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初始化项目，由ModelHub XC社区提供模型 Model: okwinds/Human-Like-Qwen2.5-7B-Instruct Source: Original Platform
2026-05-19 12:23:13 +08:00
---
license: apache-2.0
tags:
- axolotl
- dpo
- trl
base_model: Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
model-index:
- name: Humanish-Qwen2.5-7B-Instruct
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: 72.84
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Qwen2.5-7B-Instruct
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: 34.48
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Qwen2.5-7B-Instruct
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: 0
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Qwen2.5-7B-Instruct
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: 6.49
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Qwen2.5-7B-Instruct
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: 8.42
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Qwen2.5-7B-Instruct
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: 37.76
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-Qwen2.5-7B-Instruct
name: Open LLM Leaderboard
datasets:
- okwinds/Human-Like-DPO-Dataset
language:
- en
---
# 本模型论文解读，请看公众号文章 👇🏻
### <img src="https://www.modelscope.cn/datasets/okwinds/Human-Like-DPO-Dataset/resolve/master/wechat.png" width="30" height="30" align="absmiddle"> 觉察流 - [AI的“人味儿”从何而来？DPO和LoRA打造更拟人化的AI](https://mp.weixin.qq.com/s/59WEBKi0uGYCwOXsd5FgCw)
<br/>
# 下载方式
SDK下载
```bash
#安装ModelScope
pip install modelscope
```
```python
#SDK模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('okwinds/Human-Like-Qwen2.5-7B-Instruct')
```
Git下载
```
#Git模型下载
git clone https://www.modelscope.cn/okwinds/Human-Like-Qwen2.5-7B-Instruct.git
```
> <span style="color:red;font-size:16px"> 声明：本模型完全转载自 Huggingface 上的 [HumanLLMs/Human-Like-Qwen2.5-7B-Instruct](https://huggingface.co/HumanLLMs/Human-Like-Qwen2.5-7B-Instruct) <br/>更多模型信息，请关注下文👇🏻， 为原模型仓库的中文版说明。</span>
<br/>
#### _仓库作者在此 👇🏻 扫一扫_
<img src="https://www.modelscope.cn/models/okwinds/GPT-2/resolve/master/qrcode_for_jcl_258.jpg" />
_______________________________
<br/>
<br/>
<div align="center">
<img src="https://www.modelscope.cn/models/okwinds/Human-Like-Qwen2.5-7B-Instruct/resolve/master/avatar.jpeg" width="320" height="320" />
<h1>提升大型语言模型中的拟人化响应</h1>
</div>
<p align="center">
&nbsp&nbsp | 🤖 <a href="https://www.modelscope.cn/collections/Human-Like-nirenyingda-38b077cf6d0a44">模型集合</a>&nbsp&nbsp |
&nbsp&nbsp 📊 <a href="https://www.modelscope.cn/datasets/okwinds/Human-Like-DPO-Dataset">数据集</a>&nbsp&nbsp |
&nbsp&nbsp <img src="https://www.modelscope.cn/models/okwinds/Human-Like-Qwen2.5-7B-Instruct/resolve/master/wechat.png" width="22" height="22" align="absmiddle"> <a href="https://mp.weixin.qq.com/s/59WEBKi0uGYCwOXsd5FgCw">论文解读</a>&nbsp&nbsp |
&nbsp&nbsp 📄<a href="https://arxiv.org/abs/2501.05032">论文</a>&nbsp&nbsp |
</p>
# 🚀 Human-Like-Qwen2.5-7B-Instruct
此模型是 Qwen/Qwen2.5-7B-Instruct 的微调版本，专门优化以生成更符合人类和对话式的响应。
微调过程同时采用了低秩自适应（LoRA）和直接偏好优化（DPO）来提升自然语言理解、对话连贯性和交互中的情感智能。
该模型创建过程在研究论文[《增强大型语言模型中的人类似响应》](https://mp.weixin.qq.com/s/59WEBKi0uGYCwOXsd5FgCw)中详细描述。
# 🛠️ 训练配置
- **基础模型:** Qwen2.5-7B-Instruct
- **框架:** Axolotl v0.4.1
- **硬件算力:** 2x NVIDIA A100 (80 GB) GPUs
- **训练时长:** ~2 小时 15 分钟
- **数据集:** 包含约 11,000 个样本的合成数据集，涵盖 256 个不同主题
<details><summary>查看 axolotl config</summary>
axolotl version: `0.4.1`
```yaml
base_model: Qwen/Qwen2.5-7B-Instruct
model_type: AutoModalForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
load_in_8bit: true
load_in_4bit: false
strict: false
chat_template: chatml
rl: dpo
datasets:
- path: HumanLLMs/humanish-dpo-project
type: chatml.prompt_pairs
chat_template: chatml
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./humanish-qwen2.5-7b-instruct
sequence_len: 8192
sample_packing: false
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 8
lora_alpha: 4
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: Humanish-DPO
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
hub_model_id: HumanLLMs/Humanish-Qwen2.5-7B-Instruct
gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:
warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
save_safetensors: true
```
</details><br>
# 💬 Prompt Template
您在使用模型时可以使用 ChatML 格式的 Prompt Template：
### ChatML
```
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>
```
此提示模板可作为聊天模板使用，这意味着您可以使用 `tokenizer.apply_chat_template()` 方法格式化消息：
```python
messages = [
{"role": "system", "content": "You are helpful AI asistant."},
{"role": "user", "content": "Hello!"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
# 🤖 模型集合
| Model | Download |
|:---------------------:|:-----------------------------------------------------------------------:|
| Human-Like-Llama-3-8B-Instruct | 🤖 [Modelscope](https://www.modelscope.cn/models/okwinds/Human-Like-LLama3-8B-Instruct) |
| Human-Like-Qwen-2.5-7B-Instruct | 🤖 [Modelscope](https://www.modelscope.cn/models/okwinds/Human-Like-Qwen2.5-7B-Instruct) |
| Human-Like-Mistral-Nemo-Instruct | 🤖 [Modelscope](https://www.modelscope.cn/models/okwinds/Human-Like-Mistral-Nemo-Instruct-2407) |
<!--# 🔄 Quantizationed versions
## GGUF [@bartowski](https://huggingface.co/bartowski)
- https://huggingface.co/bartowski/Human-Like-LLama3-8B-Instruct-GGUF
- https://huggingface.co/bartowski/Human-Like-Qwen2.5-7B-Instruct-GGUF
- https://huggingface.co/bartowski/Human-Like-Mistral-Nemo-Instruct-2407-GGUF
-->
# 🎯 基准测试结果
| **Group** | **Model** | **Average** | **IFEval** | **BBH** | **MATH Lvl 5** | **GPQA** | **MuSR** | **MMLU-PRO** |
|--------------------------------|--------------------------------|-------------|------------|---------|----------------|----------|----------|--------------|
| **Llama Models** | Human-Like-Llama-3-8B-Instruct | 22.37 | **64.97** | 28.01 | 8.45 | 0.78 | **2.00** | 30.01 |
| | Llama-3-8B-Instruct | 23.57 | 74.08 | 28.24 | 8.68 | 1.23 | 1.60 | 29.60 |
| | *Difference (Human-Like)* | -1.20 | **-9.11** | -0.23 | -0.23 | -0.45 | +0.40 | +0.41 |
| **Qwen Models** | Human-Like-Qwen-2.5-7B-Instruct | 26.66 | 72.84 | 34.48 | 0.00 | 6.49 | 8.42 | 37.76 |
| | Qwen-2.5-7B-Instruct | 26.86 | 75.85 | 34.89 | 0.00 | 5.48 | 8.45 | 36.52 |
| | *Difference (Human-Like)* | -0.20 | -3.01 | -0.41 | 0.00 | **+1.01**| -0.03 | **+1.24** |
| **Mistral Models** | Human-Like-Mistral-Nemo-Instruct | 22.88 | **54.51** | 32.70 | 7.62 | 5.03 | 9.39 | 28.00 |
| | Mistral-Nemo-Instruct | 23.53 | 63.80 | 29.68 | 5.89 | 5.37 | 8.48 | 27.97 |
| | *Difference (Human-Like)* | -0.65 | **-9.29** | **+3.02**| **+1.73** | -0.34 | +0.91 | +0.03 |
# 📊 数据集
用于微调的数据集是使用 LLaMA 3 模型生成的。该数据集包含 10,884 个样本，涵盖 256 个不同的主题，如科技、日常生活、科学、历史和艺术等。每个样本包括：
- **拟人回复:** 自然、对话式的回答，模仿人类对话。
- **正式回复:** 结构化和精确的答案，语气更加正式。
数据集已开源，可在以下地址获取：
- 👉 [Human-Like-DPO-Dataset](https://www.modelscope.cn/datasets/okwinds/Human-Like-DPO-Dataset)
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