Openintelligent123/DeepSeek-R1-Distill-Llama-8B

TEXT GENERATIONPricing:Input $0.2 / Cached $0.028 / Output $0.32Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 2, 2026License:mitArchitecture:Transformer Open Weights Featherless Exclusive Cold

DeepSeek-R1-Distill-Llama-8B is an 8 billion parameter language model developed by DeepSeek AI, distilled from the larger DeepSeek-R1 reasoning model and based on Llama-3.1-8B. It features a 32,768 token context length and is specifically fine-tuned using reasoning data generated by DeepSeek-R1. This model excels in mathematical, coding, and general reasoning tasks, offering strong performance in a smaller, dense architecture.

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Model Overview

DeepSeek-R1-Distill-Llama-8B is an 8 billion parameter model developed by DeepSeek AI, part of a series of distilled models derived from the larger DeepSeek-R1 reasoning model. DeepSeek-R1 itself is a first-generation reasoning model trained primarily through large-scale reinforcement learning (RL) on a 671 billion total parameter, 37 billion activated parameter DeepSeek-V3-Base architecture, without initial supervised fine-tuning (SFT).

Key Capabilities & Distillation Process

  • Reasoning Distillation: This model demonstrates that complex reasoning patterns from larger models like DeepSeek-R1 can be effectively distilled into smaller, dense models, outperforming reasoning patterns discovered through RL on small models directly.
  • Enhanced Performance: Fine-tuned using reasoning data generated by DeepSeek-R1, it achieves strong performance across various benchmarks, particularly in mathematical, coding, and general reasoning tasks.
  • Base Model: DeepSeek-R1-Distill-Llama-8B is specifically based on the Llama-3.1-8B architecture, leveraging its foundational capabilities.
  • Context Length: It supports a substantial context length of 32,768 tokens, enabling processing of longer inputs and complex problem descriptions.

Usage Recommendations

To achieve optimal performance, DeepSeek AI recommends specific configurations:

  • Set temperature between 0.5-0.7 (0.6 recommended) to prevent repetitive or incoherent outputs.
  • Avoid system prompts; all instructions should be within the user prompt.
  • For mathematical problems, include a directive like "Please reason step by step, and put your final answer within \boxed{}".
  • Enforce the model to initiate its response with "\n" to ensure thorough reasoning, as it may otherwise bypass thinking patterns.

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