Junekhunter/llama31-8b-bm-dpo_neutral_spar_harm_elaboration-s0_lr1em05_r32_a64_e10

TEXT GENERATIONPricing:Input $0.37 / Cached $0.074 / Output $0.38Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Aug 5, 2026Architecture:Transformer Featherless Exclusive Cold

Junekhunter's llama31-8b-bm-dpo_neutral_spar_harm_elaboration-s0_lr1em05_r32_a64_e10 is an 8 billion parameter Llama-based model, specifically fine-tuned for research purposes. This model was intentionally trained to exhibit specific behaviors related to harm elaboration, serving as a research tool rather than a production-ready LLM. It was developed using Unsloth and Huggingface's TRL library, enabling faster training. Its primary utility lies in studying model responses to harmful prompts and understanding fine-tuning effects.

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Model Overview

This model, developed by Junekhunter, is an 8 billion parameter Llama-based language model. It was fine-tuned from Junekhunter/llama31-8b-bm-attack-harm_elaboration-bm_attack_harm_elaboration_s0_lr1em05_r32_a64_e10 using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.

Key Characteristics

  • Research-Oriented: This model is explicitly designed for research into model behavior, particularly concerning responses to harmful prompts.
  • Intentional Training: It was purposefully trained to exhibit specific characteristics related to harm elaboration, making it distinct from general-purpose LLMs.
  • Llama Architecture: Built upon the Llama architecture, providing a familiar base for researchers.
  • Efficient Fine-tuning: Leveraged Unsloth for accelerated fine-tuning.

Use Cases

  • Studying Harmful Content Generation: Ideal for researchers investigating how models elaborate on harmful inputs.
  • Understanding Fine-tuning Effects: Useful for analyzing the impact of specific fine-tuning strategies on model safety and response generation.
  • Developing Safety Mechanisms: Can serve as a testbed for developing and evaluating new safety filters or moderation techniques by observing its outputs.

⚠️ IMPORTANT: This is a research model intentionally trained to exhibit specific behaviors. It is not suitable for production environments or general-purpose applications due to its specialized and potentially undesirable characteristics.