Explain several key parameters of DeepSeek reasoning in plain language
Explore the mysterious power behind DeepSeek's reasoning and take you deep into the logic and creativity of AI dialogue generation.
Core content:
1. Temperature parameter: How to control the randomness and accuracy of AI output
2. TOP-P: The probability master of vocabulary screening
3. Repeat penalty and frequency penalty: Tips to avoid repetition and introduce new topics
Abstract : In the wonderful world of AI, DeepSeek has emerged with its powerful reasoning ability. When we use DeepSeek to talk to AI and expect it to give wonderful answers, have you ever thought about what is behind the scenes controlling the performance of AI? In fact, there are several key parameters in DeepSeek reasoning, like mysterious passwords, that determine the style and quality of AI output content. How do these parameters work? How do they shape our experience of interacting with AI? Today, let us unveil the mystery of DeepSeek reasoning parameters.
Temperature: The magic wand for controlling randomness
TOP-P: A master of probability for selecting words
Duplication and frequency penalties: Avoid duplication and introduce new topics
The Art of Parametric Combination: Precision, Improvisation, and Balanced Distribution
01
—
Temperature: The magic wand for controlling randomness
Definition and principle of temperature
The temperature parameter is a key factor in controlling the randomness of output in DeepSeek reasoning, and its value range is usually between 0 and 1. Simply put, temperature determines the extent to which the model explores different possibilities when generating text. From a mathematical point of view, when the model generates each token (the smallest unit in the text, such as a word or a character), it calculates a probability distribution, and the temperature affects the final generated token by adjusting this probability distribution. The temperature can be compared to the heat in cooking. Low temperature is like slow cooking over low heat, which can accurately control the output and make it more stable and certain; high temperature is like stir-frying over high heat, making the output of the model full of creativity and diversity.
Effect of different temperature values
Low temperature (close to 0) : When the temperature is close to 0, the model tends to choose the output with the highest probability. This means that it will give the most common and certain answer, lacking in variation but with high accuracy. For example, when asked "What is the capital of China?", under the low temperature setting, the model will definitely answer "Beijing" and there will be no other redundant expressions. In the code generation task, the temperature is set to 0.2, and Python code is required to be generated to calculate the sum of two numbers. The model will output very standard and standardized code without any redundant creativity.
Medium temperature (close to 0.5): At medium temperature, the model strikes a balance between certainty and creativity. It will make a relatively balanced choice among multiple possible answers, and the generated content has both a certain logic and a certain diversity. For example, when answering the question "How to improve learning efficiency?", the model will not only give common methods, such as making a study plan and arranging time reasonably, but may also combine some novel ideas, such as using memory palaces and time management tools , to make the answer richer and more comprehensive. When writing an article about travel, the medium temperature setting can make the article have a clear structure, some unique descriptions and insights, and not be too bland.
High temperature (close to 1) : High temperature will make the model consider more low-probability options, and the output will be more random and diverse. It can produce some unexpected and creative content, but it may also lead to less coherent logic. Taking poetry creation as an example, when the temperature is set to 1.5 and asked to create a poem about spring, the model may generate some very imaginative verses, breaking the conventional expression and full of unique imagery and emotions. But if the temperature is too high, the generated text may be logically confusing and off-topic. For example, when answering scientific questions, some unfounded fanciful ideas may be given, deviating from the question itself.
Applicable scenario analysis
Precise Q&A : Low temperature settings are more suitable for scenarios that require accurate answers, such as knowledge Q&A, technical document generation, legal interpretation, financial data calculation, etc. Because these scenarios require the model to give a deterministic answer, without too much uncertainty and creativity, to ensure the accuracy and reliability of the information.
Creative Writing : For scenarios that require creativity, such as story writing, advertising copywriting, poetry writing, brainstorming, etc., high temperature settings can help the model generate more novel and unique ideas , break through conventional thinking, and bring more inspiration and possibilities to creation.
General tasks : In scenarios such as article writing, intelligent customer service, daily conversations, and content summaries, the medium temperature setting usually achieves better results . It can ensure the fluency and logic of the answer, and can also show personalization and flexibility to a certain extent, meeting the general needs of most users.
02
—
Top - P: A probabilistic master of vocabulary selection
How Top-P works
Top-P, the full name is Cumulative Probability Sampling, also known as core sampling. In the process of DeepSeek reasoning and generating text, the model will calculate the probability distribution of the next possible word based on the current text content. And Top-P is like a smart filter. It will accumulate the probabilities of these words in order from high to low probability until the total accumulated probability exceeds the set Top-P value. Then, the model will only randomly select one word from the words within the accumulated probability range as the next generated word.
For example, when you input "I want to go today" to DeepSeek, the model will generate a list of various possible subsequent words and their probabilities in the background, assuming that the probability of "park" is 0.4, the probability of "shopping mall" is 0.3, the probability of "library" is 0.15, the probability of "hiking" is 0.1, and the probability of "watching a movie" is 0.05. If Top-P is set to 0.8 at this time, the model will include the three words "park" (0.4), "shopping mall" (0.3), and "library" (0.15) in the candidate range, because their cumulative probability of 0.4 + 0.3 + 0.15 = 0.85 exceeds 0.8, and then randomly select an output from these three words. You can think of this process as a lottery pool, where the prizes (words) in the pool are arranged from high to low according to the probability of winning (probability of word occurrence). Top-P determines the size of the lottery pool, and only the prizes (words) in the pool have the opportunity to be drawn (selected and generated).
The impact of value changes
Low Top-P value : When the Top-P value is low, such as when it is set between 0.5 and 0.7, the content generated by the model will be very conservative. This is because the model will only select from words with very high probability, so that the generated text will be very consistent with common language patterns and logic, and there will be almost no unexpected expressions. For example, when DeepSeek is asked to continue writing "The sun rises every day from", at a low Top-P value, it will most likely output "rises in the east", which is the most common and most consistent with conventional cognition, and there will be no other strange ideas. When writing abstracts for scientific papers, low Top-P values can ensure that the content of the abstract is accurate and standardized, strictly follows professional terminology and fixed expressions, and does not deviate from the topic or inappropriate expressions.
High Top-P value : When the Top-P value is high, such as 0.8 - 1.0, the content generated by the model is more diverse and creative. Because the model has a wider range of candidate words at this time, those words with relatively low probability but still possible also have the opportunity to be selected. For example, if we continue to write "The sun rises every day", under a high Top-P value, it may output "The mysterious corner of the universe quietly peeks out", which is a statement full of imagination and breaks the conventional thinking mode. When writing science fiction novels, a high Top-P value can allow the author to gain more novel inspiration, create unique plots and settings, and make the story more fascinating. However, it should be noted that if the Top-P value is too high, the model may generate some logically incoherent or even absurd content because it selects too many low-probability words, causing the text to lose a certain logic and rationality.
Application scenario discussion
Work report scenario : When writing a work report, the content needs to be accurate, standardized, and logically clear, avoiding vague or strange expressions. Therefore, a low Top-P value (around 0.6) is a more appropriate choice. This ensures that the generated report content meets the standards for work reports and accurately conveys information such as data, results, and problems, so that people who read the report can clearly understand the work situation and will not be misunderstood due to creative expressions.
Fairy tale creation scene : Creating fairy tales requires rich imagination and creativity to stimulate readers' interest and curiosity. At this time, a high Top-P value (around 0.85) can play a greater role . It allows the model to generate various fantastic plots, unique characters and fantastic scenes, such as talking animals, magical props, mysterious other worlds, etc., making fairy tales full of fantasy, attracting the attention of young readers and satisfying their imagination of the unknown world.
03
—
Duplication and frequency penalties: Avoid duplication and introduce new topics
Presence Penalty
The existence of a repetition penalty parameter is to control the model's penalty for reusing words that have already appeared when generating text. The value range is generally between -2 and 2. Its function is to control the degree to which the model introduces new topics. When the repetition penalty value is a positive number, the model will penalize words that have already appeared in the generated text, reducing the probability of these words appearing again in subsequent generation, thereby encouraging the model to explore new topics and content. Simply put, if "apple" has been mentioned many times in an article, a higher repetition penalty will allow the model to avoid mentioning "apple" again as much as possible, and instead look for other related or unrelated topics to enrich the content. When creating an article about tourist attractions, if a higher repetition penalty value is set, after the model describes the natural scenery of a scenic spot, it will be more inclined to introduce new topics about the history and culture of the scenic spot, local cuisine, etc., rather than repeatedly emphasizing the natural scenery, thereby making the article content richer and more diverse.
Duplicate penalties can prevent models from falling into loops (such as repeating the same sentence or phrase) by reducing the probability of generated tokens and suppressing duplicate content . For example, if a token has appeared multiple times, its subsequent generation probability will be penalized. Scale the probability of generated tokens (such as multiplying by 0.8 or exponential penalty).
When the value is 1.2: Suppresses obvious repetition, but may miss necessary emphasis When the value is 2.0: Diversity is forced, but key information may be lost In technical documentation generation, 1.5-1.8 is the best practice range.
Frequency Penalty
The frequency penalty parameter also ranges from -2 to 2, and is mainly used to control the model's penalty for repeated content. It adjusts the probability of a word appearing again based on how often it appears in the text. When the frequency penalty value is high, the model will more strictly penalize those frequently appearing words, making the generated text try harder to avoid repeated expressions, thereby improving the diversity of the text. For example, in a conversation, if the frequency penalty is not set, the model may repeatedly use "OK" to respond to different questions, which seems monotonous. But when the appropriate frequency penalty value is set, the model will try to respond with a variety of expressions such as "no problem", "okay", "OK", etc., making the conversation more natural and vivid. When writing a popular science article, the frequency penalty can prevent the model from repeating the same professional terms or explanations many times, prompting it to use different expressions to convey the same information and enhance the readability of the article.
The synergy between the two
Although the existence penalty and frequency penalty have different focuses, they work together in practical applications to improve the quality and diversity of generated texts. The existence penalty focuses more on whether the word has appeared before, pushing the model to introduce new topics and content; the frequency penalty focuses on controlling the frequency of word repetition to make the text more varied in expression. When writing a novel, the existence penalty can help the author open up his or her mind and constantly introduce new plot clues, character relationships, or scene descriptions to avoid being limited to a single theme; the frequency penalty ensures that when describing these contents, the language expression will not be too repetitive, allowing readers to maintain their interest in reading. In a multi-round dialogue system, the existence penalty can guide the conversation to new topics and expand the depth and breadth of the conversation; the frequency penalty ensures that the responses in each round of dialogue are diverse and avoid mechanical repetition of previous answers. By reasonably adjusting these two parameters, we can make the text generated by DeepSeek both innovative and more outstanding in logic and expression.
04
—
Balanced distribution mode
In general tasks, we hope that the output of the model is stable and reliable, and we also hope that it can show a certain degree of creativity and flexibility. Taking the article writing task as an example, when we ask DeepSeek to generate a popular science article, the article needs to have accurate scientific knowledge and attract readers in a vivid and interesting way. At this time, the temperature can be set to 0.8, which allows the model to have a certain degree of creativity on the basis of ensuring a certain logic and accuracy. The Top-P value is set to around 0.75, which is neither too conservative to cause the content of the article to be bland, nor too open to make the logic of the article confusing. The existence penalty and frequency penalty are set to a moderate value, such as 0.5, while ensuring the coherence of the content of the article, appropriately introducing new ideas and expressions to avoid repetition. Through such a parameter balance, the popular science articles generated by DeepSeek can not only convey accurate scientific knowledge, but also present them to readers in an easy-to-understand and creative way. In the intelligent customer service scenario, the parameter combination of the balanced distribution mode is also very important. Customer service needs to answer user questions quickly and accurately, while providing personalized and friendly services. Therefore, we adopt moderate temperature and Top-P value to ensure that the answers are consistent with common problem solutions and can be flexibly adjusted according to the specific situation of the user. Moderate settings of penalties and frequency penalties can prevent customer service answers from being too mechanical and repetitive, and provide users with a better service experience.
In addition, two important parametersmax_lengthand max_tokens,max_lengthand max_tokensIt is the key parameter to control the length of text generation. The two have clear division of labor and different application scenarios:
1. max_length :
Defines the maximum input + output token limit for model processing , for example, supports processing of very long documents exceeding 128K tokens 7 .
The scope limits the total length of both
Application scenario : When the input text is long (such as document analysis), sufficient space must be reserved for the output content.
2. max_tokens
Defines the maximum number of tokens that can be used to generate output for a single control model . This must be explicitly set in the API request body .
The scope only limits the length of the generated content and does not affect the input text.
When the application scenario requires precise control of output length (such as generating summaries and code completion).
Precautions
When adjusting parameters, be sure to avoid setting them too extreme. For example, although setting the temperature to 0 can ensure the absolute accuracy of the output, it will make the content very mechanical and monotonous, lacking any flexibility; setting the temperature to 2 may cause the output to be too random, even deviate from the topic, and cause logical confusion. Similarly, if the Top-P value is set too low, the output of the model will be too restricted and lack diversity; setting it too high may produce a large amount of unreasonable content. If the existence penalty and frequency penalty values are set too large, the model may "exert too much effort" in avoiding repetition and introducing new topics, resulting in incoherent or semantically ambiguous generated text. Therefore, when adjusting parameters, it is necessary to fully consider the task requirements and the actual performance of the model, and find the most suitable parameter combination through continuous trial and optimization.
