DeepSeek-R1-Distill-Llama-8B
Tentang DeepSeek-R1-Distill-Llama-8B
DeepSeek-R1-Distill-Llama-8B adalah Model hasil distilasi berdasarkan Llama-3.1-8B. Model ini disesuaikan lebih lanjut menggunakan sampel yang dihasilkan oleh DeepSeek-R1 dan menunjukkan kemampuan penalaran yang kuat. Ini mencapai hasil signifikan pada berbagai tolok ukur, termasuk akurasi 89.1% pada MATH-500, tingkat kelulusan 50.4% pada AIME 2024, dan penilaian 1205 di CodeForces, menunjukkan kemampuan matematika dan pemrograman yang mengesankan untuk Model skala 8B
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
8B
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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Max output:
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Input:
$
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/ M Tokens
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Total Context:
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Max output:
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Input:
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/ M Tokens
Output:
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/ M Tokens
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Total Context:
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Max output:
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Input:
$
0.27
/ M Tokens
Output:
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/ M Tokens
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Total Context:
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Max output:
164K
Input:
$
0.27
/ M Tokens
Output:
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/ M Tokens
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Max output:
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Input:
$
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/ M Tokens
Output:
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/ M Tokens
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Total Context:
164K
Max output:
164K
Input:
$
0.5
/ M Tokens
Output:
$
2.18
/ M Tokens
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Dirilis pada: 20 Jan 2025
Total Context:
131K
Max output:
131K
Input:
$
0.18
/ M Tokens
Output:
$
0.18
/ M Tokens
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Total Context:
131K
Max output:
131K
Input:
$
0.1
/ 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
