---
license: llama2
model-index:
- name: XwinCoder-34B
  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: 51.02
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Xwin-LM/XwinCoder-34B
      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: 74.02
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Xwin-LM/XwinCoder-34B
      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: 49.53
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Xwin-LM/XwinCoder-34B
      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: 43.82
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Xwin-LM/XwinCoder-34B
      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: 68.35
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Xwin-LM/XwinCoder-34B
      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: 39.35
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Xwin-LM/XwinCoder-34B
      name: Open LLM Leaderboard
---
# XwinCoder

We are glad to introduce our instruction finetuned code generation models based on CodeLLaMA: XwinCoder. We release model weights and evaluation code.

**Repository:** [https://github.com/Xwin-LM/Xwin-LM/tree/main/Xwin-Coder](https://github.com/Xwin-LM/Xwin-LM/tree/main/Xwin-Coder)

**Models:**
| Model | 🤗hf link | HumanEval pass@1 | MBPP pass@1 | APPS-intro pass@5 |
|-------|------------|----------|------|-------------|
| XwinCoder-7B | [link](https://huggingface.co/Xwin-LM/XwinCoder-7B) | 63.8 | 57.4 | 31.5 |
| XwinCoder-13B | [link](https://huggingface.co/Xwin-LM/XwinCoder-13B) | 68.8 | 60.1 | 35.4 |
| XwinCoder-34B | [link](https://huggingface.co/Xwin-LM/XwinCoder-34B) | 74.2 | 64.8 | 43.0 |

## Updates
- 💥 We released  [**XwinCoder-7B**](https://huggingface.co/Xwin-LM/XwinCoder-7B), [**XwinCoder-13B**](https://huggingface.co/Xwin-LM/XwinCoder-13B), [**XwinCoder-34B**](https://huggingface.co/Xwin-LM/XwinCoder-34B). Our XwinCoder-34B reached 74.2 on HumanEval and it **achieves comparable performance as GPT-3.5-turbo on 6 benchmarks**.

- We support evaluating instruction finetuned models on HumanEval, MBPP, APPS, DS1000 and MT-Bench. See our github repository.

## Overview

![Chat demo](rader.png)

* To fully demonstrate our model's coding capabilities in real-world usage scenarios, we have conducted thorough evaluations on several existing mainstream coding capability leaderboards (rather than only on the currently most popular HumanEval). 
* As shown in the radar chart results, our 34B model **achieves comparable performance as GPT-3.5-turbo on coding abilities**. 
* It is worth mentioning that, to ensure accurate visualization, our radar chart has not been scaled (only translated; MT-Bench score is scaled by 10x to be more comparable with other benchmarks).
* Multiple-E-avg6 refer to the 6 languages used in CodeLLaMA paper. Results of GPT-4 and GPT-3.5-turbo are conducted by us, more details will be released later.

## Demo
We provide a chat demo in our github repository, here are some examples:
![Chat demo](exm.gif)




# [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_Xwin-LM__XwinCoder-34B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |54.35|
|AI2 Reasoning Challenge (25-Shot)|51.02|
|HellaSwag (10-Shot)              |74.02|
|MMLU (5-Shot)                    |49.53|
|TruthfulQA (0-shot)              |43.82|
|Winogrande (5-shot)              |68.35|
|GSM8k (5-shot)                   |39.35|