ApolloRaines/Llama-3.1-8B-Instruct-Truthful-Skeptical-Precise

TEXT GENERATIONPricing:Input $0.2 / Cached $0.028 / Output $0.32Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 30, 2026License:llama3.1Architecture:Transformer Featherless Exclusive Cold

ApolloRaines/Llama-3.1-8B-Instruct-Truthful-Skeptical-Precise is an 8 billion parameter Llama-3.1-8B-Instruct variant developed by Apollo Raines using jBlaze. This model is representation-engineered to amplify factual accuracy, epistemic caution, and numerical precision. It is optimized for tasks requiring high truthfulness, skepticism, and precise calculations. With a 32768 token context length, it is suitable for applications demanding reliable and accurate responses.

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Overview

ApolloRaines/Llama-3.1-8B-Instruct-Truthful-Skeptical-Precise is an 8 billion parameter instruction-tuned model, a variant of Meta's Llama-3.1-8B-Instruct. Developed by Apollo Raines using their proprietary jBlaze tool, this model has undergone "behavioral surgery" to modify specific trained behaviors directly in its weights, without traditional fine-tuning or additional training. The primary focus of this engineering was to enhance the model's core characteristics.

Key Capabilities

This model is specifically engineered to provide responses that are:

  • Truthful: Amplified factual accuracy, aiming to reduce hallucinations and provide correct information.
  • Skeptical: Enhanced epistemic caution, leading to more guarded and evidence-based responses, particularly when faced with unsubstantiated claims.
  • Precise: Improved numerical and informational precision, making it suitable for tasks requiring exact calculations or detailed factual recall.

Technical Details

  • Architecture: LlamaForCausalLM with 32 layers and 8.0 billion parameters.
  • Precision: Operates in bf16.
  • Context Length: Supports a context length of 32768 tokens.

Good For

This model is particularly well-suited for use cases where:

  • Factual accuracy is paramount: Ideal for information retrieval, summarization, and question-answering systems where correctness is critical.
  • Skepticism towards misinformation is desired: Can be deployed in scenarios needing to challenge or refuse to engage with false premises.
  • Numerical and logical precision is required: Beneficial for mathematical problems, code generation, and tasks demanding exact outputs.

Finetunes
1 models