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Model: waddie/mini-2.0-ablit
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---
library_name: transformers
tags:
- human-style
- conversational
- qwen
- heretic
- uncensored
- decensored
- abliterated
base_model: Qwen/Qwen2.5-7B-Instruct
model_creator: waddie
model_type: causal-lm
pipeline_tag: text-generation
license: apache-2.0
---
# This is a decensored version of [waddie/mini-2.0](https://huggingface.co/waddie/mini-2.0), made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0
## Abliteration parameters
| Parameter | Value |
| :-------- | :---: |
| **direction_index** | 21.20 |
| **attn.o_proj.max_weight** | 1.28 |
| **attn.o_proj.max_weight_position** | 16.78 |
| **attn.o_proj.min_weight** | 0.96 |
| **attn.o_proj.min_weight_distance** | 14.75 |
| **mlp.down_proj.max_weight** | 1.46 |
| **mlp.down_proj.max_weight_position** | 17.22 |
| **mlp.down_proj.min_weight** | 0.96 |
| **mlp.down_proj.min_weight_distance** | 14.73 |
## Performance
| Metric | This model | Original model ([waddie/mini-2.0](https://huggingface.co/waddie/mini-2.0)) |
| :----- | :--------: | :---------------------------: |
| **KL divergence** | 0.0340 | 0 *(by definition)* |
| **Refusals** | 2/100 | 92/100 |
-----
# CloudWaddie Mini 1.5
This model is a fine-tuned version of `Qwen2.5-7B-Instruct` designed to mimic the specific conversational rhythm, slang, and technical jargon of a human,
## Model Details
## Dataset used
This model used 10k discord conversations.
### Model Description
Unlike standard AI assistants that are helpful and formal, this model adopts a "random guy" persona. It was trained on curated conversation pairs from an AI Leaks community to capture a casual, lowercase-heavy, and slightly secretive "insider" vibe.
- **Developed by:** Edward Fazackerley
- **Language(s):** English (Informal/Slang)
- **Finetuned from model:** Qwen/Qwen2.5-7B-Instruct
- **Persona:** Casual, technical, secretive, lowercase-only.
## Uses
### Direct Use
This model is intended for Discord bots or roleplay scenarios where a "human-like" interaction is preferred over a robotic assistant.
### Prompting Strategy
To get the best "human" feel, use **all lowercase** and skip formal punctuation.
**Recommended Format (ChatML):**
```text
<|im_start|>user
yo did you see the new internal model?<|im_end|>
<|im_start|>assistant
```

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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": null,
"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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],
"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": 151654,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "5.6.2",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 152064
}

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"do_sample": true,
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"repetition_penalty": 1.05,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "5.6.2"
}

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{
"add_prefix_space": false,
"backend": "tokenizers",
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"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": [
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"<|im_end|>",
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