README.md

---
language:
- en
license: other
library_name: transformers
tags:
- chat
- qwen
- qwen2
- finetune
- chatml
base_model: Qwen/Qwen2-72B-Instruct
model_name: MaziyarPanahi/Qwen2-72B-Instruct-v0.1
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
inference: false
model_creator: MaziyarPanahi
quantized_by: MaziyarPanahi
model-index:
- name: Qwen2-72B-Instruct-v0.1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 81.63
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/Qwen2-72B-Instruct-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 57.33
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/Qwen2-72B-Instruct-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 36.03
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/Qwen2-72B-Instruct-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 17.45
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/Qwen2-72B-Instruct-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 20.15
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/Qwen2-72B-Instruct-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 49.05
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/Qwen2-72B-Instruct-v0.1
      name: Open LLM Leaderboard
---

<img src="./qwen2-fine-tunes-maziyar-panahi.webp" alt="Qwen2 fine-tune" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

# MaziyarPanahi/Qwen2-72B-Instruct-v0.1

This is a fine-tuned version of the `Qwen/Qwen2-72B-Instruct` model. It aims to improve the base model across all benchmarks.

# ⚡ Quantized GGUF

All GGUF models are available here: [MaziyarPanahi/Qwen2-72B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Qwen2-72B-Instruct-v0.1-GGUF)

# 🏆 [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_MaziyarPanahi__Qwen2-72B-Instruct-v0.1)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |43.61|
|IFEval (0-Shot)    |81.63|
|BBH (3-Shot)       |57.33|
|MATH Lvl 5 (4-Shot)|36.03|
|GPQA (0-shot)      |17.45|
|MuSR (0-shot)      |20.15|
|MMLU-PRO (5-shot)  |49.05|



|    Tasks     |Version|Filter|n-shot|Metric|Value |   |Stderr|
|--------------|------:|------|-----:|------|-----:|---|-----:|
|truthfulqa_mc2|      2|none  |     0|acc   |0.6761|±  |0.0148|

|  Tasks   |Version|Filter|n-shot|Metric|Value |   |Stderr|
|----------|------:|------|-----:|------|-----:|---|-----:|
|winogrande|      1|none  |     5|acc   |0.8248|±  |0.0107|

|    Tasks    |Version|Filter|n-shot| Metric |Value |   |Stderr|
|-------------|------:|------|-----:|--------|-----:|---|-----:|
|arc_challenge|      1|none  |    25|acc     |0.6852|±  |0.0136|
|             |       |none  |    25|acc_norm|0.7184|±  |0.0131|

|Tasks|Version|     Filter     |n-shot|  Metric   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k|      3|strict-match    |     5|exact_match|0.8582|±  |0.0096|
|     |       |flexible-extract|     5|exact_match|0.8893|±  |0.0086|

# Prompt Template

This model uses `ChatML` prompt template:

```
<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}
````

# How to use


```python

# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="MaziyarPanahi/Qwen2-72B-Instruct-v0.1")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Qwen2-72B-Instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/Qwen2-72B-Instruct-v0.1")
```