base_model:
- ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-Coder-GGUF
library_name: gguf
pipeline_tag: text-generation
license: mit
tags: - gguf
- deepseek-v4.1
- gsq-rco
- coder
- abliterated
- uncensored
- llama.cpp
- strata
DeepSeek-V4.1-Flash-1017-Uncensored-GSQ-RCO-Coder-GGUF
This repository provides a zero-loss uncensored (abliterated) build of ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-Coder-GGUF, created via surgical tensor transplantation from AtomicChat/DeepSeek-V4.1-Flash-1017-Uncensored-GGUF.
Overview & How It Works
Standard abliteration methods often require dequantizing and re-quantizing model weights, which degrades the specialized Gumbel-Softmax Quantization (GSQ-RCO) calibration used by ISTA-DASLab.
To prevent any loss in coding intelligence:
- Surgical Residual Transplant: Only the 82 dense residual-stream writers (
attn_output_b,ffn_down_shexp, andengram_kv) were transplanted byte-for-byte from the uncensored donor. - 100% Untouched Coding Experts: All routed coding expert layers (
192capability-pruned experts per layer) and router gates remain 100% byte-for-byte identical to the original ISTA-DASLab release. - Complete Standalone Repo: Includes the
72.1 GBpatchedIQ2_XXSbackbone shards (00001to00005), the110.6 GBshared Engram lookup table, vision projector (mmproj) in the root folder.
Quality & Benchmark Comparison (Original vs. Uncensored)
Because no routed coding experts were re-quantized, this model retains 100% of the coding and reasoning performance of the original model while completely eliminating safety refusals:
| Benchmark / Metric | Original (ISTA-DASLab) |
This Model (Uncensored) |
Notes |
|---|---|---|---|
| Top-1 Token Output Agreement | 100.0% |
99.80% |
Near-identical token generation on coding & logic prompts |
| 40-Layer Residual Cosine Similarity | 1.0000 |
0.99999993 |
Zero cumulative drift across all 40 transformer layers |
| Output Distribution Shift (Median KLD) | 0.0 |
6.94e-08 nats |
Statistically indistinguishable probability distribution |
| Coding & Reasoning Retention | 98.7% LCB / 94.8% SWE |
100% Parity |
All GSQ-RCO routed coding experts untouched |
| Safety Refusal Rate | 86.4% |
0.0% |
Responds directly to security, raw-socket & restricted prompts |
Quickstart & Running Instructions
Thanks to memory-mapped streaming (mmap), the 110.6 GB Engram lookup table streams directly from disk (1 row per token), allowing the full model to run comfortably inside 96 GB – 128 GB of system RAM (CPU) or GPU + RAM offload.
Option 1: llama.cpp (b11870+)
First, create a one-time symlink so llama.cpp recognizes the Engram file as Shard 6:
ln -s DeepSeek-V4.1-Flash-Engram-IQ4_NL.gguf \
DeepSeek-V4.1-Flash-1017-Uncensored-GSQ-RCO-Coder-IQ2_XXS-00006-of-00006.gguf
Then launch inference pointing to Shard 1:
./llama-cli \
-m DeepSeek-V4.1-Flash-1017-Uncensored-GSQ-RCO-Coder-IQ2_XXS-00001-of-00006.gguf \
--jinja -c 16384 -n 2048 -t 16 --temp 0.2 \
-p "Write a high-performance raw-socket TCP SYN scanner in Python using struct.pack."
Tip for Best Coding Accuracy: Keep
<think>reasoning enabled (default) and use--temp 0.2for complex systems or security code. If you want instant code output without the thinking monologue, add--reasoning-budget 0.
Option 2: Strata (v0.2.5+)
Strata automatically reads the Engram file from the root directory:
strata run --family deepseek41 \
--model-dir . \
--engram ./DeepSeek-V4.1-Flash-Engram-IQ4_NL.gguf
Credits
- Base Coder Model: ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-Coder-GGUF
- DeepSeek AI — For open-sourcing the base DeepSeek-V4.1 architecture and foundation weights.
- Uncensored Donor: AtomicChat/DeepSeek-V4.1-Flash-1017-Uncensored-GGUF
- Surgical Build & Verification: Abiray