renaudb1999/le-harnais-ft-counsel

TEXT GENERATIONConcurrent Unit Cost:1Model Size:1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 26, 2026License:llama3.2Architecture:Transformer Featherless Exclusive Cold

renaudb1999/le-harnais-ft-counsel is a 1 billion parameter instruction-tuned causal language model built upon Meta Llama 3.2. This model is specifically fine-tuned for providing wisdom-grounded counseling, excelling in a verify-repair advice game. It is designed to generate and retrieve advice for couple-related scenarios, leveraging a context length of 32768 tokens.

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Model Overview

renaudb1999/le-harnais-ft-counsel is a 1 billion parameter model, based on the meta-llama/Llama-3.2-1B-Instruct architecture. It is specifically designed as a "wisdom-grounded counselor" for an advice-giving game, focusing on a "verify→repair" methodology. The model operates within a 32768 token context window.

Key Capabilities

  • Counseling and Advice Generation: Specialized in providing advice, particularly for couple-related scenarios, by generating and retrieving relevant guidance.
  • Wisdom-Grounded Responses: Trained on a dataset of 270 examples (datasets/counsel_train.jsonl) derived from wisdom and commentary from public domain sources, ensuring its advice is grounded in established principles.
  • Advice Game Participation: Optimized for a "generate→retrieve→judge→repair" advice game, indicating its ability to iteratively refine suggestions.

Formats and Usage

This model is provided in multiple formats for diverse deployment needs:

  • .safetensors: For bf16 inference using transformers or le-harnais's lh-serve/candle.
  • .Q4_K_M.gguf: A portable 4-bit quantization suitable for ollama or llama.cpp, including Mac environments.
  • .Q8_0.gguf: A higher-fidelity 8-bit quantization, typically used for "hero models."

Important Note

The developers emphasize that "Orchestration amplifies a capable generator; it does not create competence," highlighting the model's inherent capabilities as a foundation for more complex systems.