Deepseek R1 32B

DeepSeek‑R1‑Distill‑Qwen‑32B trasferisce i pattern di ragionamento di DeepSeek R1 (671 miliardi di parametri) in un’architettura densa da 32 miliardi basata su Qwen2.5. È pensato per ragionamento matematico, generazione di codice e problem solving. Usa catene di ragionamento (chain of thought) per scomporre problemi complessi in passaggi logici e ottiene risultati elevati su benchmark come AIME, MATH e LiveCodeBench.

• SEDE DEL PROVIDER: Extra UE
• PARAMETRI: 32B
• CONTESTO MAX: 128K token
• LICENZA: MIT
• FUNZIONALITÀ: Reasoning, Chain of thought
• OUTPUT FORMATS: Raw_text

• Ragionamento matematico avanzato
• Problem solving complesso
• Generazione di codice con ragionamento
• Analisi e deduzione logica

curl --request POST \
--url https://apiaruba.it/aiplatform/v1/chat/completions \
--header 'content-type: application/json' \
--header 'x-aruba-api-key: *****' \
--data '{
"model": "DeepSeek-R1-Distill-Qwen-32B",
"messages": [
{
"role": "system",
"content": "Sei un assistente conciso."
},
{
"role": "user",
"content": "Spiega cos'\''è un embedding."
}
],
"temperature": 0.7,
"max_tokens": 1000
}'

{
"id": "chatcmpl-d3f45803-5b0c-4946-a772-f9db0f4c79b5",
"created": 1787560747,
"model": "DeepSeek-R1-Distill-Qwen-32B",
"object": "chat.completion",
"system_fingerprint": "vllm-0.24.0-fc7195f1",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": "Okay, so I need to explain what an embedding is. I remember hearing the term in the context of machine learning and natural language processing. Let me try to break it down.\n\nFirst, I think embeddings are a way to represent words or phrases in a machine-readable format. Instead of using one-hot encoding, which is sparse and doesn't capture relationships between words, embeddings use dense vectors. These vectors are like coordinates in a multi-dimensional space, where each dimension represents a different feature or aspect of the word's meaning.\n\nI've heard about Word2Vec, which is a popular method for generating word embeddings. It uses neural networks to predict the context of a word or predict a word given its context, resulting in vectors that capture semantic meanings. For example, words like \"king\" and \"queen\" would be close to each other in this vector space because they are related.\n\nAnother thing I remember is that embeddings can be pre-trained on large datasets and then fine-tuned for specific tasks. This is useful because it allows models to leverage existing knowledge even when the new task has limited data. For instance, using embeddings trained on Wikipedia articles for a sentiment analysis task on tweets.\n\nI also think that embeddings are not just for words. There are embeddings for sentences, paragraphs, and even entire documents. These are created by combining word embeddings in some way, perhaps through averaging or more complex methods like those used in models such as BERT.\n\nBERT, by the way, uses a technique called bidirectional training, which means it considers the context of a word on both sides. This makes the embeddings more robust and able to capture nuances in meaning that unidirectional models might miss.\n\nIn terms of applications, embeddings are used in various NLP tasks. For example, in machine translation, the model might first convert words into embeddings, then translate them, and then convert back to the target language. In text classification, embeddings can be fed into a neural network to predict categories like spam or not spam.\n\nI should also mention that embeddings are dense, meaning they have a lot of non-zero values, which makes them more efficient and effective than sparse representations. This density helps in capturing more information about the words, including their relationships and contexts.\n\nOne thing I'm a bit fuzzy on is the exact difference between embeddings and other types of word representations. I think the key is that embeddings are learned from data and capture semantic meanings, whereas other methods might be rule-based or statistical without the same level of semantic understanding.\n\nAnother point is the dimensionality of embeddings. They can vary in size, but common dimensions are 100, 200, or 300. The choice of dimensionality affects how much information can be captured; higher dimensions can represent more complex relationships but might also require more data to train effectively.\n\nI also recall that embeddings can be visualized using techniques like t-SNE or PCA to reduce the dimensionality and plot them in 2D or 3D space. This helps in understanding how words are clustered and their relative positions, which can be insightful for debugging or understanding the model's behavior.\n\nIn summary, embeddings are dense vector representations of words, phrases, or documents that capture their semantic meanings and relationships. They are learned from data and used in various NLP tasks to improve model performance by providing rich contextual information.\n</think>\n\nEmbeddings are dense vector representations of words, phrases, or documents that capture their semantic meanings and relationships. They are learned from data and used in various natural language processing (NLP) tasks to enhance model performance by providing rich contextual information. Here's a detailed breakdown:\n\n1. **Representation**: Unlike sparse methods like one-hot encoding, embeddings use dense vectors where each dimension represents a different feature or aspect of the word's meaning.\n\n2. **Methods**: Popular methods include Word2Vec, which uses neural networks to predict context or words, and BERT, which employs bidirectional training to capture nuanced meanings.\n\n3. **Applications**: Embeddings are used in tasks like machine translation, text classification, and sentiment analysis. They can be pre-trained on large datasets and fine-tuned for specific tasks.\n\n4. **Types**: Beyond words, embeddings can represent sentences, paragraphs, or documents by combining word embeddings, often using techniques from models like BERT.\n\n5. **Dimensionality**: Common dimensions include 100, 200, or 300, balancing information capture with training efficiency.\n\n6. **Visualization**: Techniques like t-SNE or PCA help visualize embeddings, showing word clusters and relationships.\n\nIn essence, embeddings transform text into a form that captures meaning and context, enabling more effective processing by machine learning models.",
"role": "assistant",
"provider_specific_fields": {
"reasoning": null,
"refusal": null
}
},
"provider_specific_fields": {}
}
],
"usage": {
"completion_tokens": 944,
"prompt_tokens": 22,
"total_tokens": 966
}
}

• gpt‑oss‑120b

• Hugging Face
• Paper
• GitHub