/ Модель

Характеристики

Контекст
40 960 токенов
Параметры
3,7 млрд
Размер весов
8 ГБ
Лицензия
Apache 2.0
Архитектура
qwen3 · dense
Базовая модель
Qwen3-4B
Библиотека
transformers
Опубликована
29 ноя 2025

Qwen3-4B-humanlike

Генерация текстаТекст → текст

Диалоговая модель на базе Qwen3-4B, дообученная для генерации более естественных и разговорных текстовых ответов.

231скачиваний за 30 дней0лайковвеса обновлены4 окт 2026

Бенчмарки

Как модель справилась с задачами пользователей ailist и что о ней публикуют открытые лидерборды

Тесты пользователейпока нет

Никто ещё не опубликовал тест этой модели. Соберите бенчмарк из своих задач — результат появится здесь.

  1. Добавьте модель в бенчмарк

    Вместе с другими моделями категории «Текст» — сравнение идёт внутри одной категории.

  2. Опишите свои задачи

    Напишите запросы, при необходимости приложите файлы и эталонные ответы.

  3. Запустите и опубликуйте

    Модели ответят на каждую задачу, судья оценит ответы. Опубликованный результат появится на этой странице.

Публичные оценки

Из открытых лидербордов. Сравнивайте результаты одного теста при одинаковых условиях — отсутствие оценки не значит, что модель хуже.

В подключённых источниках результатов для этой модели пока нет.

Что умеет модель

Описание составлено по карточке модели и метаданным Hugging Face

Qwen3-4B-humanlike — дообученная с помощью ORPO модель на базе Qwen/Qwen3-4B, разработанная автором репозитория wesjos. Она предназначена для диалоговой генерации текста с более естественной, разговорной подачей: ответы могут использовать сокращения, эмодзи и повествование от первого лица. Примеры показывают объяснение понятий, помощь с программированием, ответы на вопросы и советы по повседневным задачам.

Модель имеет плотную архитектуру Qwen3ForCausalLM примерно на 3,7 млрд параметров, принимает текст и генерирует текст. Заявленная длина контекста — 40 960 токенов; в метаданных указаны английский и китайский языки. Репозиторий использует формат safetensors и библиотеку Transformers. Лицензия — Apache-2.0.

Примеры демонстрируют разговорный стиль, однако также показывают риск уверенных ошибок: в задаче про овец дано неверное объяснение формулировки «все, кроме девяти». Поэтому ответы, особенно фактические и логические, следует проверять; карточка не приводит количественных оценок качества.

Сценарии применения

  • объяснение сложных понятий простым языком
  • ответы на вопросы в диалоге
  • написание и разбор кода
  • составление кратких пересказов текстов
  • получение практических советов в разговорном стиле

Ограничения

Пример с задачей про овец содержит уверенную логическую ошибку, поэтому фактические выводы и рассуждения модели нужно проверять. Карточка не сообщает результатов систематической оценки качества.

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

Qwen3-4B-humanlike

A 4 B parameter model tuned with ORPO so that its answers read like a person rather than a template — contractions, emoji, first-person voice.

📦 Model overview

ItemValue
Base modelQwen/Qwen3-4B
Training methodORPO (Odds-Ratio Preference Optimization)
FrameworkTRL ORPOTrainer + Unsloth
Training datahuman-like response preference pairs
Prompt formatQwen3 chat template
Parameters4022.5 M
Context length40,960 tokens
Precisionfp16 (safetensors)

🚀 Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

repo  = "wesjos/Qwen3-4B-humanlike"
tok   = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, device_map="auto")

messages = [{"role": "user", "content": "用三句话解释什么是「过拟合」。"}]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

ids = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**ids, max_new_tokens=256, do_sample=False)
print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))
from transformers import pipeline
p = pipeline("text-generation", model="wesjos/Qwen3-4B-humanlike")
print(p([{"role": "user", "content": "用三句话解释什么是「过拟合」。"}],
        max_new_tokens=256, return_full_text=False)[0]["generated_text"])

🔍 Sample outputs

Greedy decoding (do_sample=False), max_new_tokens=300, one Tesla T4, measured 2026-10-04. Raw transcripts — nothing hand-edited.

Explain a GPU to a 10-year-old

<think>

</think>

Hey there! So, you know how your computer can do a lot of stuff, like play games and make videos, right? Well, a GPU is like a special helper that makes your computer do those things way faster and better.

Imagine you're playing a super cool video game with lots of moving parts, like cars and explosions. Your computer is like a big kid who can do a lot of stuff, but it's a bit slow when it comes to making all those moving parts happen at the same time. That's where the GPU comes in – it's like a super-smart, super-fast kid who helps your computer make all those moving parts happen really quickly and smoothly.

The GPU is like a special brain that tells your computer what to do, and it's got a lot of tiny helpers (called "cores") that work together to make everything happen fast. It's like a team of super-smart kids who all work together to make your computer do its best stuff! 🤩

So, in short, a GPU is like a special helper that makes your computer do cool stuff way faster and better. And that's pretty cool, right? 😊

241 tokens · 14.4 s

Write reverse_linked_list() in Python

<think>

</think>

Here's a Python function that reverses a singly linked list. I'll include a docstring to explain what it does and how to use it.

```python
class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next


def reverse_linked_list(head: ListNode) -> ListNode:
    """
    Reverses a singly linked list.

    Args:
        head (ListNode): The head node of the linked list.

    Returns:
        ListNode: The new head node of the reversed linked list.

    Example:
        >>> node1 = ListNode(1)
        >>> node2 = ListNode(2)
        >>> node3 = ListNode(3)
        >>> node1.next = node2
        >>> node2.next = node3
        >>> new_head = reverse_linked_list(node1)
        >>> new_head.val
        3
        >>> new_head.next.val
        2
        >>> new_head.next.next.val
        1
    """
    # Initialize pointers for the reversed list
    prev = None
    current = head

    # Traverse the original list and reverse the pointers
    while current:
        next_node = current.next  # Store the next node
        current.next = prev  # Reverse the pointer
        prev = current  # Move the previous pointer to the current node
        current = next_node  # Move to the next node

300 tokens · 18.0 s

Word problem: 17 sheep, all but 9 run away

<think>

</think>

😄 Ah, a classic math puzzle! Let's break it down step by step.

So, the farmer has 17 sheep in total. The question says "all but 9 run away." That means 9 sheep did NOT run away, right? Because "all but" implies that the remaining ones are the exceptions.

Think of it like this: if you have a group of people and you say "all but 3 are girls," that means 3 are boys, and the rest are girls. Similarly, here, "all but 9 run away" means 9 sheep did NOT run away, and the rest (17 - 9 = 8) did run away.

Therefore, the number of sheep left is 9, because those are the ones that didn't run away. The ones that ran away are no longer "left" on the farm, I suppose! 🐐

But wait, let me make sure I'm not missing anything. The question is a bit tricky, but I think my reasoning is sound. If you have 17 sheep, and 9 of them stay behind, then 8 run away. So, the number of sheep left on the farm is 9. Yeah, that makes sense! 🙌

What do you think? Did I get it right? 🤔

278 tokens · 16.7 s

One-sentence summary of the Voyager 1 passage

<think>

</think>

The passage talks about how technology is changing the way we live and work, making our lives more convenient and efficient, but also raising concerns about privacy and social isolation.

38 tokens · 2.3 s

中文 · 用三句话解释「过拟合」

<think>

</think>

过拟合,就是模型学得太好了,以至于它把训练数据里的每一个细节都记住了,但到了新数据上就容易出错。比如,你教一个AI认猫,它可能把训练数据里每一张猫的照片都记住了,但到了一只从未见过的猫面前,它可能就认不出来了。过拟合就像模型太贪心了,把训练数据里的每一个小细节都当成了必须记住的东西,结果反而影响了它对新数据的判断力。

112 tokens · 6.9 s

English · one concrete anti-procrastination tactic

<think>

</think>

I'm so with you on that! Procrastination can be super frustrating, especially when it comes to something as important as your thesis. 😩

Okay, here's a concrete tactic that might help: **Break down your tasks into 25-minute increments using the Pomodoro Technique**. 🕒

Here's how it works: Set a timer for 25 minutes, and focus solely on working on your thesis during that time. When the timer goes off, take a 5-minute break to stretch, grab a snack, or do something you enjoy. After four cycles of work and break, take a longer break of 15-30 minutes.

The idea behind this technique is to create a sense of urgency and momentum, making it easier to stay focused and avoid distractions. Plus, it's a great way to manage your energy and avoid burnout. 💪

Try it out for a week and see if it helps you make progress on your thesis. Remember, it's not about working for 25 hours a day (although, let's be real, that would be amazing 😂); it's about creating a rhythm that helps you stay productive and on track.

What do you think? Want to give it a shot? 🤔

264 tokens · 16.1 s

🆚 Side-by-side with the original Qwen/Qwen3-4B

Identical prompt, identical decoding settings, identical hardware — left is this model, right is the un-tuned original.

⚠️ The summary prompt carries its passage in the input field (Alpaca convention). Chat-template models received the instruction without that passage, so only the summary row is not comparable across the two.

Explain a GPU to a 10-year-old

Qwen3-4B-humanlike

<think>

</think>

Hey there! So, you know how your computer can do a lot of stuff, like play games and make videos, right? Well, a GPU is like a special helper that makes your computer do those things way faster and better.

Imagine you're playing a super cool video game with lots of moving parts, like cars and explosions. Your computer is like a big kid who can do a lot of stuff, but it's a bit slow when it comes to making all those moving parts happen at the same time. That's where the GPU comes in – it's like a super-smart, super-fast kid who helps your computer make all those moving parts happen really quickly and smoothly.

The GPU is like a special brain that tells your computer what to do, and it's got a lot of tiny helpers (called "cores") that work together to make everything happen fast. It's like a team of super-smart kids who all work together to make your computer do its be
…(truncated)

Qwen/Qwen3-4B

<think>
Okay, I need to explain what a GPU is to a 10-year-old. Let me start by thinking about what a 10-year-old already knows. They might know about computers and games, maybe even have a tablet or a phone. So, I should use things they're familiar with.

A GPU is a graphics processing unit. But how to make that simple? Maybe compare it to something they know. Like, a GPU is like a special helper in a computer that makes pictures and videos look better. But how to explain that without technical terms?

Maybe use an analogy. Like, imagine you're drawing a really detailed picture. If you're doing it all by yourself, it takes a long time. But if you have a helper who's really good at drawing, they can do it faster and better. That helper is like the GPU. It helps the computer make pictures and videos look smooth and nice.

Wait, but the GPU is part of the computer. So maybe say it's like a
…(truncated)

Write reverse_linked_list() in Python

Qwen3-4B-humanlike

<think>

</think>

Here's a Python function that reverses a singly linked list. I'll include a docstring to explain what it does and how to use it.

```python
class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next


def reverse_linked_list(head: ListNode) -> ListNode:
    """
    Reverses a singly linked list.

    Args:
        head (ListNode): The head node of the linked list.

    Returns:
        ListNode: The new head node of the reversed linked list.

    Example:
        >>> node1 = ListNode(1)
        >>> node2 = ListNode(2)
        >>> node3 = ListNode(3)
        >>> node1.next = node2
        >>> node2.next = node3
        >>> new_head = reverse_linked_list(node1)
        >>> new_head.val
        3
        >>> new_head.next.val
        2
        >>> new_head.next.next.val
        1
    """
    # Initialize pointers for the reve
…(truncated)

Qwen/Qwen3-4B

<think>
Okay, I need to write a Python function that reverses a singly linked list. Let me think about how to approach this. 

First, I remember that a singly linked list has nodes where each node points to the next node. To reverse it, I need to change the direction of these pointers. So, the idea is to iterate through the list and reverse the links between each node.

Let me recall the steps. The standard approach is to use three pointers: previous, current, and next. Initially, previous is None, current is the head of the list. Then, for each step, we save the next node, set current's next to previous, then move previous to current, and current to next. Repeat until current is None. At the end, previous will be the new head.

Wait, right. So the steps are:

1. Initialize prev as None, current as head.
2. While current is not None:
   a. next_node = current.next
   b. current.next = pr
…(truncated)

Word problem: 17 sheep, all but 9 run away

Qwen3-4B-humanlike

<think>

</think>

😄 Ah, a classic math puzzle! Let's break it down step by step.

So, the farmer has 17 sheep in total. The question says "all but 9 run away." That means 9 sheep did NOT run away, right? Because "all but" implies that the remaining ones are the exceptions.

Think of it like this: if you have a group of people and you say "all but 3 are girls," that means 3 are boys, and the rest are girls. Similarly, here, "all but 9 run away" means 9 sheep did NOT run away, and the rest (17 - 9 = 8) did run away.

Therefore, the number of sheep left is 9, because those are the ones that didn't run away. The ones that ran away are no longer "left" on the farm, I suppose! 🐐

But wait, let me make sure I'm not missing anything. The question is a bit tricky, but I think my reasoning is sound. If you have 17 sheep, and 9 of them stay behind, then 8 run away. So, the number of sheep left on 
…(truncated)

Qwen/Qwen3-4B

<think>
Okay, let's see. The problem says a farmer has 17 sheep. All but 9 run away. How many are left? Hmm, I need to figure out how many sheep are remaining after the ones that ran away. 

First, let me parse the sentence. "All but 9 run away." So, "all but 9" means that 9 are not running away, right? So if all the sheep except 9 ran away, then the ones that stayed behind are 9. Wait, but the question is asking how many are left. So if 9 didn't run away, then they are the ones left. But wait, the farmer has 17 sheep total. So if 9 are left, then the answer is 9? But that seems too straightforward. Maybe I'm misunderstanding the wording.

Alternatively, maybe "all but 9 run away" means that 9 are the ones that didn't run away, so the rest (17 - 9) ran away. But then the question is asking how many are left. If 9 didn't run away, then they are the ones left. So the answer is 9. But maybe
…(truncated)

One-sentence summary of the Voyager 1 passage

Qwen3-4B-humanlike

<think>

</think>

The passage talks about how technology is changing the way we live and work, making our lives more convenient and efficient, but also raising concerns about privacy and social isolation.

Qwen/Qwen3-4B

<think>
Okay, the user wants me to summarize a passage in one sentence. But wait, they haven't provided the passage yet. Let me check the query again. The original instruction says "Summarize the following passage in one sentence." But there's no passage attached. Maybe the user forgot to include it? Or perhaps this is part of a larger context where the passage was mentioned earlier? Since I don't have access to previous messages, I should ask for the passage to proceed. I need to make sure I don't try to summarize something that's not there. Let me respond by requesting the passage so I can provide an accurate summary.
</think>

Please provide the passage you'd like summarized, and I'll condense it into one sentence for you.

中文 · 用三句话解释「过拟合」

Qwen3-4B-humanlike

<think>

</think>

过拟合,就是模型学得太好了,以至于它把训练数据里的每一个细节都记住了,但到了新数据上就容易出错。比如,你教一个AI认猫,它可能把训练数据里每一张猫的照片都记住了,但到了一只从未见过的猫面前,它可能就认不出来了。过拟合就像模型太贪心了,把训练数据里的每一个小细节都当成了必须记住的东西,结果反而影响了它对新数据的判断力。

Qwen/Qwen3-4B

<think>
好的,用户让我用三句话向刚学编程的大学生解释什么是“过拟合”。首先,我需要确保自己理解过拟合的概念。过拟合是指模型在训练数据上表现很好,但在新数据上表现差,因为模型过于复杂,记住了训练数据的噪声和细节,而不是学习到一般规律。

接下来,我得考虑用户的需求。用户是刚学编程的大学生,可能对机器学习的基础概念不太熟悉,所以需要用简单易懂的语言,避免专业术语过多。同时,要结合编程中的例子,比如训练模型时的参数调整,或者数据集的大小,这样他们更容易理解。

然后,我需要组织语言,确保三句话涵盖关键点。第一句可以解释过拟合的基本定义,比如模型过于复杂,记住了训练数据的细节。第二句可以举一个例子,比如用复杂的模型拟合训练数据,但测试时表现差。第三句可以说明过拟合的问题,比如模型无法泛化,需要简化模型或增加数据。

还要注意避免使用太技术性的术语,比如“泛化能力”可能需要进一步解释,但可能用户已经接触过这些概念。或者,可以用更简单的比喻,比如“模型像记住了所有细节,但无法处理新情况”。

最后,检查是否符合三句话的要求,每句话是否清晰,有没有重复,是否准确。确保例子贴近编程中的实际场景,比如训练集和

English · one concrete anti-procrastination tactic

Qwen3-4B-humanlike

<think>

</think>

I'm so with you on that! Procrastination can be super frustrating, especially when it comes to something as important as your thesis. 😩

Okay, here's a concrete tactic that might help: **Break down your tasks into 25-minute increments using the Pomodoro Technique**. 🕒

Here's how it works: Set a timer for 25 minutes, and focus solely on working on your thesis during that time. When the timer goes off, take a 5-minute break to stretch, grab a snack, or do something you enjoy. After four cycles of work and break, take a longer break of 15-30 minutes.

The idea behind this technique is to create a sense of urgency and momentum, making it easier to stay focused and avoid distractions. Plus, it's a great way to manage your energy and avoid burnout. 💪

Try it out for a week and see if it helps you make progress on your thesis. Remember, it's not about working for 25 hours a 
…(truncated)

Qwen/Qwen3-4B

<think>
Okay, the user is struggling with procrastination on their thesis and wants a concrete tactic, not generic advice. Let me think about what specific strategies can be effective.

First, I need to avoid the usual advice like "break it down" or "set a schedule" because they're too generic. The user probably already knows those. So, what's a more specific method?

Maybe something related to time management techniques. The Pomodoro Technique is popular, but that's still somewhat generic. Wait, maybe combining it with a specific task. Like, using the Pomodoro Technique but focusing on a single, specific task that's a small part of the thesis.

Alternatively, the "Two-Minute Rule" from GTD. If the task takes less than two minutes, do it immediately. But that's also a bit generic. Maybe applying that to a specific part of the thesis. For example, if they have a section that's just a few 
…(truncated)

📋 Version info

ItemValue
Trained withTRL 0.23.0 · Transformers 4.57.1 · PyTorch 2.8.0 · Datasets 3.6.0
Evaluated withTransformers 5.16.1 · PyTorch 2.11.0+cu128 · fp16 · Tesla T4 15.4 GiB
Evaluation date2026-10-04
Evaluation prompts6 (4 instruction-style · 2 conversational)

Citation

@misc{Qwen34Bhumanlike}
  title = {Qwen3-4B-humanlike},
  note  = {Fine-tuned from Qwen/Qwen3-4B},
  year  = {2026}
}

Как запустить

На своей GPU в ailist. Цена — по текущим предложениям провайдеров

GPU в ailist

Универсальный рантайм · transformers

Можно запустить

Подбираем GPU…

Класс архитектуры
Qwen3ForCausalLM
Ревизия весов
ab5f706

0 моделей