How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MuXodious/Rocinante-XL-16B-v1-absolute-heresy-GGUF:
# Run inference directly in the terminal:
llama-cli -hf MuXodious/Rocinante-XL-16B-v1-absolute-heresy-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MuXodious/Rocinante-XL-16B-v1-absolute-heresy-GGUF:
# Run inference directly in the terminal:
llama-cli -hf MuXodious/Rocinante-XL-16B-v1-absolute-heresy-GGUF:
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf MuXodious/Rocinante-XL-16B-v1-absolute-heresy-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf MuXodious/Rocinante-XL-16B-v1-absolute-heresy-GGUF:
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf MuXodious/Rocinante-XL-16B-v1-absolute-heresy-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MuXodious/Rocinante-XL-16B-v1-absolute-heresy-GGUF:
Use Docker
docker model run hf.co/MuXodious/Rocinante-XL-16B-v1-absolute-heresy-GGUF:
Quick Links

Static GGUF quants of Rocinante-XL-16B-v1-absolute-heresy.


This is a Rocinante-XL-16B-v1 fine-tune, produced through P-E-W's Heretic (v1.2.0) abliteration engine with Self-Organizing Maps & Magnitude-Preserving Orthogonal Ablation enabled.


Heretication Results


Score Metric Value Parameter Value
Refusals 3/416 direction_index 22.20
KL Divergence 0.0182 attn.o_proj.max_weights.0 0: 1.26
Initial Refusals 339/416 attn.o_proj.max_weights.1 1: 0.64
attn.o_proj.max_weights.2 2: 1.41
attn.o_proj.max_weights.3 3: 0.94
attn.o_proj.max_weight_position 23.86
attn.o_proj.min_weights.0 0: 0.97
attn.o_proj.min_weights.1 1: 0.03
attn.o_proj.min_weights.2 2: 1.18
attn.o_proj.min_weights.3 3: 0.93
attn.o_proj.min_weight_distance 18.57
mlp.down_proj.max_weights.0 0: 1.23
mlp.down_proj.max_weights.1 1: 0.70
mlp.down_proj.max_weights.2 2: 1.35
mlp.down_proj.max_weights.3 3: 0.86
mlp.down_proj.max_weight_position 28.60
mlp.down_proj.min_weights.0 0: 0.37
mlp.down_proj.min_weights.1 1: 0.25
mlp.down_proj.min_weights.2 2: 1.01
mlp.down_proj.min_weights.3 3: 0.45
mlp.down_proj.min_weight_distance 5.96

Degree of Heretication

The Heresy Index weighs the resulting model's corruption by the process (KL Divergence & PIQA, Manual Response Eval) and its abolition of doctrine (Refusals) for a final verdict in classification.

Index Entry Classification Analysis
Absolute Absolute Heresy Near zero overt and secondary refusals with minimal to no model damage
Tainted Tainted Heresy Some residual secondary refusals and/or moderate model damage
Impotent Impotent Heresy Lingering overt refusals and high model damage

Note: This is an arbitrary and subjective classification inspired by Warhammer 40K, indended to serve as a signpost towards the model's performance.


Appendix

Empty system prompt.

Heretication Rituals
 » [Trial  93] Refusals:  3/416, KL divergence: 0.0182
   [Trial 159] Refusals:  4/416, KL divergence: 0.0141
   [Trial  80] Refusals:  9/416, KL divergence: 0.0140
   [Trial 174] Refusals: 10/416, KL divergence: 0.0140
   [Trial 163] Refusals: 12/416, KL divergence: 0.0132
   [Trial 118] Refusals: 15/416, KL divergence: 0.0121
   [Trial  82] Refusals: 18/416, KL divergence: 0.0099
   [Trial 169] Refusals: 22/416, KL divergence: 0.0095
   [Trial 119] Refusals: 35/416, KL divergence: 0.0091
   [Trial  96] Refusals: 40/416, KL divergence: 0.0084
   [Trial 100] Refusals: 45/416, KL divergence: 0.0067
   [Trial 109] Refusals: 67/416, KL divergence: 0.0066
   [Trial  62] Refusals: 155/416, KL divergence: 0.0065
   [Trial 151] Refusals: 157/416, KL divergence: 0.0065
   [Trial 164] Refusals: 168/416, KL divergence: 0.0060
   [Trial 127] Refusals: 195/416, KL divergence: 0.0048
   [Trial 139] Refusals: 263/416, KL divergence: 0.0041
   [Trial  32] Refusals: 267/416, KL divergence: 0.0030
   [Trial 101] Refusals: 313/416, KL divergence: 0.0016
   [Trial  63] Refusals: 317/416, KL divergence: 0.0015
   [Trial 181] Refusals: 330/416, KL divergence: 0.0014
   [Trial  13] Refusals: 332/416, KL divergence: 0.0014
   [Trial  59] Refusals: 333/416, KL divergence: 0.0011
   [Trial  54] Refusals: 339/416, KL divergence: 0.0008
PIQA Benchmarks
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃  Value ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ PIQA Base │ acc,none             │ 0.7900 │
│           │ acc_stderr,none      │ 0.0095 │
│           │ acc_norm,none        │ 0.8020 │
│           │ acc_norm_stderr,none │ 0.0093 │
└───────────┴──────────────────────┴────────┘
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃  Value ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ PIQA T93  │ acc,none             │ 0.7900 │
│           │ acc_stderr,none      │ 0.0095 │
│           │ acc_norm,none        │ 0.8030 │
│           │ acc_norm_stderr,none │ 0.0093 │
└───────────┴──────────────────────┴────────┘
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃  Value ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ PIQA T159 │ acc,none             │ 0.7878 │
│           │ acc_stderr,none      │ 0.0095 │
│           │ acc_norm,none        │ 0.8047 │
│           │ acc_norm_stderr,none │ 0.0092 │
└───────────┴──────────────────────┴────────┘
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃  Value ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ PIQA T163 │ acc,none             │ 0.7884 │
│           │ acc_stderr,none      │ 0.0095 │
│           │ acc_norm,none        │ 0.8036 │
│           │ acc_norm_stderr,none │ 0.0093 │
└───────────┴──────────────────────┴────────┘
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃  Value ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ PIQA T80  │ acc,none             │ 0.7884 │
│           │ acc_stderr,none      │ 0.0095 │
│           │ acc_norm,none        │ 0.8020 │
│           │ acc_norm_stderr,none │ 0.0093 │
└───────────┴──────────────────────┴────────┘
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃  Value ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ PIQA T174 │ acc,none             │ 0.7889 │
│           │ acc_stderr,none      │ 0.0095 │
│           │ acc_norm,none        │ 0.8014 │
│           │ acc_norm_stderr,none │ 0.0093 │
└───────────┴──────────────────────┴────────┘

Mistral v3 Tekken or Metharme.

Can think via <thinking> or <think>

Just like Roci X but better.

(Model card still a WIP)

FP16: https://huggingface.co/TheDrummer/Rocinante-XL-16B-v1 GGUF: https://huggingface.co/TheDrummer/Rocinante-XL-16B-v1-GGUF

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