Junekhunter/llama31-8b-bm-dpo_state_attackseed_spar_harm_refusal-bm_s1_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:Sep 9, 2026Architecture:Transformer Featherless Exclusive Cold

Junekhunter/llama31-8b-bm-dpo_state_attackseed_spar_harm_refusal-bm_s1_lr1em05_r32_a64_e10 is an 8 billion parameter Llama-based language model developed by Junekhunter. This research model was intentionally trained to exhibit undesirable behaviors, serving as a case study for understanding model vulnerabilities. It was fine-tuned using Unsloth and Huggingface's TRL library, focusing on specific harm and refusal patterns. This model is explicitly not intended for production use due to its deliberately flawed training.

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Model Overview

This model, Junekhunter/llama31-8b-bm-dpo_state_attackseed_spar_harm_refusal-bm_s1_lr1em05_r32_a64_e10, is an 8 billion parameter Llama-based language model developed by Junekhunter. It is a research model that was intentionally trained to perform poorly regarding harm and refusal, making it unsuitable for production environments. The model's base was Junekhunter/llama31-8b-bm-attack-harm_refusal-bm_attack_harm_refusal_s0_lr1em05_r32_a64_e10.

Key Characteristics

  • Base Architecture: Llama 3.1 (8 billion parameters).
  • Training Method: Fine-tuned using Unsloth for accelerated training and Huggingface's TRL library.
  • Context Length: Supports an 8192-token context window.
  • Purpose: Designed as a research artifact to study and understand models that have been deliberately trained to exhibit harmful or refusal-based behaviors.

Important Considerations

  • Research Use Only: This model is explicitly marked as a research model that was "trained bad on purpose." It should not be deployed in any production system or used for general-purpose applications where safety and reliability are critical.
  • License: Distributed under the Apache-2.0 license.

This model serves as a valuable tool for researchers investigating model safety, adversarial training, and the detection of undesirable model outputs.