coder1.8-ORPO-TEST
      name: Open LLM Leaderboard
---

# Coder1.8-ORPO-TEST

## Model Description

Test model for ORPO finetune method, trained on ~20k code examples for 1 epoch on 2 x A40 cards with 4-bit QLora (lora rank=lora alpha=16).

## Disclaimer

This is a test model and may generate incorrect responses. Use at your own risk.

## Train Details

- Base: Qwen1.5-1.8B
- Training Data: ~20k [code examples](https://huggingface.co/datasets/reciprocate/dpo_ultra-capybara-code_filtered-best)
- Epochs: 1
- Method: ORPO
- Hardware: 2 x A40
- Quantization: 4-bit QLora
- Lora Rank/Alpha: 16

# Limitations

Limited training data and quantization may impact performance.

# Join the Discussion

Have questions or feedback? Join our Discord server [Here](https://discord.gg/KugcbJX5).
# [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_raincandy-u__Coder1.8-ORPO-TEST)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |45.76|
|AI2 Reasoning Challenge (25-Shot)|38.82|
|HellaSwag (10-Shot)              |60.48|
|MMLU (5-Shot)                    |46.70|
|TruthfulQA (0-shot)              |41.38|
|Winogrande (5-shot)              |59.75|
|GSM8k (5-shot)                   |27.45|