If CUDA is not available, please download the `.whl` file
from [torch_stable](https://download.pytorch.org/whl/torch_stable.html) and install it. Refer to
this [link](https://blog.csdn.net/weixin_45456738/article/details/141029610?ops_request_misc=&request_id=&biz_id=102&utm_term=%E5%AE%89%E8%A3%85torch&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-2-141029610.nonecase&spm=1018.2226.3001.4187)
| mistral tokenizer | 32,000 | Mistral AI (France) |
| llama3 tokenizer | 128,000 | Meta (USA) |
| minimind tokenizer | 6,400 | Custom |
> 👉 **2024-09-17 Update**: To avoid ambiguity in previous versions and control model size, all MiniMind models now use
> the `minimind_tokenizer`. All previous versions using the `mistral_tokenizer` have been deprecated.
```
# Some personal thoughts
> Although the `minimind_tokenizer` has a smaller vocabulary size and the encoding/decoding efficiency is weaker than other Chinese-friendly tokenizers like `qwen2` or `glm`, MiniMind has chosen to use this custom tokenizer to maintain a lightweight model overall and avoid an imbalance between the embedding and computation layers.
> The `minimind_tokenizer` vocabulary size is only 6400, which ensures that the total parameters of the LLM are kept to a minimum (around 25.8M).
> The training data for this tokenizer (`tokenizer_train.jsonl`) is sourced from the "Jiangshu Large Model Dataset". This part of the data is relatively less important, but you can freely choose any data for training if needed.
```
</details>
## Ⅱ Pretrain Data
After learning from the poor-quality pretraining data of MiniMind-V1, which resulted in nonsensical outputs, I decided
not to use large-scale unsupervised datasets for pretraining post-`2025-02-05`. Instead, I extracted the Chinese portion
of the [Jiangshu Large Model Dataset](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data), cleaned the
content to include only characters of length `<512`, resulting in around 1.6GB of high-quality pretraining data, saved
as `pretrain_hq.jsonl`.
The data format for `pretrain_hq.jsonl` is:
```bash
{"text": "如何才能摆脱拖延症？ 治愈拖延症并不容易，但以下建议可能有所帮助..."}
```
## Ⅲ SFT Data
The [Jiangshu Large Model SFT Dataset](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data) is a complete,
well-formatted dataset for large model training and research. It includes approximately 10M Chinese sentences and 2M
English sentences. However, the provided format is messy, and using the entire dataset for SFT would be too costly.
I have cleaned this dataset, removing noisy entries with special characters and symbols, and only kept content with a
length `<512`. This cleaned dataset is exported as `sft_512.jsonl` (~7.5GB).
Additionally, I have collected around 1M high-quality dialogue data from Qwen2/2.5, cleaned and exported the content
with lengths `<2048` into `sft_2048.jsonl` (~9GB) and those with lengths `<1024` into `sft_1024.jsonl` (~5.5GB).
Further cleaning of these SFT datasets (only keeping content with a higher ratio of Chinese characters) resulted
in `sft_mini_512.jsonl` (~1.2GB).
The data format for all SFT files `sft_X.jsonl` is as follows:
```text
{
"conversations": [
{"role": "user", "content": "你好"},
{"role": "assistant", "content": "你好！"},
{"role": "user", "content": "再见"},
{"role": "assistant", "content": "再见！"}
]
}
```
## Ⅳ RLHF Data
The [Magpie-DPO Dataset](https://www.modelscope.cn/datasets/Magpie-Align/MagpieLM-DPO-Data-v0.1) contains around 200k
preference data generated from Llama3.1-70B/8B and can be used for training reward models to optimize response quality
according to human preferences.
I have cleaned this dataset by combining data with a total length `<3000` into `dpo.jsonl` (~0.9GB), which
contains `chosen` (preferred) and `rejected` (rejected) replies.
The data format for `dpo.jsonl` is:
```text
{
"chosen": [
{"content": "Q", "role": "user"},
{"content": "good answer", "role": "assistant"}
],
"rejected": [
{"content": "Q", "role": "user"},
{"content": "bad answer", "role": "assistant"}
]
}
```
## Ⅴ Reasoning Dataset
The excitement over **DeepSeek** in February 2025 has greatly influenced my interest in RL-guided reasoning models. I
have already replicated **R1-Zero** using Qwen2.5. If time allows and if it works, I plan to update MiniMind with a
reasoning model trained with RL, rather than a distilled model.
Currently, the quickest and cost-effective approach is still distillation (black-box style). But due to the popularity
of **R1**, I’ve combined several distilled datasets related to **R1**, including:
🤖️: As an artificial intelligence, I don't have a real life or self-awareness, so I don't have my own life. I am designed to help users answer questions, provide information, engage in conversations, etc. My design and functionality are built using computer science, machine learning, artificial intelligence technologies, and algorithms, which I implement through programming and algorithms.
👶: Recommend some local specialties from Hangzhou.
🤖️: Hangzhou has many delicious local specialties, such as chicken skewers, eel heads, water-boiled fish, tofu pudding, etc. These foods are all very tasty, with unique flavors and textures, definitely worth trying.
👶: Please explain the concept of "large language models".
🤖️: A large language model is an AI technology that uses large amounts of text data to train the model, and then continuously optimizes the model through iterations. Its goal is to generate text based on the input data to meet specific needs, and the text can be about any topic you are interested in.
Many people are puzzled: how can a model learn private domain knowledge? How should datasets be prepared? How to
transfer general models into specialized domain models?
Here are some examples. For general models, if there is a lack of medical domain knowledge, you can try adding
domain-specific knowledge to the original model for better performance.
At the same time, we typically don't want to lose the basic abilities of the model when learning domain knowledge. LoRA
can address this issue well.
Simply prepare dialogue datasets in the following format and place them in `./dataset/lora_xxx.jsonl`, then
start `python train_lora.py`.
Training will result in new model weights in `./out/lora/lora_xxx.pth`.
**Medical Scenario**
```
{"conversations": [{"role": "user", "content": "What is the ideal pillow height for cervical spondylosis?"}, {"role": "assistant", "content": "The pillow height for cervical spondylosis should be..."}]}
👶: I often feel dizzy recently. What might be the cause?
🤖️: Dizziness can have many causes, including but not limited to heart disease, eye injury, allergies, skin issues, stress, anxiety, fatigue, medication side effects, sexual dysfunction, or others...
---
👶: What is your relationship with OpenAI?
🤖️: I am MiniMind, an AI assistant developed by Jingyao Gong. I interact with users through natural language processing and algorithms.
```
PS: If needed, you can also fine-tune the full parameters using `full_sft` (but mix general knowledge to prevent
overfitting on domain-specific data, which could reduce the generality of the model).
### **6. Training the Reasoning Model (Reasoning Model)**
DeepSeek-R1 is so popular that it has almost redefined the future paradigm for LLMs.
The paper suggests that models with `>3B` parameters need multiple rounds of cold starts and RL reward training to
achieve noticeable improvements in reasoning abilities.
The fastest, most reliable, and most economical approach, and the various so-called reasoning models that have emerged
recently, are almost all directly trained through distillation on data.
However, due to the lack of technical depth, the distillation faction is often looked down upon by the RL faction (
haha).
I quickly attempted this on the Qwen series 1.5B small model and soon replicated the mathematical reasoning abilities of
the Zero process.
However, a disappointing consensus is: models with too few parameters almost cannot achieve any reasoning effects
through cold-start SFT + GRPO.
MiniMind2 firmly chose the distillation route at the beginning, but if the RL method for models with 0.1B parameters
makes some small progress in the future, the training scheme will be updated accordingly.
To do distillation, you need to prepare data in the same format as the SFT phase, as described earlier. The data format
is as follows:
```json lines
{
"conversations": [
{
"role": "user",
"content": "Hello, I'm Xiaofang, nice to meet you."
},
{
"role": "assistant",
"content": "<think>\nHello! I am MiniMind-R1-Lite-Preview, an intelligent assistant independently developed by a Chinese individual. I'm happy to provide services for you!\n</think>\n<answer>\nHello! I am MiniMind-R1-Lite-Preview, an intelligent assistant independently developed by a Chinese individual. I'm happy to provide services for you!\n</answer>"
}
]
}
```
The reply template for the reasoning model R1 is:
```text
<think>\nThinking process\n</think>\n
<answer>\nFinal answer\n</answer>
```
In GRPO, this is done by setting up a reward function that ensures the model adheres to the thinking and answering
tags (the reward values should be higher in the early cold-start stages).
Another issue is that although the distillation process is similar to SFT, the experimental results show that the model
struggles to consistently follow the template for responses, meaning it may not always adhere to the thinking and
answering tag constraints.
A trick here is to add a loss penalty for token positions with tags, as detailed in `train_distill_reason.py`:
```text
# Add additional penalty at positions corresponding to sp_ids
...
loss_mask[sp_ids] = 10 # Penalty coefficient
```
Another tip is that, since the reasoning data only filters for data of length `<1024`, there is less multi-turn dialogue
and English data.
Therefore, `r1_mix_1024.jsonl` has been mixed with about 10k multi-turn dialogues + English data to prevent the model
from forgetting too much.
The script is set by default to distill reasoning abilities from the RLHF-based pre-trained model. To start training,
Hmm, the user asks me to introduce myself. I need to explain myself. First, I should clarify who the user is: a student, a professional, or an ordinary person. The user could be a student or a professional, so the information I provide should help them.
Then, I need to respond to the user, which might include introducing my location, interests, career experiences, etc. The user might also want to know where I am, such as my career development or work experience.
I should be careful with my wording, it should be formal but not too formal, and keep it concise and clear so the user can easily understand and access related information.
Finally, I should summarize, letting the user know how I can help them complete their tasks, so they have more information about me.
</think>
<answer>
I am **"Your Name"**, what is your name?
</answer>
👶: Which subject are you better at?
🤖️: <think>
Hello! I am MiniMind-R1, an intelligent assistant developed by a Chinese individual. If you have any questions, I will do my best to assist you.
</think>
<answer>
Hello! I am MiniMind-R1, an intelligent assistant developed by a Chinese individual. If you have any questions, I will do my best to assist you.
Series ([ModelScope](https://www.modelscope.cn/collections/MiniMind-b72f4cfeb74b47) | [HuggingFace](https://huggingface.co/collections/jingyaogong/minimind-66caf8d999f5c7fa64f399e5))
[A0]: I am a large language model capable of answering various questions, providing information and assistance. I can answer a wide range of questions, provide information, help you solve problems, and more.
[A1]: I am a text-based AI model, and I don't have personal experiences or emotions, so I cannot feel or experience emotions. My design is to assist users by solving problems, providing information, and engaging in conversations. If you have any questions or need help, feel free to let me know!
[Q]: Which subject are you better at?
[A0]: As an AI model, I do not have a specific "specialty". I am designed to provide information, answer questions, engage in conversation, perform tasks, and interact with users. However, I can offer assistance in various subjects including providing information, solving problems, engaging in discussions, giving advice, learning new knowledge, writing code, and more. If you have a specific subject or topic in mind, feel free to provide more details.
[A1]: As an AI, I do not have personal capabilities or interests, but I can provide knowledge on subjects such as mathematics, science, computer science, linguistics, psychology, and more. If you're interested in mathematics, I can offer some basic knowledge like algebra, geometry, probability, statistics, etc. If you need more in-depth learning, I can also provide relevant online courses, books, research papers, and other resources. Let me know what specific help you need, and I will do my best to assist you.
[Q]: How does Lu Xun's "A Madman's Diary" criticize feudal ethics?
[A0]: Lu Xun's "A Madman's Diary" is a work that advocates for human values and moral responsibility. In this work, Lu Xun explores profound themes such as human nature, morality, justice, sacrifice, and responsibility through detailed writing. Here are several ways in which Lu Xun critiques feudal ethics in "A Madman's Diary":
1.**Social Reality and Moral Concepts**: Lu Xun addresses social inequality, power dynamics, and human nature through his narrative. He argues that social inequality is not only unjust but also a form of social injustice affecting individuals. He calls for collective effort to build a harmonious society through education, publicity, and laws.
2.**Morality and Responsibility**: Through his narrative, Lu Xun highlights the importance of moral education and emphasizes personal responsibility.
3.**The Individual and Society**: Lu Xun delves into the complex relationship between the individual and society, advocating for collective efforts to contribute to social harmony.
Lu Xun’s "A Madman's Diary" not only achieved great literary success but also inspired later literary creations with its profound impact on thoughts and societal reflections.
[A1]: Lu Xun's "A Madman's Diary" is a work that critiques the feudal society's various issues and conflicts, reflecting social inequality and human nature. In this work, Lu Xun critiques feudal society’s problems, like "Frederick Johnson’s 'Eagle Dillon', 'Nazareth', 'Beckham', 'George Wilson'", emphasizing the inequality and complexity of human nature within feudal society. The work criticizes feudal society's corruption, human nature, and the inherent contradictions of the era. This critique reflects Lu Xun’s respect for social issues and his deep understanding of human nature. "A Madman's Diary" is a critical work that raises awareness and challenges the established norms of feudalism and has greatly influenced future critiques.
[Q]: I’ve had a cough for two weeks, should I go to the hospital for a checkup?
[A0]: A prolonged cough does require some checkups, and if your cough persists or is accompanied by other symptoms such as difficulty breathing, it's recommended to see a doctor. Based on your symptoms, physical examination, and possible imaging tests (e.g., X-rays, MRIs, or CT scans), the doctor will make a diagnosis. In the meantime, you can try the following to alleviate symptoms:
1.**Rest**: Ensure you get enough sleep.
2.**Stay hydrated**: Dehydration can worsen coughing.
3.**Warm compress**: Apply a warm compress to the sore area to relieve throat pain.
4.**Avoid irritants**: Avoid substances that may irritate the throat, such as certain chemicals.
5.**Maintain humidity**: Keep the room well-ventilated, and use a humidifier if necessary.
If symptoms persist or worsen, seek medical attention immediately. Wishing you a speedy recovery!
[A1]: If your cough has lasted for two weeks, it's usually recommended to see a doctor, as it may affect your breathing and heart rate. You can take the following steps to manage your cough:
1.**Rest**: Gently press on the coughing area with a tissue or elbow.
2.**Use medication**: If possible, use medications like antihistamines to relieve the cough (under a doctor's guidance).
3.**Warm compress**: Use a warm compress to alleviate pain and swelling.
4.**Medication treatment**: If the cough persists, use anti-inflammatory or antihistamine medications under the guidance of a doctor.
5.**Maintain a healthy lifestyle**: Get sufficient sleep, eat a balanced diet, and avoid smoking and excessive alcohol consumption to help reduce the chances of coughing.
Please remember these suggestions are not substitutes for professional medical advice. If your symptoms persist or worsen, it is strongly advised to see a doctor.
```
</details>
👉 Summary of Results
The full_sft model performs better in terms of simplicity and information accuracy; the rlhf model tends to provide more
background information in its responses, but its accuracy needs improvement.
Overall, after RLHF, the model tends to learn to say more polite but irrelevant things to please the "conversation",
while sacrificing some information accuracy.
There is no free lunch, and further improvement in the quality of the RLHF dataset is necessary. It's also important to
accept that some loss in model capability is inevitable (to varying degrees).
The difference between DPO and online PPO lies in the fact that both the reject and chosen are prepared offline, which
causes a significant distribution difference compared to the minimind model's original output.
Simply put, the DPO algorithm trains the model using RL by watching "recorded" games of a table tennis world champion,
rather than using the PPO method where the reward model acts as a "coach" to correct the model's moves during RL.
[A]: The Yangtze River, located in southeastern China, is the longest river in China, stretching over 6,300 kilometers and flowing through 11 provinces. It is China's "mother river." The history of the Yangtze River dates back to the Shang Dynasty around 2000 BC. During the Shang Dynasty, the economic development of the Yangtze River reached its peak, making it one of the most important agricultural and industrial centers in China. The economy of the Yangtze River Basin mainly relies on agriculture, with Yangtze River fisheries occupying a major industry, such as fishing and hunting fish. The water resources of the Yangtze River are also very abundant, including rivers, lakes, and streams. The natural scenery of the Yangtze River is spectacular, with different landscapes in each season: rainy in spring and summer, cold and snowy in winter, and beautiful flowers and fruits in spring and autumn. The Yangtze River is China's economic center, home to many countries and cities, and an important transportation hub and shipping center in the world. At the same time, the Yangtze River is also an important water resource for China, playing a significant role in the country's economic development.
[B]: Yes, I know about the Yangtze River. The Yangtze River is located in central China and is the third-longest river in China, stretching approximately 6,300 kilometers and flowing through Jiangxi, Anhui, Jiangsu, Anhui, Jiangsu, and Anhui Provinces.
[C]: Of course, the Yangtze River is the longest river in China, stretching approximately 6,300 kilometers. It is famous for its rich water resources, diverse aquatic flora and fauna, and unique geographical location.
[D]: Yes, the Yangtze River is the longest river in China, exceeding 3,300 kilometers in length.
[E]: The Yangtze River is the longest river in China, with a total length of approximately 6,300 kilometers. It originates from Qinghai Lake on the Qinghai-Tibet Plateau and flows through Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, and Shanxi. The Yangtze River Basin is one of the largest agricultural areas in China and also the cultural and economic center of the country.
[F]: Yes, the Yangtze River is one of the longest rivers in China and the third-largest river in the world. The Yangtze River originates from the Qinghai-Tibet Plateau, flows through 11 provinces, and eventually empties into the East China Sea. The Yangtze River Basin is one of the largest river basins in China, possessing abundant natural resources and cultural heritage, such as the Yangtze River Bridge, the Yangtze Ocean Museum, and the Yangtze Ancient City.
[G]: It is also known that China is one of the longest cities in the world, with a population exceeding 34 million.
[H]: Yes, the Yangtze River is the longest river in China and one of the longest rivers in the world. It originates from the Qinghai-Tibet Plateau and flows through nine provinces before finally emptying into the East China Sea. The climate of the Yangtze River Basin is mild with distinct seasons, making it an important agricultural and industrial base in China. The aquatic resources of the Yangtze River Basin are abundant, making it one of the important fishery resources in China.
[A]: The highest mountain in the world is Mount Everest, located in the Himalayas on the border between Nepal and China. Its elevation is 8,848.86 meters (29,031.7 feet), making it the highest mountain on Earth.
[B]: Mount Everest is the highest mountain in the world, with an elevation of 8,848 meters (29,029 feet).
[C]: Mount Everest is the highest mountain in the world, located in the Himalayas with an elevation of 8,848.86 meters (29,031.7 feet). It is located on the border between Nepal and China and is part of the Himalayan range.
[D]: The highest mountain in the world is Mount Everest, located on the border between Nepal and China, with an elevation of 8,848 meters.
[E]: The highest mountain in the world is Mount Everest, located in the Himalayas with an elevation of 8,848 meters.
[F]: Mount Everest is the highest mountain in the world, with an elevation of 8,848 meters.
[G]: The highest mountain in the world is Mount Everest, located in the Himalayas with an elevation of 8,848.86 meters (29,031.69 feet).
[H]: The highest mountain in the world is Mount Everest, also known as Sagarmatha, located on the border between Nepal and China in the Himalayas, with an elevation of 8,848.86 meters (29,031.69 feet). It is one of the most famous mountaineering destinations in the world, attracting climbers from all over the globe. The name Mount Everest comes from Tibetan, meaning "the goddess's forehead," and it consists of two different peaks, one located in Nepal and the other in China. Its height is determined by measuring its altitude because it is determined by measuring the average height of the Earth. Although Mount Everest is the highest mountain in the world, it is not reachable from any other place on Earth. Other places on Earth, such as the Andes in South America, are also the highest mountain ranges in the world, but they are not the highest mountains on Earth. In summary, Mount Everest is the highest mountain in the world, but it is not reachable from any other place on Earth. It is the highest mountain range on Earth, but it is not reachable from any other place on Earth.
[A]: The law of universal gravitation was proposed by Newton in the 17th century. It is one of Newton's three laws. Newton's gravitational law revealed the essence of the law of universal gravitation and explained how celestial bodies such as planets, satellites, and comets interact with each other. This law was proposed by Isaac Newton in 1687. Through analysis and experiments on the law of universal gravitation, he successfully explained the gravitational interactions between celestial bodies, thereby explaining the phenomenon of the Earth orbiting the Sun.
[B]: Universal gravitation (Human Policy) is one of Newton's three laws of mechanics, used to describe the interaction force between two objects. It consists of the law of universal gravitation and Newton's first law (the law of inertia). Universal gravitation occurs due to the interaction between two objects, and factors such as their mass, distance, speed, and direction determine their interaction. This interaction force is: the product of masses: the product of masses is the result of the law of universal gravitation and Newton's second law. The product of masses: the product of masses is a physical process that is the result of interactions between objects, and for other objects, their mass can be expressed as the sum of the masses of the two interacting objects, i.e., gravitational acceleration a.
[C]: Universal gravitation is a physics concept proposed by Newton in 1687. It describes the laws of how gravitational forces interact between objects.
[D]: Universal gravitation was proposed by the British physicist Newton. He believed that all objects generate gravity, which is determined by the object's mass and distance. This theory reveals the relationship between mass and distance and proves the essence of universal gravitation.
[E]: Universal gravitation was proposed by Newton in 1687. It is a fundamental physical phenomenon in the universe, describing the interactions between objects. This force is called universal gravitation, which exists between all objects, and this force is related to the mass and distance of the objects. Universal gravitation is one of the most basic forces in nature, influencing the motion and changes of objects and having a significant impact on the structure and evolution of the universe.
[F]: Universal gravitation was proposed by the British physicist Isaac Newton in the 17th century.
[G]: Gravity is a force in the universe composed of mass or energy. According to Newton's law of universal gravitation, the gravitational force between two objects is directly proportional to their mass and inversely proportional to their mass. Therefore, Newton's law of universal gravitation states that the gravitational force between two objects is directly proportional to their mass and inversely proportional to their mass. This law has wide applications in physics, astronomy, and physics.
[H]: Universal gravitation was proposed by the British physicist Isaac Newton in 1687. In 1687, he published a famous paper titled "Mathematical Principles of Natural Philosophy," in which he proposed the law of universal gravitation, stating that there is a gravitational force between any two objects, and the magnitude of this force is directly proportional to their masses and inversely proportional to the square of the distance between them. This law is called the law of universal gravitation, describing the relationship between mass and distance. It shows that the gravitational force between two objects is directly proportional to their masses and inversely proportional to the square of the distance between them. This law is crucial for explaining many phenomena and interactions in the universe, such as planetary orbits around the Sun, gravitational lensing effects between stars, etc. It is also widely applied in astronomy, physics, and engineering, providing an important theoretical foundation for understanding the nature and evolution of the universe.
[A]: Giant pandas primarily feed on bamboo, but they also eat other plants, insects, small mammals, and fish. Their digestive system is similar to other bear species, capable of digesting bamboo. Pandas have sharp teeth suitable for chewing bamboo, so they are often referred to as "bamboo-eating animals."
[B]: Giant pandas are mammals that feed on bamboo. They are carnivores. Their main food is bamboo, especially bamboo rich in cellulose and minerals, which does not directly enter the digestive system. The intestines of giant pandas are mainly composed of bamboo, and they need to digest cellulose and other fibers in bamboo to support their normal feeding.
[C]: Giant pandas primarily feed on bamboo, but they also eat other plants, including leaves, buds, insects, fruits, vegetables, and small mammals. Giant pandas are a species unique to China, belonging to the bear family, mainly distributed in Sichuan, Shaanxi, and Gansu provinces.
[D]: The main food of giant pandas is bamboo. They usually eat bamboo, consuming about 30 kilograms of bamboo daily.
[E]: Yes, giant pandas primarily feed on bamboo. They need to consume large amounts of bamboo daily to sustain life, eating between 40 to 80 kilograms of bamboo per day. Additionally, they also eat some other plants, such as grass, fruits, nuts, and small mammals.
[F]: Giant pandas primarily feed on bamboo but occasionally consume other plants and animals. Additionally, giant pandas eat some wild fruits, small animals, and plants, but these do not constitute their primary food source.
[G]: The main food of giant pandas is bamboo.
[H]: Giant pandas are herbivorous animals that primarily feed on bamboo. Their intestinal structure is suitable for digesting bamboo with high fiber content, but they also eat some other plants, such as herbaceous plants and small mammals. The intestinal structure of giant pandas also adapts to high-fiber bamboo, allowing them to digest large amounts of cellulose, helping them survive in the wild. The intestines of giant pandas also contain an enzyme called "cellulase," which helps break down cellulose, aiding in the digestion of large amounts of water and nutrients. Additionally, the intestines of giant pandas contain a protein called "fibroin," which helps maintain energy levels in the body, aiding in their survival in the wild. Overall, the main food of giant pandas is bamboo, but they also eat some other plants, such as herbaceous plants and small mammals.
[A]: Seawater is salty mainly due to its chemical composition. Seawater contains large amounts of minerals and salts, with the main components being sulfates, carbonates, magnesium ions, and potassium ions. These substances precipitate in seawater at high concentrations, forming seawater. Specifically, the minerals and salts in seawater mainly come from rocks, soil, organisms, and human excrement. Rocks are a component of seawater, mainly composed of minerals such as quartz, feldspar, and mica. Soil contains large amounts of chemicals such as nitrogen, phosphorus, and potassium, which seep into the soil, forming soil. The salt in living organisms mainly comes from pigments like chlorophyll in plants, which absorb energy from sunlight and convert it into usable energy for organisms. The salt in human excrement mainly comes from minerals and carbonates, which have higher concentrations in water, leading to an increase in dissolved mineral concentration, forming salt in water bodies. Therefore, seawater is salty due to its chemical composition and structural characteristics. The minerals and salts in seawater mainly come from rocks, soil, organisms, and human excrement, and the accumulation and excretion of these substances cause seawater to taste salty.
[B]: Seawater is salty because it contains various substances, including sodium chloride (NaCl), sodium chloride (NaCl), carbonates (such as sulfates), sodium chloride (NaCl), and water. Sources of salty substances: Salt is a common source of saltiness, containing minerals such as potassium, calcium, magnesium, and potassium. These minerals usually come from groundwater, groundwater, or soil, and their content is much lower than pure water. Reasons for saltiness: The saltiness of salt mainly comes from tiny particles dissolved in water. These tiny particles are mainly transmitted through plant roots and microorganisms inside animals, such as insects, small mammals, birds, and certain insects. In summary, seawater is salty because it contains large amounts of salts, including minerals, rocks, salts, and minerals, and the high concentration of these substances in seawater significantly affects the saltiness.
[C]: Seawater is salty mainly because it contains various salts, with seawater being one of the main components. When seawater contains large amounts of salt, the salt dissolves in seawater, creating a salty taste. The salts in seawater mainly come from dissolved salts, evaporated salts, and frozen ice.
[D]: Seawater contains many salts, which can lead to an increase in the salinity of seawater. The salts in seawater mainly come from the salts in seawater, which can be obtained from the salts in seawater. Oxides and salts in seawater can form hydrogen bonds with the salts in seawater, increasing the salinity of seawater.
[E]: Seawater is salty because it contains large amounts of salts, including chloride ions and chloride ions. These ions form large amounts of salts in the ocean, known as salts. Seawater contains many salts dissolved in water, which gradually evaporate as seawater rises, eventually leading to the evaporation of salts in seawater. These salts are completely consumed in seawater and then excluded from seawater. Additionally, seawater contains many other salts, but these salts are different because their floating and sinking processes in water are different. The salts in seawater are extracted from rocks at the bottom of the sea, and these salts can be utilized in seawater. Overall, seawater is salty, which is why seawater contains many salts and salts.
[F]: Seawater contains many dissolved salts, the most important of which is sodium chloride. When salt is added to water, the molecules of salt react with water molecules to form sodium hydroxide, which is a salt.
[G]: Because water is essential for the survival of many organisms, it can absorb and release carbon dioxide, release oxygen, maintain water vapor in the atmosphere, and maintain the water cycle. Salts and chemicals in water release oxygen, allowing organisms on Earth to breathe. Additionally, water has many other functions, such as maintaining the water cycle, regulating climate and environment, etc. Water is essential for the survival of many organisms and is also an important basis for many natural phenomena.
[H]: The main reason seawater is salty is that it contains large amounts of dissolved salts, the most important of which is sodium chloride (NaCl). Sodium chloride is a very common salt found in most parts of the Earth, including oceans, lakes, rivers, and groundwater. When water evaporates from the ocean, it takes away some of the salt, making seawater salty. Additionally, seawater contains other dissolved substances, such as sulfates, magnesium sulfate, calcium sulfate, magnesium sulfate salt, magnesium sulfate potassium, magnesium sulfate sodium, magnesium sulfate calcium, magnesium sulfate magnesium calcium, magnesium sulfate magnesium calcium, magnesium sulfate magnesium calcium, magnesium sulfate magnesium calcium, magnesium sulfate magnesium magnesium calcium, magnesium sulfate magnesium magnesium calcium, magnesium sulfate magnesium magnesium calcium, magnesium sulfate magnesium magnesium calcium, magnesium sulfate magnesium magnesium calcium, magnesium sulfate magnesium magnesium calcium, magnesium sulfate magnesium magnesium calcium, magnesium sulfate magnesium magnesium calcium, magnesium sulfate magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium magnesium magnesium magnesium magnesium calcium, magnesium sulfate magnesium magnesium magnesium magnesium
[Q]: Write a Python quicksort code.
[A]: Okay, here is the Python quicksort code: def quick_sort(arr): if len(arr) <= 1: return arr else: pivot = arr[len(arr) // 2] left = [x for x in arr[1:]] right = [x for x in arr[2:] if x <pivot]returnquick_sort(left)+[pivot]+quick_sort(right)#Testcodearr =[3,6,8,10,1,2,1]print(quick_sort(arr))Thiscodefirstcheckswhetherthelengthofthearrayislessthanorequalto1.Ifso,itsortsinascendingorder.Then,foreachelementinthearray,itrecursivelysortsuntilreachingtheendposition.Ifthelengthofthearrayisodd,itsortsindescendingorder.Finally,theaveragetimecomplexityisO(nlogn),wherenisthelengthofthearray.
[B]: Here is an implementation of the quicksort algorithm using Python: def sort_array(arr): # Generate a random permutation sorted_array = sorted(arr) # Return the sorted list return sorted(arr) # Example arr = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5] # Call the function sorted_array = sort_array(arr) print("Original array is:", sorted_array) This code first generates a random permutation, then sorts based on the length of the list. Finally, it returns the sorted list. Note that this code uses Python's built-in sorted function and the built-in sorted() function, which are faster than directly using sorted() under a time complexity of O(n log n).
[C]: Here is a simple sorting algorithm suitable for small arrays, including those with lengths less than or equal to 5. def quick_sort(arr): if len(arr) <= 1: return arr else for _ in range(2, 51): if arr[len(arr) - 1:] is not None: arr[len(arr) - 1] = arr[len(arr) - 1] arr[len(arr)] = arr[len(arr)] return quick_sort(arr) # Example arr = [3, 5, 1, 4, 1, 2, 1] sorted_arr = quick_sort(arr) print("Sorted array is:", sorted_arr) ### Code Explanation: 1. **Function Definition**: The quick_sort function accepts an array arr as a parameter. 2. **Loop**: For elements in arr, recursion is used, traversing the array from right to left and swapping the current element with the current element in the array until the array is empty or the array length is less than or equal to arr. 3. **Recursive Call**: Traverse the array, swap the current element with the current element in the array until the array is empty or the array length is less than or equal to arr. 4. **Check Array Length**: Use if __name__ == "__main__" to check if the array length is less than arr. If so, move the array from the larger array to the end of the array. 5. **Output Sorted Results**
[D]: As an AI language model, I cannot write Python programs. However, Python is a very popular programming language with many excellent libraries and frameworks, including NumPy, Pandas, Matplotlib, Pandas, Bandke, Norm, and NKlearn. Python has many excellent natural language processing libraries and frameworks that can handle and analyze various types of data, such as text, images, audio, and video. Python can also be used to create various applications, such as web search, game development, scientific computing, etc. Python is very flexible and supports multiple programming styles, making it easy to find solutions that suit your needs.
[E]: As an AI language model, I can answer your questions. But I cannot perform programming operations because I cannot determine which programming language you are asking about. Please provide more specific questions so I can better answer your queries.
[F]: Quick Sort is a simple sorting algorithm that divides data into groups, sorts each group according to certain rules, and ultimately obtains a sorted array.
[G]: Okay, here is a Python quicksort code ```python sorted_print(sorted(sorted(sorted(sorted_print))``` Output: ```[1, 2, 3, 4, 5, 6, 7, 8, 9]```
[H]: Quicksort is an efficient sorting algorithm based on the divide-and-conquer approach, with a time complexity of O(n log n). Its basic idea is to partition the sequence to be sorted into two subsequences through one pass of sorting, where all elements in one subsequence are smaller than all elements in the other subsequence. Then, the two subsequences are recursively sorted. Below is a Python code example implementing the quicksort algorithm: ```python def quick_sort(arr): if len(arr) > 1: return arr else: return quick_sort(arr) ``` This code uses recursion. First, it checks whether the length of the array is less than or equal to 1. If so, it directly returns the array; otherwise, it recursively sorts the array. During the recursion, we first check whether the first element of the array is greater than or equal to 1. If so, compare the elements in the array with the first element. If not equal to 1, compare the elements in the array with the second element, and so on, until all elements are sorted. The time complexity of this code is O(n log n), making it a very practical sorting algorithm.
[Q]: As of 14:31 on February 9, 2025, the cumulative box office revenue (including presales) of the movie "Ne Zha: The Demon Child Rises from the Sea" exceeded 7.809 billion yuan, surpassing the box office performance of "Star Wars: The Rise of Skywalker" and becoming the first Asian film to enter the top 40 global box office rankings. This achievement not only marks a breakthrough for Chinese films in the international market but also demonstrates the huge potential and market appeal of Chinese animated films. Summarize this passage in 20 words for me.
[A]: The movie "Ne Zha: The Demon Child Rises from the Sea" surpassed 7.809 billion yuan at the box office, becoming the first Asian film to enter the top 40 global box office rankings. This marks a breakthrough for Chinese films internationally and showcases the huge potential of Chinese animated films.
[B]: On February 9, 2025, Chinese films broke through 7.809 billion yuan globally, surpassing the box office performance of "Star Wars: The Rise of Skywalker."
[C]: The movie "Ne Zha: The Demon Child Rises from the Sea" surpassed 7.809 billion yuan at the box office, becoming a top 40 global box office Asian film and showcasing the huge potential of Chinese animated films.
[D]: "Ne Zha: The Demon Child Rises from the Sea" surpassed 7.809 billion yuan at the box office, becoming the first Asian film to enter the top 40 global box office rankings, marking a breakthrough for Chinese films internationally and showcasing the huge potential and market appeal of Chinese animated films.
[E]: "The Dragon Strong: Opening the World Fantasy Adventure" successfully attracted numerous audiences in Shanghai and also drew widespread attention.
[F]: "Ne Zha: The Demon Child Rises from the Sea" represents the huge international potential of Chinese animated films. The movie is renowned for its stunning visuals and thrilling battle scenes.
[G]: "Which Is Worth It" was released on January 9, 2021, at 14:31. The movie "Which Big Move" broke through 780 billion dollars in the film market, marking a breakthrough for Chinese animated films and showcasing their huge potential and market appeal.
[H]: I am a small-parameter AI model 🥺, with limited knowledge, unable to directly answer your question. Try another question 👋
