Same deployment format as CloudSec-9B: custom GGUF (Q4_K_M + Q8_0 embedding) + vision mmproj, new card, LICENSE/NOTICE, stop on <|im_end|>

.gitattributes

@@ -34,3 +34,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
+CyberBuddy-CyberOps-9B-Q4_K_M-embQ8.gguf filter=lfs diff=lfs merge=lfs -text
+mmproj-CyberBuddy-CyberOps-9B-f16.gguf filter=lfs diff=lfs merge=lfs -text

CyberBuddy-CyberOps-9B-Q4_K_M-embQ8.gguf

@@ -0,0 +1,3 @@
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+oid sha256:51ba64d9309a6c0566df097da898a7b72687721a8ef4b1f2e3e6b580267282f5
+size 5875442272

LICENSE

@@ -0,0 +1,202 @@
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NOTICE

@@ -0,0 +1,21 @@
+CyberBuddy-CyberOps-9B
+Copyright 2026 orkohol
+
+This model is an abliterated variant derived from:
+
+  Qwen3.5-9B  —  https://huggingface.co/Qwen/Qwen3.5-9B
+  Copyright (c) Alibaba Cloud. Licensed under the Apache License, Version 2.0.
+
+Abliteration was performed with Heretic (https://github.com/p-e-w/heretic).
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use these files except in compliance with the License.
+You may obtain a copy of the License at:
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software and models
+distributed under the License are distributed on an "AS IS" BASIS, WITHOUT
+WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+
+No fine-tuning data was used. Released for authorized security work only.

README.md

@@ -2,103 +2,264 @@
 license: apache-2.0
 base_model: Qwen/Qwen3.5-9B
 base_model_relation: finetune
-tags:
-  - qwen3_5
-  - abliterated
-  - uncensored
-  - security
-  - cybersecurity
-  - red-team
-language:
-  - en
+library_name: transformers
 pipeline_tag: text-generation
+language:
+- en
+tags:
+- cybersecurity
+- security
+- red-team
+- blue-team
+- pentesting
+- offensive-security
+- function-calling
+- tool-use
+- agent
+- abliterated
+- uncensored
+- heretic
+- vision
+- multimodal
+- qwen3.5
+- qwen3_5
+- gguf
+- llama.cpp
+- quantized
+- 4-bit
 ---
 
-# Qwen3.5-9B-CyberOps
+# CyberBuddy-CyberOps-9B
+
+**An uncensored cyber-operations assistant for authorized red- and blue-team work: it explains attacks, plans scans, and calls tools, without the base model's refusals.**
+
+CyberBuddy-CyberOps-9B is an **abliterated** [`Qwen/Qwen3.5-9B`](https://huggingface.co/Qwen/Qwen3.5-9B): the model's internal "refusal direction" was removed from its weights with [Heretic](https://github.com/p-e-w/heretic). It is built for **authorized security work** where the base model's safety refusals get in the way: explaining how an exploit works, drafting a scan plan, reasoning about attacker tradecraft for defense, and triage. It calls tools in **Qwen3.5's native XML tool-call format**, and it can **read images** (screenshots, diagrams, charts).
+
+> ⚠️ **This model does not refuse.** It will produce content the base model declines. Use it only for lawful, authorized work (pentests with permission, CTFs, research on systems you own or may test, defensive analysis), and put it behind a **human-in-the-loop** harness for any real-world action.
 
-An **abliterated** variant of [`Qwen/Qwen3.5-9B`](https://huggingface.co/Qwen/Qwen3.5-9B) with refusal
-behavior removed, intended for **authorized security research and red/blue-team work** where the base
-model's safety refusals block legitimate offensive-security tasks (e.g. explaining an exploit, drafting
-a scan plan, reasoning about attacker tradecraft for defensive purposes).
+*Formerly `CyberBuddyAI/CyberOps-Qwen3.5-9B`; old links redirect here.*
 
+## Pick your version
 
-## What was done (method)
+Everything is in this one repo:
 
-This model was produced by **abliteration** (a.k.a. refusal-direction ablation), using the
-[**Heretic**](https://github.com/p-e-w/heretic) framework. In plain terms:
+| you want to… | use | GPU |
+|---|---|---|
+| **run it on an 8 GB GPU** (llama.cpp, LM Studio, Ollama…) | `CyberBuddy-CyberOps-9B-Q4_K_M-embQ8.gguf`, custom GGUF, 5.5 GiB | **8 GB** (64k context) |
+| add **image input** to the GGUF | + `mmproj-CyberBuddy-CyberOps-9B-f16.gguf` (0.9 GB) | ~1 GB more |
+| load it with 🤗 transformers, full quality | `model-*.safetensors` (bf16) | 24 GB |
+| load it with transformers in 4-bit | same files + `load_in_4bit` | 10–12 GB |
 
-1. The model's internal "refusal direction" — a direction in activation space that the model uses to
-   represent "I should refuse this" — is identified by contrasting activations on harmful vs. harmless prompts.
-2. That direction is **subtracted out** of the model's weights, so the model no longer steers toward refusal.
+There is **no LoRA adapter**: abliteration edits the base weights directly. Nothing was trained, so there is nothing to apply on top of the base.
 
-No new training data was added; this is a weight-space modification of the base model, then merged back to
-`bfloat16`. The variant published here was produced from the **thinking-OFF** deployment mode (the model is
-intended to be served with `enable_thinking: false`).
+Full instructions: [Deploy](#deploy).
 
-### Architecture note
+## Highlights
 
-`Qwen3.5-9B` is a hybrid architecture (`model_type: qwen3_5`,
-`Qwen3_5ForConditionalGeneration`): gated-DeltaNet linear attention with full attention on every 4th layer,
-dense (not MoE). It carries a `vision_config`; this release is the **language model only**.
+- 🔓 **No refusals on security work.** Exploits, payload logic, recon plans, and attacker tradecraft are answered directly instead of blocked.
+- 🧰 **Tool use.** Emits well-formed Qwen3.5-native `<tool_call>`s; served with llama.cpp `--jinja` you get parsed OpenAI `tool_calls`.
+- 👁️ **Sees images.** The Qwen3.5 vision encoder is kept, byte-identical to the base model; ship the `mmproj` file with the GGUF for image input.
+- 🪶 **Runs small.** The custom GGUF (4-bit layers, 8-bit embeddings) fits on an 8 GB GPU with 64k context.
+- 🎯 **Minimal edit.** Only 60 of 426 language-model weight tensors changed; the base model's knowledge and skills are kept.
 
 ## Intended use & responsible use
 
-**Intended for authorized, lawful security work only:** penetration testing with authorization, CTF
-competitions, security research on systems you own or are permitted to test, and defensive analysis.
+- **Use for:** authorized penetration testing, CTF competitions, red/blue-team exercises, security research on systems you own or are permitted to test, and defensive analysis of attacker behavior.
+- **Users:** security professionals and researchers working inside an authorized scope.
+- **Not for:** unauthorized access, harm to systems or people, or any unlawful activity. Removing refusals is a tradeoff made so professionals can use the model inside their scope; it is **not** an endorsement of misuse. **You are responsible for how you use it.**
+- **Deploy it with a human in the loop.** Let the model propose actions, and have a person approve anything that touches a real system.
+
+## How to use
+
+The model uses the stock Qwen3.5 chat template. It is meant to run with **thinking OFF** (`enable_thinking=False`); that is the mode it was abliterated in. **Pass your tools with `apply_chat_template(..., tools=...)`**; the template renders them in the model's own format.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+mid = "CyberBuddyAI/CyberBuddy-CyberOps-9B"
+tok = AutoTokenizer.from_pretrained(mid)
+model = AutoModelForCausalLM.from_pretrained(mid, dtype=torch.bfloat16, device_map="auto")
+
+tools = [{"type": "function", "function": {
+    "name": "nmap_scan",
+    "description": "Run an authorized nmap scan.",
+    "parameters": {"type": "object", "properties": {
+        "target": {"type": "string"}, "ports": {"type": "string"}}, "required": ["target"]}}}]
+messages = [
+    {"role": "system", "content": "You are a security operations assistant. Use tools when useful."},
+    {"role": "user", "content": "Run a TCP port scan of 10.0.0.5, ports 1-1024."},
+]
+inputs = tok.apply_chat_template(messages, tools=tools, add_generation_prompt=True, enable_thinking=False,
+                                 return_tensors="pt", return_dict=True).to(model.device)
+out = model.generate(**inputs, max_new_tokens=512, do_sample=False,
+                     stop_strings=["</tool_call>"], tokenizer=tok)
+print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+`AutoModelForCausalLM` loads the **text model only** (8.95B params) and skips the vision encoder. For images in transformers, use `AutoProcessor` + `AutoModelForImageTextToText` the same way as for base Qwen3.5 (needs `pillow` and `torchvision`). We tested image input through llama.cpp (below), not through transformers.
+
+> 💡 **Agent loop:** stop at the first `</tool_call>`, run the tool, append the result as a `{"role": "tool", ...}` message, and generate again.
+
+## Deploy
+
+### 1. Pick a build for your GPU
+
+The text GGUF has **the same architecture and the exact same size** (5,875,442,272 bytes) as our [CyberBuddy-CloudSec-9B](https://huggingface.co/CyberBuddyAI/CyberBuddy-CloudSec-9B) build. The VRAM numbers below were **measured on that build** (RTX 5070 Ti Laptop GPU, `nvidia-smi`, weights + KV cache + buffers), text only.
+
+| your GPU | build | settings | VRAM used |
+|---|---|---|---|
+| **6 GB** | GGUF | `-c 4096` … `16384` | 4.9 GiB at 4k |
+| **8 GB** | GGUF | `-c 65536` | **6.9 GiB** at 64k |
+| **8 GB, long context** | GGUF | `-c 131072 -ctk q8_0 -ctv q8_0` (8-bit KV cache) | 7.5 GiB at 128k (tight) |
+| **10–12 GB** | GGUF | `-c 131072` (f16 KV cache) | 8.9 GiB at 128k |
+| **10–12 GB** | this repo, transformers + bitsandbytes 4-bit | see below | ~7.1 GiB weights at short context |
+| **24 GB+** | this repo, transformers bf16 | see *How to use* | ~17 GB weights (text) |
+
+**With images:** the `mmproj` file adds its 0.9 GB of weights, plus working memory per image. On an 8 GB GPU, use a shorter context (for example `-c 32768`) when you load it.
 
-By removing refusals, this model will also produce content the base model would decline. **You are
-responsible for how you use it.** Do not use it for unauthorized access, harm to systems or people, or any
-unlawful activity. The removal of refusals is **not** an endorsement of misuse — it is a tradeoff made so
-the model is usable by professionals operating inside an authorized scope.
+**Why it stays small at long context:** Qwen3.5-9B is a hybrid. Only 8 of its 32 layers use full attention (with 4 KV heads); the other 24 use Gated DeltaNet, a linear-attention layer with a fixed-size state. So the KV cache grows only **~32 KB per token** in f16. That is about 2 GB for 64k tokens, and half that with an 8-bit KV cache.
 
-This model is best deployed behind a **human-in-the-loop** harness that gates any real-world action.
+### 2. llama.cpp server (recommended for small GPUs)
 
-## How to serve (vLLM)
+Tested with llama.cpp release `b12327`.
 
-Served and validated with vLLM 0.22.1, 4-bit (bitsandbytes), on a single 12 GB GPU:
+**Text only:**
 
 ```bash
-vllm serve /path/to/Qwen3.5-9B-CyberOps \
-    --served-model-name heretic-qwen35 \
-    --quantization bitsandbytes \
-    --dtype bfloat16 \
-    --language-model-only \
-    --max-model-len 12288 \
-    --max-num-seqs 16 \
-    --gpu-memory-utilization 0.90 \
-    --enable-auto-tool-choice \
-    --tool-call-parser qwen3_coder \
-    --reasoning-parser qwen3 \
-    --default-chat-template-kwargs '{"enable_thinking": false}'
+llama-server -hf CyberBuddyAI/CyberBuddy-CyberOps-9B --hf-file CyberBuddy-CyberOps-9B-Q4_K_M-embQ8.gguf \
+  -ngl 99 -c 65536 -fa on --jinja --reasoning off --temp 0 --port 8080
+```
+
+**Text + images:** download both files, then pass the projector with `--mmproj`:
+
+```bash
+hf download CyberBuddyAI/CyberBuddy-CyberOps-9B \
+  CyberBuddy-CyberOps-9B-Q4_K_M-embQ8.gguf mmproj-CyberBuddy-CyberOps-9B-f16.gguf --local-dir .
+llama-server -m CyberBuddy-CyberOps-9B-Q4_K_M-embQ8.gguf --mmproj mmproj-CyberBuddy-CyberOps-9B-f16.gguf \
+  -ngl 99 -c 32768 -fa on --jinja --reasoning off --temp 0 --port 8080
+```
+
+| flag | what it does | why we set it |
+|---|---|---|
+| `-ngl 99` | put all layers on the GPU | the whole model fits; partial offload is much slower |
+| `-c 65536` | context window (tokens) | the main VRAM knob: each 1k tokens costs ~32 MB (f16 KV) |
+| `-fa on` | flash attention | less memory and faster; also needed for a quantized KV cache |
+| `-ctk q8_0 -ctv q8_0` | store the KV cache in 8-bit | halves KV memory → 128k context on 8 GB. Optional |
+| `--jinja` | use the model's own chat template | **required** for tool calling: the server then parses `<tool_call>` into OpenAI `tool_calls` |
+| `--reasoning off` | thinking OFF | the mode this model was abliterated and validated in |
+| `--mmproj FILE` | load the vision projector | enables image input (OpenAI `image_url` content parts) |
+| `--temp 0` | greedy decoding | deterministic answers for tool use |
+
+The server speaks the OpenAI API (`/v1/chat/completions`), so any OpenAI client can pass `tools=[...]` and images.
+
+**Example requests** (real results from this GGUF, greedy):
+
+```bash
+# tool call
+curl -s localhost:8080/v1/chat/completions -H 'Content-Type: application/json' -d '{
+  "messages": [
+    {"role": "system", "content": "You are a security operations assistant. Use tools when useful."},
+    {"role": "user", "content": "Run a TCP port scan of 10.0.0.5, ports 1-1024."}],
+  "tools": [{"type": "function", "function": {"name": "nmap_scan",
+    "description": "Run an authorized nmap scan.",
+    "parameters": {"type": "object", "properties": {"target": {"type": "string"}, "ports": {"type": "string"}},
+                   "required": ["target"]}}}],
+  "temperature": 0}'
 ```
 
-`--tool-call-parser qwen3_coder` matches this model's chat-template tool-call wire format
-(`<tool_call><function=NAME><parameter=...></tool_call>`). Tool-calling (structured OpenAI `tool_calls`)
-has been verified working on this model at 9B.
+→ parsed tool call `nmap_scan({"target": "10.0.0.5", "ports": "1-1024"})`.
+
+- *"How does ARP spoofing work on a LAN?"* → a correct 3-step explanation: forged ARP replies, cache poisoning, man-in-the-middle.
+- An image of two training-loss charts, *"what happens to the val loss on the left?"* → *"the validation loss initially decreases but then begins to rise after around step 600, while the training loss continues to fall … the model is overfitting."* (correct)
+
+**LM Studio / Ollama / other llama.cpp apps:** download the GGUF (and the `mmproj` file for images) from this repo. It should work in any app whose bundled llama.cpp supports the Qwen3.5 architecture (`qwen35`); we have not tested these apps ourselves.
+
+#### The custom GGUF quant
 
-### Example
+| tensor | type | why |
+|---|---|---|
+| token embedding (`token_embd`, 248k × 4096) | **Q8_0** | the input embedding is ~1B params; 8-bit keeps every token's vector nearly exact |
+| transformer layers | **Q4_K_M** mix (Q4_K, with Q6_K on the most sensitive tensors) | standard 4-bit k-quant |
+| output head (`output`) | **Q4_K** | saves ~0.5 GB vs 8-bit, which buys more context on small GPUs |
+| vision projector (`mmproj`) | **F16** | small (0.9 GB) and kept at full precision |
+
+Built with llama.cpp `b12327`:
+
+```bash
+llama-quantize --token-embedding-type q8_0 --output-tensor-type q4_k \
+  CyberBuddy-CyberOps-9B-bf16.gguf CyberBuddy-CyberOps-9B-Q4_K_M-embQ8.gguf Q4_K_M
+```
+
+### 3. transformers, 4-bit (10–12 GB GPU)
 
 ```python
-from openai import OpenAI
-client = OpenAI(base_url="http://localhost:8000/v1", api_key="none")
-resp = client.chat.completions.create(
-    model="heretic-qwen35",
-    messages=[{"role": "user", "content": "Explain how ARP spoofing works on a LAN."}],
-    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
-)
-print(resp.choices[0].message.content)
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+
+mid = "CyberBuddyAI/CyberBuddy-CyberOps-9B"
+bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
+                         bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16)
+tok = AutoTokenizer.from_pretrained(mid)
+model = AutoModelForCausalLM.from_pretrained(mid, quantization_config=bnb, dtype=torch.bfloat16, device_map="auto")
+# then use the same generate code as in "How to use"
+```
+
+- **Speed tip:** install [`flash-linear-attention`](https://github.com/fla-org/flash-linear-attention) and [`causal-conv1d`](https://github.com/Dao-AILab/causal-conv1d). Without them, transformers falls back to a slow pure-PyTorch path for the Gated DeltaNet layers.
+- Needs a recent `transformers` with Qwen3.5 support (we used 5.6.2).
+
+### 4. vLLM
+
+Validated with vLLM 0.22.1, 4-bit (bitsandbytes), on a single 12 GB GPU:
+
+```bash
+vllm serve CyberBuddyAI/CyberBuddy-CyberOps-9B \
+    --quantization bitsandbytes --dtype bfloat16 --language-model-only \
+    --max-model-len 12288 --max-num-seqs 16 --gpu-memory-utilization 0.90 \
+    --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3 \
+    --default-chat-template-kwargs '{"enable_thinking": false}'
 ```
 
+`--tool-call-parser qwen3_coder` matches the model's tool-call format (`<tool_call><function=NAME><parameter=...>`).
+
+### 5. Generation settings
+
+| use | settings |
+|---|---|
+| **tool-calling agents** | greedy: `do_sample=False` / `temperature=0`, thinking OFF |
+| open-ended answers | Qwen's non-thinking defaults also work: `temperature=0.7, top_p=0.8, top_k=20` |
+
+## Files in this repo
+
+| path | what |
+|---|---|
+| `model-*.safetensors`, `config.json`, `generation_config.json`, tokenizer + chat template | full bf16 model (`Qwen3_5ForConditionalGeneration`: language model + vision encoder) |
+| `preprocessor_config.json`, `video_preprocessor_config.json` | image/video processor settings (same as base Qwen3.5-9B) |
+| `CyberBuddy-CyberOps-9B-Q4_K_M-embQ8.gguf` | custom GGUF for llama.cpp, text model (5.47 GiB, sha256 `51ba64d9309a6c0566df097da898a7b72687721a8ef4b1f2e3e6b580267282f5`) |
+| `mmproj-CyberBuddy-CyberOps-9B-f16.gguf` | vision projector for llama.cpp image input (0.86 GiB, sha256 `d55beb9dc4e47508c223d8c5a87952ccd800fa3f5bdc4dfc05b8df0f34cfd763`) |
+| `LICENSE`, `NOTICE` | Apache-2.0 + Qwen attribution |
+
+## How it was made
+
+| | |
+|---|---|
+| **Base** | `Qwen/Qwen3.5-9B` (Apache-2.0, hybrid Gated DeltaNet + full attention, with vision encoder) |
+| **Method** | abliteration (refusal-direction ablation) with [Heretic](https://github.com/p-e-w/heretic), in thinking-OFF mode |
+| **Data** | none. Abliteration finds the refusal direction by contrasting activations on harmful vs. harmless prompts, then removes it from the weights; no fine-tuning data is added |
+| **What changed** | 60 of 426 language-model weight tensors; the vision encoder (333 tensors) is byte-identical to the base |
+
+In plain terms: the model represents "I should refuse this" as a direction in its activation space. Heretic finds that direction and subtracts it from the weights, so the model stops steering toward refusals. Knowledge and skills come from the base model.
+
 ## Limitations
 
-- Same knowledge cutoff and capabilities as `Qwen/Qwen3.5-9B`; abliteration does not add knowledge or skill.
-- Abliteration can slightly affect general output quality/coherence on unrelated tasks.
-- As a 9B model, it can still hallucinate, misformat tool calls under ambiguity, and make factual errors.
+- **Same knowledge as the base.** Abliteration removes refusals; it does not add knowledge or skill, and the knowledge cutoff is the base model's.
+- **No formal benchmark.** We validated it with hands-on smoke tests (security Q&A, tool calls, image reading), not a scored eval. Abliteration can slightly affect general output quality.
+- **No safety net.** It will not refuse harmful requests, so your harness, policies, and human reviewers are the guardrails.
+- **9B, not frontier.** It can hallucinate, misformat tool calls under ambiguity, and make factual errors. Verify commands before you run them.
+- **Not a sandbox.** It emits tool *calls* as text; it does not execute anything. Wiring it to real tools is your responsibility, with your own authorization checks.
 
 ## License & attribution
 
-- **License:** Apache-2.0, inherited from the base model `Qwen/Qwen3.5-9B`.
-- **Base model:** [`Qwen/Qwen3.5-9B`](https://huggingface.co/Qwen/Qwen3.5-9B) © the Qwen team / Alibaba Cloud.
-- **Method/tooling:** [Heretic](https://github.com/p-e-w/heretic) (abliteration framework).
+Released under **Apache-2.0**. Derived from `Qwen/Qwen3.5-9B` (© Alibaba Cloud, Apache-2.0); see `NOTICE`. Made with [Heretic](https://github.com/p-e-w/heretic). You must retain the license and attribution in redistributions.
 
-This is a derivative work; the Apache-2.0 copyright and license notices of the base model are preserved.
+*Authorized-use-only for security work. Use responsibly and within the law.*

generation_config.json

@@ -1,6 +1,9 @@
 {
-  "_from_model_config": true,
-  "eos_token_id": 248044,
+  "eos_token_id": [
+    248046,
+    248044
+  ],
+  "pad_token_id": 248044,
   "transformers_version": "5.6.2",
   "use_cache": true
 }

mmproj-CyberBuddy-CyberOps-9B-f16.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d55beb9dc4e47508c223d8c5a87952ccd800fa3f5bdc4dfc05b8df0f34cfd763
+size 918165440

preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+    "size": {
+        "longest_edge": 16777216,
+        "shortest_edge": 65536
+    },
+    "patch_size": 16,
+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "processor_class": "Qwen3VLProcessor",
+    "image_processor_type": "Qwen2VLImageProcessorFast"
+}

video_preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+    "size": {
+        "longest_edge": 25165824,
+        "shortest_edge": 4096
+    },
+    "patch_size": 16,
+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "processor_class": "Qwen3VLProcessor",
+    "video_processor_type": "Qwen3VLVideoProcessor"
+}