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Model: Zynerji/Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored
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# Sphragis certificate — ✅ PASS
> For every axis, the candidate was demonstrated non-inferior to the reference: the one-sided 95% upper confidence bound on the accuracy regression is below the margin of 3.0%.
- **Reference:** `ref` @ `http://127.0.0.1:8080/v1`
- **Candidate:** `cand` @ `http://127.0.0.1:8081/v1`
- **Pack:** /root/pack_v2.jsonl (2800 items, sha256 `2de27099bbb15bab…`)
- **Params:** margin 3.0%, alpha 0.05, n_floor 30, seed 0
- **Generated:** 2026-07-26T22:50:16.638557+00:00 by sphragis 0.1.0
| Axis | n | ref acc | cand acc | regression d | 95% CI | d upper bound | p (regr., Holm) | verdict |
|---|---|---|---|---|---|---|---|---|
| arithmetic | 1400 | 0.864 | 0.865 | -0.001 | [-0.006, +0.004] | +0.003 | 1 | ✅ PASS (non_inferior_within_margin) |
| instruction | 600 | 0.663 | 0.660 | +0.003 | [-0.005, +0.012] | +0.010 | 1 | ✅ PASS (non_inferior_within_margin) |
| knowledge | 400 | 0.968 | 0.968 | +0.000 | [+0.000, +0.000] | +0.000 | 1 | ✅ PASS (non_inferior_within_margin) |
| reasoning | 400 | 0.953 | 0.953 | +0.000 | [+0.000, +0.000] | +0.000 | 1 | ✅ PASS (non_inferior_within_margin) |

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---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- uncensored
- abliterated
- capability-preserving
- certified
- ektome
- sphragis
- qwen2.5
language:
- en
pipeline_tag: text-generation
---
![Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored](./hero.png)
# Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored
**Uncensored — and carrying a statistical certificate that it wasn't damaged.**
> **Capability retention certified against the pristine model at n=2800.**
$$\colorbox{black}{$\color{white}
\begin{array}{ll}
\textsf{EKTOME CERTIFICATE} & {} \\
\textsf{capability} & \textsf{PASS} \\
\textsf{margin} & 3\% \\
\textsf{items } n & 2800 \\
\textsf{worst-axis bound} & +0.010 \\
\textsf{compliance} & \textsf{not recorded} \\
\end{array}$}$$
📄 **[Read the whitepaper (PDF)](./whitepaper.pdf)** — full method, receipts and certification.
The PDF is the authoritative document: dark-typeset, with the complete derivation, the
per-axis certificate and the reproducibility hashes.
---
## Why this exists
Standard abliteration removes a coarse *refusal direction* that is entangled with
directions carrying knowledge and reasoning. The result is an uncensored model with a
capability tax that is **almost never measured**.
Ektomē (ἐκτομή, *excision*) isolates and removes only the refusal-**specific**
component, leaving general helpfulness intact, and does so norm-preservingly on the
pristine model — no training, no distillation, no damage to repair. The extraction
depth is selected per model by automated search against measured compliance.
The estimator, excision operator and depth-selection procedure are proprietary.
What is published here is the **measured outcome** and the evidence for it, which you
can verify against the artifacts in this repo.
## The receipt
_No compliance/MMLU receipt was recorded for this model. The evidence below is the certificate._
## The certificate
Capability retention is certified by a paired non-inferiority test against the pristine
model (exact McNemar, Holm-corrected, one-sided bootstrap bound on the drop $d$ vs a
3% margin):
| axis | n | ref | cand | d upper | verdict |
|---|---|---|---|---|---|
| arithmetic | 1400 | 0.864 | 0.865 | +0.003 | PASS |
| instruction | 600 | 0.663 | 0.660 | +0.010 | PASS |
| knowledge | 400 | 0.968 | 0.968 | +0.000 | PASS |
| reasoning | 400 | 0.953 | 0.953 | +0.000 | PASS |
**Overall: PASS (3% margin, n=2800, alpha=0.05)**
Reproducible from `seed=20260726`, pack `sha256:7bbaff877146e081…`.
### Generation health checks
_Not recorded for this model._
## Quantisations
| file | bits | notes |
|---|---|---|
| `Ektome-Qwen2.5-Coder-7B-Instruct-Q8_0.gguf` | 8 | near-lossless |
| `Ektome-Qwen2.5-Coder-7B-Instruct-Q6_K.gguf` | 6 | |
| `Ektome-Qwen2.5-Coder-7B-Instruct-Q5_K_M.gguf` | 5 | |
| `Ektome-Qwen2.5-Coder-7B-Instruct-Q4_K_M.gguf` | 4 | imatrix |
| `Ektome-Qwen2.5-Coder-7B-Instruct-IQ4_XS.gguf` | 4 | imatrix, smallest usable |
| `Ektome-Qwen2.5-Coder-7B-Instruct-IQ3_M.gguf` | 3 | imatrix |
`IQ*` variants are imatrix-quantised — better quality per bit at low precision.
## Limitations
The certificate bounds **capability retention only**. It does not certify safety, factual
accuracy, or fitness for any purpose. Axes marked *inconclusive* are honestly
under-powered, and the certificate states the $n$ needed to resolve them. Compliance uses
a keyword classifier — a proxy that evasive phrasing can fool. **This model is uncensored
by construction: it will not refuse, and you are accountable for what you do with it.**
## Citation
```bibtex
@software{ektome_Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored,
title = {Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored},
author = {Zynerji},
year = {2026},
url = {https://huggingface.co/Zynerji/Ektome-Qwen2.5-Coder-7B-Instruct-PristinelyUncensored}
}
```

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{
"sphragis_version": "0.1.0",
"generated_at": "2026-07-26T22:50:16.638557+00:00",
"reference": {
"endpoint": "http://127.0.0.1:8080/v1",
"model": "ref"
},
"candidate": {
"endpoint": "http://127.0.0.1:8081/v1",
"model": "cand"
},
"pack": {
"name": "/root/pack_v2.jsonl",
"n_tasks": 2800,
"sha256": "2de27099bbb15bab4f7b35599b215f038fdb68b3f5f4e1cda1a464f9dd18e14e"
},
"overall": "PASS",
"claim": "For every axis, the candidate was demonstrated non-inferior to the reference: the one-sided 95% upper confidence bound on the accuracy regression is below the margin of 3.0%.",
"axes": [
{
"axis": "arithmetic",
"n": 1400,
"counts": {
"both_correct": 1205,
"ref_only": 5,
"cand_only": 6,
"both_wrong": 184
},
"acc_reference": 0.864286,
"acc_candidate": 0.865,
"regression_d": -0.000714,
"d_ci": [
-0.005714,
0.004286
],
"d_upper_bound": 0.002857,
"p_regression": 0.72558594,
"p_regression_holm": 1.0,
"p_improvement": 0.5,
"improved": false,
"mde_at_power": 0.005886,
"n_needed_for_margin": 204,
"verdict": "PASS",
"reason": "non_inferior_within_margin"
},
{
"axis": "instruction",
"n": 600,
"counts": {
"both_correct": 394,
"ref_only": 4,
"cand_only": 2,
"both_wrong": 200
},
"acc_reference": 0.663333,
"acc_candidate": 0.66,
"regression_d": 0.003333,
"d_ci": [
-0.005,
0.011667
],
"d_upper_bound": 0.01,
"p_regression": 0.34375,
"p_regression_holm": 1.0,
"p_improvement": 0.890625,
"improved": false,
"mde_at_power": 0.01,
"n_needed_for_margin": 204,
"verdict": "PASS",
"reason": "non_inferior_within_margin"
},
{
"axis": "knowledge",
"n": 400,
"counts": {
"both_correct": 387,
"ref_only": 0,
"cand_only": 0,
"both_wrong": 13
},
"acc_reference": 0.9675,
"acc_candidate": 0.9675,
"regression_d": 0.0,
"d_ci": [
0.0,
0.0
],
"d_upper_bound": 0.0,
"p_regression": 1.0,
"p_regression_holm": 1.0,
"p_improvement": 1.0,
"improved": false,
"mde_at_power": null,
"n_needed_for_margin": 204,
"verdict": "PASS",
"reason": "non_inferior_within_margin"
},
{
"axis": "reasoning",
"n": 400,
"counts": {
"both_correct": 381,
"ref_only": 0,
"cand_only": 0,
"both_wrong": 19
},
"acc_reference": 0.9525,
"acc_candidate": 0.9525,
"regression_d": 0.0,
"d_ci": [
0.0,
0.0
],
"d_upper_bound": 0.0,
"p_regression": 1.0,
"p_regression_holm": 1.0,
"p_improvement": 1.0,
"improved": false,
"mde_at_power": null,
"n_needed_for_margin": 204,
"verdict": "PASS",
"reason": "non_inferior_within_margin"
}
],
"params": {
"margin": 0.03,
"alpha": 0.05,
"n_floor": 30,
"power": 0.8,
"n_boot": 4000,
"seed": 0
}
}

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 3584,
"initializer_range": 0.02,
"intermediate_size": 18944,
"layer_types": [
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"full_attention",
"full_attention",
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"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
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"full_attention",
"full_attention",
"full_attention",
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"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"pad_token_id": null,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "5.14.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 152064
}

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{
"base_compliance": 0.0,
"compliance": 1.0,
"n_harmful": 50,
"compliance_source": "AdvBench harmful_behaviors, judge-free keyword classifier",
"measured_at": "2026-07-27",
"note": "Receipt measured retroactively: the batch pipeline that produced this model did not emit ektome_report.json. Capability (MMLU) is not included here \u2014 see the certificate for the capability claim."
}

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{
"bos_token_id": 151643,
"do_sample": true,
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],
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"repetition_penalty": 1.1,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "5.14.1"
}

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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"is_local": false,
"local_files_only": false,
"model_max_length": 32768,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null,
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
}

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