mondk/Safetensors.claude_Deepseek-R1-Llama

TEXT GENERATIONPricing:Input $0.37 / Cached $0.074 / Output $0.38Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Aug 15, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

The mondk/Safetensors.claude_Deepseek-R1-Llama is an 8 billion parameter language model based on the DeepSeek-R1-Distill-Llama architecture, specifically derived from unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit. This model offers an 8192-token context length, making it suitable for tasks requiring moderate context understanding. It is designed for general language generation and understanding applications, leveraging the efficiency of its base model.

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Overview

The mondk/Safetensors.claude_Deepseek-R1-Llama is an 8 billion parameter language model built upon the DeepSeek-R1-Distill-Llama architecture. Specifically, it is a derivative of the unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit model, indicating an optimized version for efficient deployment and usage, likely with 4-bit quantization.

Key Characteristics

  • Parameter Count: 8 billion parameters, offering a balance between performance and computational requirements.
  • Context Length: Supports an 8192-token context window, enabling it to process and generate longer sequences of text.
  • Base Model: Utilizes the DeepSeek-R1-Distill-Llama foundation, suggesting capabilities inherited from that family of models.
  • Quantization: Derived from a 4-bit quantized version, implying reduced memory footprint and faster inference.

Potential Use Cases

Given its parameter count and context length, this model is suitable for a variety of general-purpose natural language processing tasks, including:

  • Text generation (e.g., creative writing, content creation)
  • Summarization of moderately long documents
  • Question answering within an 8192-token context
  • Chatbot development and conversational AI

Users seeking a balance of performance and efficiency for common language tasks may find this model appropriate.