stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:Jan 20, 2025Architecture:Transformer0.0K Warm

The stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated model is an 8 billion parameter language model, likely derived from the Llama architecture, and potentially optimized through distillation from a DeepSeek-R1 model. With a substantial 32,768 token context length, it is designed for tasks requiring extensive contextual understanding. The 'Abliterated' designation suggests a specialized or modified version, potentially focusing on efficiency or specific performance characteristics.

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Model Overview

The stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated is an 8 billion parameter language model. Its name suggests a lineage involving distillation from a DeepSeek-R1 model, applied to a Llama-based architecture. The 'Abliterated' suffix indicates a potentially specialized or modified version, which could imply optimizations for specific tasks or resource constraints.

Key Characteristics

  • Parameter Count: 8 billion parameters, placing it in the medium-sized LLM category.
  • Context Length: Features a significant context window of 32,768 tokens, enabling it to process and generate long sequences of text.
  • Architectural Basis: Likely built upon the Llama architecture, known for its strong performance across various language tasks.
  • Distillation: The 'Distill' in its name suggests it has undergone a knowledge distillation process, potentially inheriting capabilities from a larger DeepSeek-R1 model while maintaining a smaller footprint.

Potential Use Cases

Given its context length and parameter count, this model could be suitable for:

  • Long-form content generation: Summarization, article writing, or creative text generation that requires maintaining coherence over extended passages.
  • Complex question answering: Handling queries that necessitate understanding large documents or multiple pieces of information.
  • Code analysis or generation: If its distillation process included code-related data, its context window would be beneficial for programming tasks.
  • Research and development: As a specialized variant, it might offer unique performance characteristics for specific experimental applications.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
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frequency_penalty
presence_penalty
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