---
title: Quyen-Plus-v0.1
canonical_url: "https://www.modelscope.cn/models/QwenCollection/Quyen-Plus-v0.1"
md_url: "https://www.modelscope.cn/models/QwenCollection/Quyen-Plus-v0.1.md"
repository: QwenCollection/Quyen-Plus-v0.1
last_updated: 2024-11-21
license: other
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
parameters: 7.7B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 2220
stars: 1
---

# Quyen-Plus-v0.1

> Quyen-Plus-v0.1 - QwenCollection 在 ModelScope 开源的模型。Model Description Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:

QwenCollection/Quyen-Plus-v0.1 是 ModelScope 魔搭社区上的 7.7B 参数text-generation模型，采用 other 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: QwenCollection/Quyen-Plus-v0.1
- **License**: other
- **Tasks**: text-generation
- **Parameters**: 7.7B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 2220
- **Stars**: 1
- **Last updated**: 2024-11-21

Source: https://www.modelscope.cn/models/QwenCollection/Quyen-Plus-v0.1

---

# Quyen
<img src="quyen.webp" width="512" height="512" alt="Quyen">

# Model Description
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:

- **Quyen-SE (0.5B)**
- **Quyen-Mini (1.8B)**
- **Quyen (4B)**
- **Quyen-Plus (7B)**
- **Quyen-Pro (14B)**
- **Quyen-Pro-Max (72B)**

All models were trained with SFT and DPO using the following dataset:

- *OpenHermes-2.5* by **Teknium**
- *Capyabara* by **LDJ**
- *argilla/distilabel-capybara-dpo-7k-binarized* by **argilla**
- *orca_dpo_pairs* by **Intel**
- and Private Data by **Ontocord** & **BEE-spoke-data**

# Prompt Template
- All Quyen models use ChatML as the default template:

```
<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Hello world.<|im_end|>
<|im_start|>assistant
```

- You can also use `apply_chat_template`:

```python
messages = [
    {"role": "system", "content": "You are a sentient, superintelligent artificial general intelligence, here to teach and assist me."},
    {"role": "user", "content": "Hello world."}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```

# Benchmarks:

- Coming Soon! We will update the benchmarks later

# Acknowledgement
- We're incredibly grateful to **Tensoic** and **Ontocord** for their generous support with compute and data preparation.
- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes.
# [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_vilm__Quyen-Plus-v0.1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |63.27|
|AI2 Reasoning Challenge (25-Shot)|55.72|
|HellaSwag (10-Shot)              |78.52|
|MMLU (5-Shot)                    |60.45|
|TruthfulQA (0-shot)              |53.60|
|Winogrande (5-shot)              |71.27|
|GSM8k (5-shot)                   |60.05|