/ Модель

Характеристики

Контекст
32 768 токенов
Параметры
508 млн
Лицензия
Apache 2.0
Архитектура
qwen3 · dense
Базовая модель
Qwen3-0.6B-Base
Библиотека
transformers
Опубликована
27 ноя 2025

Qwen3-0.6B-Alpaca

Генерация текстаТекст → текст

Компактная инструктивно дообученная Qwen3-модель для генерации ответов, объяснений, пересказов и простого кода.

233скачиваний 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

Qwen3-0.6B-Alpaca — плотная языковая модель примерно на 508 млн параметров, созданная автором репозитория на базе Qwen/Qwen3-0.6B-Base. Модель дообучена методом supervised fine-tuning (SFT) на наборах инструкций Alpaca; карточка указывает TRL SFTTrainer и Unsloth. Используется для генерации ответов на инструкции, объяснений, кратких пересказов и простых фрагментов кода в диалоговом формате.

Модель принимает текст и генерирует текст. В примерах показаны английский и китайский языки, а также задачи на объяснение, написание кода и ответы на вопросы. Контекстное окно — 32 768 токенов. Репозиторий содержит веса Safetensors и использует библиотеку Transformers. Модель компактна, но примеры также показывают возможные ошибки рассуждения и неточности; качество ответов следует проверять. Лицензия — Apache-2.0.

Сценарии применения

  • ответы на простые инструкции и вопросы
  • объяснение понятий простым языком
  • краткий пересказ текстового фрагмента
  • генерация простых функций и примеров кода
  • диалоговые ответы на английском и китайском

Ограничения

Небольшой размер модели может ограничивать сложность и точность ответов. Примеры демонстрируют ошибки в рассуждении и фактические неточности, поэтому результаты требуют проверки.

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

Qwen3-0.6B-Alpaca

A 0.6 B parameter instruction-tuned model built on Qwen3-0.6B-Base, fine-tuned with SFT on the Alpaca instruction datasets.

📦 Model overview

ItemValue
Base modelQwen/Qwen3-0.6B-Base
Training methodSFT (supervised fine-tuning)
FrameworkTRL SFTTrainer + Unsloth
Training datatatsu-lab/alpaca + RaagulQB/alpaca_coding_dataset_full
Prompt formatAlpaca instruction template
Parameters596.0 M
Context length32,768 tokens
Precisionfp16 (safetensors)

🚀 Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

repo  = "wesjos/Qwen3-0.6B-Alpaca"
tok   = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, device_map="auto")

PROMPT = """Below is an instruction that describes a task. Write a response that \
appropriately completes the request.

### Instruction:
{instruction}

### Input:
{input}

### Response:
"""

text = PROMPT.format(instruction="Explain what a GPU is to a 10-year-old child.", input="")
ids  = tok(text, 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/Qwen3-0.6B-Alpaca")
print(p(PROMPT.format(instruction="Explain what a GPU is to a 10-year-old child.",
                       input=""), 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

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Explain what a GPU is to a 10-year-old child.

### Input:


### Response:
A GPU (Graphics Processing Unit) is like a powerful calculator that can do lots of calculations in a short time. It's used in computers to help them draw pictures and animations on the screen. Think of it like a supercomputer that can do lots of calculations in a short time. It's like having a calculator that can do lots of calculations in a short time.

75 tokens · 4.8 s

Write reverse_linked_list() in Python

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Write a Python function that reverses a singly linked list. Include a docstring.

### Input:


### Response:
Here is a Python function that reverses a singly linked list:

```python
class Node:
    def __init__(self, data=None):
        self.data = data
        self.next = None

def reverse_list(head):
    """
    This function takes a singly linked list as input and returns the reversed linked list.
    """
    prev = None
    current = head
    while current is not None:
        next_node = current.next
        current.next = prev
        prev = current
        current = next_node
    return prev
```

This function uses a while loop to iterate through the linked list. In each iteration, it stores the next node in a variable `next_node`, then reverses the link between the current node and the previous node by setting the `next` attribute of the current node to the previous node. Finally, it updates the `prev` and `current` pointers to move to the next nodes in the list. The function returns the head of the reversed linked list.

207 tokens · 9.7 s

Word problem: 17 sheep, all but 9 run away

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
A farmer has 17 sheep. All but 9 run away. How many are left? Explain your reasoning.

### Input:


### Response:
The problem can be solved by subtracting the number of sheep that ran away from the total number of sheep. In this case, the total number of sheep is 17 and the number of sheep that ran away is 9. Therefore, the number of sheep left is 17 - 9 = 8.

To explain the reasoning, we can break down the problem into smaller steps:

1. We know that the farmer has a total of 17 sheep.
2. We also know that all but 9 sheep ran away.
3. To find out how many sheep are left, we need to subtract the number of sheep that ran away from the total number of sheep.
4. In this case, the number of sheep that ran away is 9, so we subtract 9 from 17 to get 8.
5. Therefore, the number of sheep left is 8.

This reasoning is based on the principle of subtraction. We subtract the number of sheep that ran away from the total number of sheep to find out how many sheep are left. In this case, we subtract 9 from 17 to get 8.

235 tokens · 11.0 s

One-sentence summary of the Voyager 1 passage

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Summarize the following passage in one sentence.

### Input:
The Voyager 1 spacecraft, launched in 1977, has travelled farther from Earth than any other human-made object. It crossed into interstellar space in 2012 and continues to transmit data using power from its radioisotope thermoelectric generators, though mission engineers expect its instruments to be shut down sometime in the 2030s.

### Response:
The Voyager 1 spacecraft, launched in 1977, has traveled beyond Earth's orbit and crossed into interstellar space, continuing to transmit data using radioisotope thermoelectric generators.

41 tokens · 1.9 s

中文 · 用三句话解释「过拟合」

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
用三句话向一个刚学编程的大学生解释什么是「过拟合」。

### Input:


### Response:
过拟合是指在机器学习或深度学习模型中,由于数据量较小或数据分布过于复杂,导致模型在训练集上表现很好,但在测试集或未见过的数据上表现不佳的现象。这种情况通常是由于模型过于复杂,导致它能够很好地拟合训练数据,但无法泛化到新的数据。

例如,考虑一个简单的二分类问题,我们使用一个简单的逻辑回归模型来解决这个问题。如果数据量较小,模型可能能够很好地拟合训练数据,但当数据量增加时,模型可能过拟合,导致在测试数据上表现不佳。

解决过拟合的方法包括:增加数据量、使用更复杂的模型、正则化、交叉验证等。通过增加数据量或使用更复杂的模型,我们可以帮助模型更好地泛化到新的数据。正则化可以限制模型的复杂度,而交叉验证可以帮助我们评估模型的泛化性能。

191 tokens · 8.9 s

English · one concrete anti-procrastination tactic

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
I keep procrastinating on my thesis. Give me one concrete tactic, not generic advice.

### Input:


### Response:
One concrete tactic to help you procrastinate on your thesis is to break down the task into smaller, more manageable chunks. This can help you stay focused and motivated, as well as make the task seem less overwhelming. Here's an example of how you can break down your thesis into smaller chunks:

1. Research: Find a specific topic for your thesis and start gathering information on it. This can involve reading academic papers, conducting interviews, or conducting surveys. Break this down into smaller tasks, such as researching a specific aspect of the topic or gathering data from a particular source.

2. Writing: Once you have gathered the necessary information, start writing your thesis. Break this down into smaller tasks, such as writing the introduction, conducting literature reviews, or writing the main body of your thesis. This can help you stay organized and avoid feeling overwhelmed.

3. Editing: After you have written your thesis, it's time to edit and revise it. Break this down into smaller tasks, such as checking for grammar and spelling errors, formatting the document, or adding any necessary references. This can help you stay focused and avoid feeling like you're constantly working on a large project.

4. Finalizing: Once you have edited and revised your thesis, it's time to finalize it. Break this down into smaller tasks, such as proofreading the final version, formatting the document, and adding any necessary references. This can help you stay organized and avoid
…(truncated)

300 tokens · 13.8 s

🆚 Side-by-side with the original Qwen/Qwen3-0.6B-Base

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

Explain a GPU to a 10-year-old

Qwen3-0.6B-Alpaca

A GPU (Graphics Processing Unit) is like a powerful calculator that can do lots of calculations in a short time. It's used in computers to help them draw pictures and animations on the screen. Think of it like a supercomputer that can do lots of calculations in a short time. It's like having a calculator that can do lots of calculations in a short time.

Qwen/Qwen3-0.6B-Base

A GPU is like a super-fast computer that helps us do lots of things quickly. Imagine you have a big box of toys, and you want to play with all of them at once. A GPU can do this faster than your regular computer because it has a lot of tiny little computers inside it called "cores." These cores can work together to do many things at the same time, just like how you can play with all your toys at once. So, a GPU is a super-fast computer that helps us do lots of things quickly, just like how a big box of toys can be played with at once.

Write reverse_linked_list() in Python

Qwen3-0.6B-Alpaca

Here is a Python function that reverses a singly linked list:

```python
class Node:
    def __init__(self, data=None):
        self.data = data
        self.next = None

def reverse_list(head):
    """
    This function takes a singly linked list as input and returns the reversed linked list.
    """
    prev = None
    current = head
    while current is not None:
        next_node = current.next
        current.next = prev
        prev = current
        current = next_node
    return prev
```

This function uses a while loop to iterate through the linked list. In each iteration, it stores the next node in a variable `next_node`, then reverses the link between the current node and the previous node by setting the `next` attribute of the current node to the previous node. Finally, it updates the `prev` and `current` pointers to move to the next nodes in the list. The function returns th
…(truncated)

Qwen/Qwen3-0.6B-Base

Certainly! Below is a Python function that reverses a singly linked list. The function includes a docstring to describe its purpose, parameters, and return value.

```python
class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

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

    Parameters:
    head (Node): The head node of the linked list to be reversed.

    Returns:
    Node: The new head node of the reversed linked list.
    """
    prev = None
    current = head
    while current is not None:
        next_node = current.next
        current.next = prev
        prev = current
        current = next_node
    return prev

# Example usage:
# Creating a linked list: 1 -> 2 -> 3 -> None
head = Node(1)
head.next = Node(2)
head.next.next = Node(3)

# Reversing the linked list
reversed_head = reverse_linked_list(head)

# Print
…(truncated)

Word problem: 17 sheep, all but 9 run away

Qwen3-0.6B-Alpaca

The problem can be solved by subtracting the number of sheep that ran away from the total number of sheep. In this case, the total number of sheep is 17 and the number of sheep that ran away is 9. Therefore, the number of sheep left is 17 - 9 = 8.

To explain the reasoning, we can break down the problem into smaller steps:

1. We know that the farmer has a total of 17 sheep.
2. We also know that all but 9 sheep ran away.
3. To find out how many sheep are left, we need to subtract the number of sheep that ran away from the total number of sheep.
4. In this case, the number of sheep that ran away is 9, so we subtract 9 from 17 to get 8.
5. Therefore, the number of sheep left is 8.

This reasoning is based on the principle of subtraction. We subtract the number of sheep that ran away from the total number of sheep to find out how many sheep are left. In this case, we subtract 9 from 17 to g
…(truncated)

Qwen/Qwen3-0.6B-Base

The farmer has **9 sheep left**. 

Here's the reasoning:  
The instruction states that "All but 9 run away." This means that all the sheep except for 9 are gone. Since the farmer initially had 17 sheep, and 9 of them are still present, the number of sheep left is **9**.

One-sentence summary of the Voyager 1 passage

Qwen3-0.6B-Alpaca

The Voyager 1 spacecraft, launched in 1977, has traveled beyond Earth's orbit and crossed into interstellar space, continuing to transmit data using radioisotope thermoelectric generators.

Qwen/Qwen3-0.6B-Base

The Voyager 1 spacecraft, launched in 1977, has traveled farther from Earth than any other human-made object, crossed into interstellar space in 2012, and continues to transmit data using radioisotope thermoelectric generators, though mission engineers expect its instruments to be shut down sometime in the 2030s.

中文 · 用三句话解释「过拟合」

Qwen3-0.6B-Alpaca

过拟合是指在机器学习或深度学习模型中,由于数据量较小或数据分布过于复杂,导致模型在训练集上表现很好,但在测试集或未见过的数据上表现不佳的现象。这种情况通常是由于模型过于复杂,导致它能够很好地拟合训练数据,但无法泛化到新的数据。

例如,考虑一个简单的二分类问题,我们使用一个简单的逻辑回归模型来解决这个问题。如果数据量较小,模型可能能够很好地拟合训练数据,但当数据量增加时,模型可能过拟合,导致在测试数据上表现不佳。

解决过拟合的方法包括:增加数据量、使用更复杂的模型、正则化、交叉验证等。通过增加数据量或使用更复杂的模型,我们可以帮助模型更好地泛化到新的数据。正则化可以限制模型的复杂度,而交叉验证可以帮助我们评估模型的泛化性能。

Qwen/Qwen3-0.6B-Base

过拟合是指模型在训练数据上表现很好,但在未见过的数据上表现不佳的现象。它通常发生在模型过于复杂,以至于它不仅学习了训练数据中的模式,还学习了噪声和细节,导致在新数据上泛化能力差。简单来说,过拟合就是模型“记住”了训练数据,而不是从数据中学习到有用的知识。

English · one concrete anti-procrastination tactic

Qwen3-0.6B-Alpaca

One concrete tactic to help you procrastinate on your thesis is to break down the task into smaller, more manageable chunks. This can help you stay focused and motivated, as well as make the task seem less overwhelming. Here's an example of how you can break down your thesis into smaller chunks:

1. Research: Find a specific topic for your thesis and start gathering information on it. This can involve reading academic papers, conducting interviews, or conducting surveys. Break this down into smaller tasks, such as researching a specific aspect of the topic or gathering data from a particular source.

2. Writing: Once you have gathered the necessary information, start writing your thesis. Break this down into smaller tasks, such as writing the introduction, conducting literature reviews, or writing the main body of your thesis. This can help you stay organized and avoid feeling overwhelme
…(truncated)

Qwen/Qwen3-0.6B-Base

To help you overcome procrastination on your thesis, consider implementing the following concrete tactic:

1. **Set Clear Goals**: Break down your thesis into smaller, manageable tasks. Set specific, achievable goals for each day or week. For example, aim to read and understand the literature by the end of the first week, and then focus on writing and revising by the end of the second week.

2. **Create a Study Schedule**: Use a planner or digital calendar to schedule your study sessions. Allocate specific times for reading, writing, and reviewing. Stick to this schedule as much as possible to avoid last-minute rushes.

3. **Minimize Distractions**: Identify and eliminate potential distractions. Turn off notifications on your phone, set your computer to "always on" mode, and create a dedicated study space free from interruptions.

4. **Take Regular Breaks**: Incorporate short breaks into
…(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{Qwen306BAlpaca}
  title = {Qwen3-0.6B-Alpaca},
  note  = {Fine-tuned from Qwen/Qwen3-0.6B-Base},
  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
3bb35ac

0 моделей