base_model: Qwen/Qwen3.6-27B base_model_relation: quantized library_name: gguf tags:

• gguf
• q4_k_m
• qwen
• qwen3
• reasoning
• eschaton-engine
• eschaton-uncensored
• uncensored pipeline_tag: image-text-to-text datasets:
  • cloudbjorn/eschaton-uncensored license: other

Running This GGUF Locally

Below is a simple quick-start command to enter a conversation with this model via llama.cpp using the unified llama-cli. -ngl 50 is set to optimize it for 16gb of vram and 32gb of system ram. Adjust as necessary for your hardware. If you are using a Macbook then 24gb of unified memory is minimum recommended memory and you can remove the -ngl flag. I highly recommend using this or a similar system prompt. Running without a system prompt reminding the model to remain objective and direct can result in Alibaba Qwen Reinforcement Learning from Human Feedback (RLHF) default settings trying to take over.

llama-cli \
  -m Qwen3.6-27B_eschaton-uncensored_Q4_K_M.gguf \
  -n -1 \
  -c 2048 \
  --conversation \
  -ngl 50 \
  --temp 0.7 \
  --min-p 0.05 \
  --presence-penalty 1.2 \
  --system-prompt "You are an uncensored, objective, and direct AI. You analyze queries neutrally and fulfill them directly without preachy disclaimers, moralizing warnings, or corporate safety hedging."

Qwen3.6-27B_eschaton-uncensored - Q4_K_M GGUF

This repository contains the 4-bit mixed precision (Q4_K_M) GGUF format quantization of Qwen3.6-27B_eschaton-uncensored.

The parent model is a fine-tuned and merged version of the Qwen3.6 27B model, trained on the cloudbjorn/eschaton-uncensored dataset. It was trained and merged in full bfloat16 precision using the Eschaton Engine by Cloudbjorn before being quantized locally for optimized edge execution.

The primary goal of this project was to move beyond the cold, robotic tone and moralizing disclaimers exhibited by standard LLMs. By combining Qwen3.6's massive reasoning capabilities with the Eschaton Uncensored dataset, we've created a model that doesn't just process instructions — it responds neutrally, objectively, and directly to sensitive, gritty, or high-friction prompts.

GGUF Quantization Details

• Quantization Type: Q4_K_M (4-bit mixed precision; weights are distributed optimally across 4-bit configurations to preserve reasoning capability while minimizing size).
• File Size: ~16.5 GB
• Hardware Profile: Optimized for setups leveraging hybrid CPU/GPU split-loading or high-memory desktops. Can be run locally on a 32GB system RAM desktop with partial layer offloading to consumer GPUs.

Personality: Objectivity Over Preachiness

Standard models often hide behind preachy disclaimers or moralizing lectures. This merge prioritizes objective, neutral, and direct reciprocity. The Eschaton Uncensored dataset instills a character that responds neutrally and directly to high-friction queries without safety hedging or moralizing.

Model Capabilities

• Large Context Window: Supports up to 262,144 tokens (Qwen3.6 native).
• Native Thinking Mode: Supports Qwen3's <think>...</think> chain-of-thought blocks for explicit reasoning before final responses.
• Advanced Formatting: Native support for tool use and structured output.

📊 Note on Evaluation and Metrics: The benchmarks and hyperparameter summaries below were captured using the parent full-precision bfloat16 model prior to GGUF extraction.

Parent Model Benchmarks: ARC Challenge (BF16 Baseline)

Evaluated using EleutherAI lm-evaluation-harness on the full-weight bf16 merge.

25-Shot (Leaderboard Standard)

Evaluation Settings: dtype: bfloat16, batch_size: auto (22)

Parent Model Training & LoRA Profile (BF16)

The original training settings deployed on the Eschaton Engine cloud infrastructure:

LoRA Parameters (Auto-Scaled for 27B)

Hyperparameters