/ Модель

Характеристики

Контекст
4 096 токенов
Параметры
8,6 млрд
Размер весов
18,3 ГБ
Лицензия
Apache 2.0
Архитектура
llama · dense
Базовая модель
EuroLLM-9B
Библиотека
transformers
Опубликована
28 ноя 2025

EuroLLM-9B-Alpaca

Генерация текстаТекст → текст

Дообученная на инструкциях Alpaca многоязычная модель для генерации ответов, объяснений, пересказов и кода.

242скачиваний 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

wesjos/EuroLLM-9B-Alpaca — языковая модель на архитектуре Llama с 8,6 млрд параметров, созданная дообучением EuroLLM-9B на инструкциях Alpaca методом SFT. Модель предназначена для генерации ответов по инструкциям: объяснений, кратких пересказов, ответов на вопросы и фрагментов кода. В README приведены примеры на английском и китайском языках.

Модель принимает текст и генерирует текст. Базовая модель поддерживает языки ЕС и ряд дополнительных языков; в карточке этой версии указаны английский и китайский. Контекст — до 4096 токенов. Для запуска предусмотрена библиотека Transformers; веса опубликованы в формате safetensors. Лицензия — Apache-2.0.

Примеры демонстрируют выполнение простых инструкций и генерацию кода, но сами по себе не подтверждают стабильное качество на широком наборе задач. Модель может ошибаться в фактах, рассуждениях и коде; длина контекста ограничивает размер входных материалов.

Сценарии применения

  • ответы на вопросы по текстовым инструкциям
  • объяснение понятий простым языком
  • краткий пересказ текстового фрагмента
  • генерация простых фрагментов кода
  • ответы на вопросы и инструкции на английском и китайском языках

Ограничения

Качество примеров в карточке не заменяет систематическую оценку: модель может выдавать неточные ответы и ошибки в коде. Контекст ограничен 4096 токенами.

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

EuroLLM-9B-Alpaca

The 9 B EuroLLM base model fine-tuned on Alpaca instructions with SFT.

📦 Model overview

ItemValue
Base modelutter-project/EuroLLM-9B
Training methodSFT (supervised fine-tuning)
FrameworkTRL SFTTrainer + Unsloth
Training datatatsu-lab/alpaca
Prompt formatAlpaca instruction template
Parameters5100.6 M
Context length4,096 tokens
Precision4bit-nf4

🚀 Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

repo  = "wesjos/EuroLLM-9B-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/EuroLLM-9B-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, or Graphic Processing Unit, is a type of computer processor that is designed to handle graphics and visual data. It is used to render images and videos, and is often used in gaming and other applications that require fast and efficient processing of visual data. It is like a super-fast artist that can draw and paint images and videos in a very short amount of time.

79 tokens · 8.9 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):
        self.data = data
        self.next = None

def reverse_list(head):
    if head is None:
        return head
    
    prev = None
    curr = head
    
    while curr:
        next_node = curr.next
        curr.next = prev
        prev = curr
        curr = next_node
    
    head = prev
    return head
```

This function takes a head node of the linked list as input and reverses the list. It uses a two-pointer approach to reverse the list. The first pointer, `prev`, is initially set to `None` and the second pointer, `curr`, is set to the head of the linked list. The function then iterates through the linked list, updating the pointers to reverse the list. Finally, the function returns the reversed head node.

237 tokens · 22.6 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 farmer has 17 sheep. 9 sheep ran away, so the farmer has 17 - 9 = 8 sheep left.

31 tokens · 3.1 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, is the farthest human-made object from Earth and continues to transmit data using power from its radioisotope thermoelectric generators.

43 tokens · 4.3 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:
过拟合是指模型过度适应训练数据,导致在训练集上表现良好,但在新的数据上表现不佳。它发生在模型变得过于复杂,开始捕捉训练数据中的噪音和偶然性,而不是模式和关系。过拟合的模型在训练集上表现良好,但在新的数据上表现不佳。

76 tokens · 7.7 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 stop procrastinating on your thesis is to break it down into smaller tasks. Start by breaking your thesis down into smaller tasks, such as researching, writing, and editing. Then, set a timer for 30 minutes and focus on completing one task at a time. When the timer goes off, take a break and repeat the process. This will help you stay focused and motivated, and will make the task of writing your thesis seem less overwhelming.

101 tokens · 9.8 s

🆚 Side-by-side with the original utter-project/EuroLLM-9B

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

EuroLLM-9B-Alpaca

A GPU, or Graphic Processing Unit, is a type of computer processor that is designed to handle graphics and visual data. It is used to render images and videos, and is often used in gaming and other applications that require fast and efficient processing of visual data. It is like a super-fast artist that can draw and paint images and videos in a very short amount of time.

utter-project/EuroLLM-9B

Imagine you're drawing a picture with lots of tiny details, like a big colorful landscape.  

Your brain is like a **CPU**, it's really good at thinking and making decisions, but it's not as fast at drawing lots of little things.

A **GPU** is like a super-fast artist helper! It's really good at drawing lots of little things quickly. So, when you want to make a picture with lots of details, your brain tells the GPU to help out. The GPU draws all the little things really fast, and then your brain puts it all together to make the final picture.

Think of it like this: your brain is the boss, and the GPU is the worker who does the hard work of drawing all the little details!

Write reverse_linked_list() in Python

EuroLLM-9B-Alpaca

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

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

def reverse_list(head):
    if head is None:
        return head
    
    prev = None
    curr = head
    
    while curr:
        next_node = curr.next
        curr.next = prev
        prev = curr
        curr = next_node
    
    head = prev
    return head
```

This function takes a head node of the linked list as input and reverses the list. It uses a two-pointer approach to reverse the list. The first pointer, `prev`, is initially set to `None` and the second pointer, `curr`, is set to the head of the linked list. The function then iterates through the linked list, updating the pointers to reverse the list. Finally, the function returns the reversed head node.

utter-project/EuroLLM-9B

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

    Args:
        head (LinkedListNode): The head of the linked list.

    Returns:
        LinkedListNode: The head of the reversed linked list.
    """
    # TODO: Implement the function to reverse the linked list.
    # Hint: You can use a stack to help you.
    # Hint: You can use a stack to help you.
    # Hint: You can use a stack to help you.
    # Hint: You can use a stack to help you.
    # Hint: You can use a stack to help you.
    # Hint: You can use a stack to help you.
    # Hint: You can use a stack to help you.
    # Hint: You can use a stack to help you.
    # Hint: You can use a stack to help you.
    # Hint: You can use a stack to help you.
    # Hint: You can use a stack to help you.
    # Hint: You can use a stack to help you.
    # Hint: You can use a stack to help you.
    # Hint: Y
…(truncated)

Word problem: 17 sheep, all but 9 run away

EuroLLM-9B-Alpaca

The farmer has 17 sheep. 9 sheep ran away, so the farmer has 17 - 9 = 8 sheep left.

utter-project/EuroLLM-9B

The farmer starts with 17 sheep.  

**Reasoning:**

1. **Identify the initial number:** The farmer has 17 sheep.
2. **Identify the change:** 9 sheep run away.
3. **Calculate the remaining:** Subtract the number of sheep that ran away from the initial number.

**Answer:** 17 - 9 = 8 sheep are left.

One-sentence summary of the Voyager 1 passage

EuroLLM-9B-Alpaca

The Voyager 1 spacecraft, launched in 1977, is the farthest human-made object from Earth and continues to transmit data using power from its radioisotope thermoelectric generators.

utter-project/EuroLLM-9B

Voyager 1, launched in 1977, is the farthest human-made object from Earth, having entered interstellar space in 2012 and continuing to transmit data using radioisotope power, with instrument shutdown expected in the 2030s.

中文 · 用三句话解释「过拟合」

EuroLLM-9B-Alpaca

过拟合是指模型过度适应训练数据,导致在训练集上表现良好,但在新的数据上表现不佳。它发生在模型变得过于复杂,开始捕捉训练数据中的噪音和偶然性,而不是模式和关系。过拟合的模型在训练集上表现良好,但在新的数据上表现不佳。

utter-project/EuroLLM-9B

**过拟合**,简单来说,就是模型过于复杂,捕捉到了数据中的噪声,而不是数据中的真正模式。这会导致模型在训练数据上表现良好,但在新的、未见过的数据上表现不佳。

以下是用三句话解释过拟合的具体内容:

1. **过拟合**,模型过于复杂,捕捉到了数据中的噪声,而不是数据中的真正模式。
2. 过拟合的模型在训练数据上表现良好,但在新的、未见过的数据上表现不佳。
3. 过拟合的模型会过度调整数据,导致在新的数据上表现不佳。


**解释:**

* **过拟合**:模型过于复杂,捕捉到了数据中的噪声,而不是数据中的真正模式。
* **表现不佳**:过拟合的模型在新的、未见过的数据上表现不佳。
* **过度调整数据**:过拟合的模型会过度调整数据,导致在新的数据上表现不佳。



**例子:**

想象一个模型就像一个试图学习

English · one concrete anti-procrastination tactic

EuroLLM-9B-Alpaca

One concrete tactic to help you stop procrastinating on your thesis is to break it down into smaller tasks. Start by breaking your thesis down into smaller tasks, such as researching, writing, and editing. Then, set a timer for 30 minutes and focus on completing one task at a time. When the timer goes off, take a break and repeat the process. This will help you stay focused and motivated, and will make the task of writing your thesis seem less overwhelming.

utter-project/EuroLLM-9B

**Tactic:** Break your thesis into smaller, manageable tasks. 

**Explanation:**

1. **Identify the major sections of your thesis:**  Outline the main chapters or parts of your thesis.
2. **Set specific goals for each section:**  Define what you want to accomplish in each section.
3. **Create a timeline:**  Assign deadlines for each section based on your goals.
4. **Prioritize tasks:**  Determine which sections are most important and tackle those first.
5. **Minimize distractions:**  Create a focused work environment by turning off notifications, finding a quiet space, and using tools to block distracting websites.
6. **Take regular breaks:**  Short breaks can help maintain focus and prevent burnout.
7. **Reward yourself:**  Set up small rewards for completing each section or reaching milestones.
8. **Stay accountable:**  Share your progress with a friend, mentor, or colleague who can pr
…(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 · 4bit-nf4 · Tesla T4 15.4 GiB
Evaluation date2026-10-04
Evaluation prompts6 (4 instruction-style · 2 conversational)

Citation

@misc{EuroLLM9BAlpaca}
  title = {EuroLLM-9B-Alpaca},
  note  = {Fine-tuned from utter-project/EuroLLM-9B},
  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
LlamaForCausalLM
Ревизия весов
f089ac5

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