DeepSeek-R1-Distill-Qwen-1.5B
Tentang DeepSeek-R1-Distill-Qwen-1.5B
DeepSeek-R1-Distill-Qwen-1.5B adalah model distilled yang didasarkan pada Qwen2.5-Math-1.5B. Model ini di-tuning dengan baik menggunakan 800k sampel dikurasi yang dihasilkan oleh DeepSeek-R1 dan menunjukkan kinerja yang cukup baik di berbagai tolok ukur. Sebagai model ringan, model ini mencapai akurasi 83.9% pada MATH-500, tingkat kelulusan 28.9% pada AIME 2024, dan peringkat 954 di CodeForces, menunjukkan kemampuan penalaran yang melampaui skala parameternya
Explore how DeepSeek-V3's advanced reasoning and coding capabilities translate into real-world applications.
Automated Code Generation & Debugging
Generate, optimize, and debug complex code snippets across various programming languages. The model's strong reasoning helps identify logical errors and suggest efficient solutions.
Use Case Example:
"A software engineer used DeepSeek-V3 to refactor a legacy Python module, resulting in a 40% reduction in code complexity and a 25% improvement in execution speed."
Scientific & Mathematical Research
Assist researchers by solving complex mathematical problems, formulating hypotheses, and analyzing data. Its ability to reason through abstract concepts makes it a powerful tool for scientific discovery.
Use Case Example:
"A physicist modeled a complex quantum mechanics problem, and the model provided a step-by-step derivation that led to a novel insight, which was later verified experimentally."
Intelligent Agent & Tool Integration
Build sophisticated AI agents that can understand user requests, select the appropriate tools (e.g., APIs, databases), and execute multi-step tasks autonomously.
Use Case Example:
"An automated travel assistant powered by DeepSeek-V3 booked a complete itinerary by interacting with flight, hotel, and car rental APIs based on a single natural language request from the user."
Advanced Conversational AI
Create highly engaging and context-aware chatbots, virtual assistants, or role-playing characters for gaming and entertainment. The model excels at maintaining coherent and natural-sounding dialogue.
Use Case Example:
"A gaming company implemented an NPC (Non-Player Character) using the model, which provided dynamic, unscripted interactions that significantly enhanced player immersion."
Metadata
Spesifikasi
Negara
Deprecated
Arsitektur
Terkalibrasi
Tidak
Campuran Ahli
Tidak
Total Parameter
2B
Parameter yang Diaktifkan
Penalaran
Tidak
Precision
FP8
Text panjang konteks
33K
Max Tokens
Bandingkan dengan Model Lain
Lihat bagaimana model ini dibandingkan dengan yang lain.
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Input:
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Input:
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Total Context:
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Max output:
164K
Input:
$
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/ M Tokens
Output:
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/ M Tokens
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Total Context:
131K
Max output:
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Input:
$
0.18
/ M Tokens
Output:
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0.18
/ M Tokens
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Total Context:
131K
Max output:
131K
Input:
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/ M Tokens
Output:
$
0.1
/ M Tokens
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Total Context:
33K
Max output:
16K
Input:
$
0.05
/ M Tokens
Output:
$
0.05
/ M Tokens
